Authors: "Authors
Title: Title
Year: Year
Source title: Source title
Link: Link
Abstract: Abstract
Author Keywords: Author Keywords
Index Keywords: Index Keywords
Authors: "Huaman E.; Fensel D.
Title: Knowledge Graph Curation: A Practical Framework
Year: 2021
Source title: ACM International Conference Proceeding Series
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124011772&doi=10.1145%2f3502223.3502247&partnerID=40&md5=42a352d3688237e750b5b46c8eac5dd6
Abstract: Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are not curated, e.g., low-quality KGs produce low-quality applications that are built on top of them. In this vision paper, we propose a practical knowledge graph curation framework for improving the quality of KGs. First, we define a set of quality metrics for assessing the status of KGs, Second, we describe the verification and validation of KGs as cleaning tasks, Third, we present duplicate detection and knowledge fusion strategies for enriching KGs. Furthermore, we give insights and directions toward a better architecture for curating KGs. © 2021 ACM.
Author Keywords: Knowledge graph assessment; Knowledge graph cleaning; Knowledge graph curation; Knowledge graph enrichment
Index Keywords: Search engines; Curation; Knowledge graph assessment; Knowledge graph cleaning; Knowledge graph curation; Knowledge graph enrichment; Knowledge graphs; Low qualities; Knowledge graph
Authors: "Tang X.; Chi G.; Cui L.; Ip A.W.H.; Yung K.L.; Xie X.
Title: Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis
Year: 2023
Source title: Sensors
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161535202&doi=10.3390%2fs23115295&partnerID=40&md5=4acc4413e4a677f2f08112881a7657b9
Abstract: Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly. © 2023 by the authors.
Author Keywords: aircraft fault diagnosis; deep learning; fault knowledge extraction; knowledge graph; question-answering system
Index Keywords: Aircraft; Engineering; Heuristics; Knowledge; Pattern Recognition, Automated; Aircraft; Data mining; Deep learning; Engineers; Fault detection; Heuristic methods; Knowledge graph; Learning systems; Aircraft fault diagnose; Deep learning; Diagnosis methods; Fault knowledge extraction; Faults diagnosis; High complexity; Knowledge extraction; Knowledge graphs; Maintenance engineers; Question answering systems; aircraft; automated pattern recognition; engineering; heuristics; knowledge; Failure analysis
Authors: "Lee C.-H.; Kang D.-O.; Song H.J.
Title: Fast knowledge graph completion using graphics processing units
Year: 2024
Source title: Journal of Parallel and Distributed Computing
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189523652&doi=10.1016%2fj.jpdc.2024.104885&partnerID=40&md5=036806ac0b15cb16e0f2854502fd6f70
Abstract: Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate N×N×R vector operations, where N is the number of entities and R is the number of relation types. It is very costly. In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem. © 2024 Elsevier Inc.
Author Keywords: GPU processing; Knowledge graph completion; Knowledge graph embedding; Similarity join; TransE
Index Keywords: Computer graphics; Computer graphics equipment; Graph embeddings; Graphics processing unit; Knowledge graph; Program processors; Semantics; Set theory; Completion problem; GPU processing; Graph embeddings; Knowledge graph completion; Knowledge graph embedding; Knowledge graphs; Metric spaces; Similarity join; Transe; Topology
Authors: "Gorshkov S.; Kondratiev C.; Shebalov. R.
Title: Ontology-Based Question Answering over Corporate Structured Data
Year: 2021
Source title: 2021 International Symposium on Knowledge, Ontology, and Theory, KNOTH 2021
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126764834&doi=10.1109%2fKNOTH54462.2021.9685024&partnerID=40&md5=7e1d286b1b5e4e9300ebcb1182de88ce
Abstract: Ontology-based approach to the Natural Language Understanding (NLU) processing allows to improve questions answering quality in dialogue systems. We describe our NLU engine architecture and evaluate its implementation. The engine transforms user's input into the SPARQL SELECT, ASK or INSERT query to the knowledge graph provided by the ontology-based data virtualization platform. The transformation is based on the lexical level of the knowledge graph built according to the Ontolex ontology. The described approach can be applied for graph data population tasks and to the question answering systems implementation, including chat bots. We describe the dialogue engine for a chat bot which can keep the conversation context and ask clarifying questions, simulating some aspects of the human logical thinking. Our approach uses graph-based algorithms to avoid gathering datasets, required in the neural nets-based approaches, and provide better explainability of our models. Using question answering engine in conjunction with data virtualization layer over the corporate data sources allows extracting facts from the structured data to be used in conversation. © 2021 IEEE.
Author Keywords:
Index Keywords: Graphic methods; Knowledge graph; Natural language processing systems; Ontology; Population statistics; Speech processing; Virtual reality; Chat bots; Corporates; Data virtualization; Dialogue systems; Knowledge graphs; Natural language understanding; Ontology-based; Question Answering; Structured data; User input; Engines
Authors: "Sheth A.; Padhee S.; Gyrard A.
Title: Knowledge Graphs and Knowledge Networks: The Story in Brief
Year: 2019
Source title: IEEE Internet Computing
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073874197&doi=10.1109%2fMIC.2019.2928449&partnerID=40&md5=0c19e91c66174d26583118794a80c204
Abstract: Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI. © 1997-2012 IEEE.
Author Keywords:
Index Keywords: Biology; Knowledge representation; Search engines; AI applications; Computational biology; Knowledge graphs; Knowledge networks; Link prediction; Question answering systems; Relationships between entities; Research problems; Real time systems
Authors: "Pan Z.; Jiang S.; Su J.; Guo M.; Zhang Y.
Title: Knowledge graph based platform of COVID-19 drugs and symptoms
Year: 2021
Source title: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124425488&doi=10.1145%2f3487351.3489484&partnerID=40&md5=cef306eef1bbedc457b0dca0d1591f88
Abstract: Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms. © 2021 ACM.
Author Keywords:
Index Keywords: Drug interactions; Graphic methods; Clinical experiments; Graph-based; Keep track of; Knowledge graphs; Literature search; Named entity recognition; Question Answering; Rapid growth; Scientific literature; Search system; Knowledge graph
Authors: "Avila C.V.S.; Franco W.; Maia J.G.R.; Vidal V.M.P.
Title: CONQUEST: A framework for building template-based iqa chatbots for enterprise knowledge graphs
Year: 2020
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087530549&doi=10.1007%2f978-3-030-51310-8_6&partnerID=40&md5=90b1cf371558c57ff7751490c108e38c
Abstract: The popularization of Enterprise Knowledge Graphs (EKGs) brings an opportunity to use Question Answering Systems to consult these sources using natural language. We present CONQUEST, a framework that automates much of the process of building chatbots for the Template-Based Interactive Question Answering task on EKGs. The framework automatically handles the processes of construction of the Natural Language Processing engine, construction of the question classification mechanism, definition of the system interaction flow, construction of the EKG query mechanism, and finally, the construction of the user interaction interface. CONQUEST uses a machine learning-based mechanism to classify input questions to known templates extracted from EKGs, utilizing the clarification dialog to resolve inconclusive classifications and request mandatory missing parameters. CONQUEST also evolves with question clarification: these cases define question patterns used as new examples for training. © Springer Nature Switzerland AG 2020.
Author Keywords: ChatBot; Interactive Question Answering; Knowledge Graph; Linked Data
Index Keywords: Artificial intelligence; Clarifiers; Information systems; Information use; Learning algorithms; Query processing; User interfaces; NAtural language processing; Natural languages; Query mechanisms; Question answering systems; Question Answering Task; Question classification; System interactions; User interaction; Natural language processing systems
Authors: "Bi Z.; Wang S.; Chen Y.; Li Y.; Kim J.Y.
Title: A knowledge-enhanced dialogue model based on multi-hop information with graph attention
Year: 2021
Source title: CMES - Computer Modeling in Engineering and Sciences
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111284313&doi=10.32604%2fcmes.2021.016729&partnerID=40&md5=b7cc5ccd7c0f625ca000820a61c1d639
Abstract: With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper introduces knowledge information into the research of question answering system by using a knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledge query, can acquire knowledge faster, and improves the background information needed for answering questions. To avoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge Information Enhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directly related to the input information from the entire knowledge base and then uses the graph neural network as the knowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is used to determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregation of highly relevant neighbor information. This further enriches the semantic information to provide a better understanding of the meaning of the input question and generate appropriate response information. In the process of generating a response, a multi-attention flow mechanism is used to focus on different information to promote the generation of better responses. Experiments have proved that the model presented in this article can generate more meaningful responses than other models. © 2021 Tech Science Press. All rights reserved.
Author Keywords: Conversation generation; E-commerce ecosystem; Graph attention; Graph neural network; Knowledge graph
Index Keywords: Ecosystems; Knowledge based systems; Knowledge representation; Mobile robots; Sales; Semantics; Attention mechanisms; Back-ground knowledge; Background information; Continuous improvements; E-commerce ecosystems; Graph neural networks; Knowledge information; Question answering systems; Electronic commerce
Authors: "Li Z.; Zhong Q.; Yang J.; Duan Y.; Wang W.; Wu C.; He K.
Title: DeepKG: An end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications
Year: 2022
Source title: Bioinformatics
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125735082&doi=10.1093%2fbioinformatics%2fbtab767&partnerID=40&md5=15221cf8f8447b41c75ab31a7542d8ac
Abstract: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144 900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43 760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform. Availability and implementation: All the results are publicly available at the website (http://covidkg.ai/). © 2021 The Author(s). Published by Oxford University Press. All rights reserved.
Author Keywords:
Index Keywords: Algorithms; Animals; COVID-19; Deep Learning; Pattern Recognition, Automated; Workflow; algorithm; animal; automated pattern recognition; workflow
Authors: "Zeng H.; Xu Q.
Title: Intelligent question answering system for medical knowledge
Year: 2022
Source title: Proceedings of SPIE - The International Society for Optical Engineering
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146694103&doi=10.1117%2f12.2661853&partnerID=40&md5=3c161e9173caaae889189da481656d0f
Abstract: In recent years, intelligent question answering system has been widely used in various interactive services. Intelligent question answering system is to arrange the accumulated disordered corpus information in an orderly and scientific way, and establish a classification model based on knowledge to support the question answering of various forms. In this paper, vertical medical website data and electronic medical record data are fused to solve the mapping problem of colloquial medical vocabulary and medical professional vocabulary. PLSA (probabilistic latent semantic analysis) theme architecture model solves the hidden theme content crawling problem of web page crawler. The application of vector space model and knowledge graph in medical knowledge question answering system solves the problem of low efficiency in traditional interactive question answering system. After testing, it can accurately understand the user's intention, and the accuracy of question and answer is high. © 2022 SPIE.
Author Keywords: Artificial intelligence; clinical thinking; mining
Index Keywords: Artificial intelligence; Classification (of information); Medical computing; Semantics; Vector spaces; Web crawler; Classification models; Clinical thinking; Intelligent question answering systems; Interactive services; Mapping problem; Medical knowledge; Medical record; Model-based OPC; Question Answering; Question answering systems; Websites
Authors: "Etezadi R.; Shamsfard M.
Title: PeCoQ: A dataset for persian complex question answering over knowledge graph
Year: 2020
Source title: 2020 11th International Conference on Information and Knowledge Technology, IKT 2020
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101394548&doi=10.1109%2fIKT51791.2020.9345610&partnerID=40&md5=9b50696a0568f11029f39ab753aa663b
Abstract: Question answering systems may find the answers to users' questions from either unstructured texts or structured data such as knowledge graphs. Answering questions using supervised learning approaches including deep learning models need large training datasets. In recent years, some datasets have been presented for the task of Question answering over knowledge graphs, which is the focus of this paper. Although many datasets in English were proposed, there have been a few question answering datasets in Persian. This paper introduces PeCoQ, a dataset for Persian question answering. This dataset contains 10, 000 complex questions and answers extracted from the Persian knowledge graph, FarsBase. For each question, the SPARQL query and two paraphrases that were written by linguists are provided as well. There are different types of complexities in the dataset, such as multi-relation, multi-entity, ordinal, and temporal constraints. In this paper, we discuss the dataset's characteristics and describe our methodolozv for buildinz it. © 2020 IEEE.
Author Keywords: complex question; knowledge graph; question answering
Index Keywords: Deep learning; Knowledge representation; Natural language processing systems; Complex questions; Knowledge graphs; Question Answering; Question answering systems; Supervised learning approaches; Temporal constraints; Training data sets; Unstructured texts; Large dataset
Authors: "Di Paolo G.; Rincon-Yanez D.; Senatore S.
Title: A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems
Year: 2023
Source title: Information (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151097432&doi=10.3390%2finfo14030186&partnerID=40&md5=150db02edd086594e6dfdafc004bc258
Abstract: Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form
Author Keywords: knowledge base; knowledge graph; knowledge graph embeddings; question answering
Index Keywords: Graph embeddings; Graphic methods; Knowledge management; Natural language processing systems; Query languages; Graph embeddings; Graph-based; Knowledge base; Knowledge graph embedding; Knowledge graphs; Learning methods; Question Answering; Question answering systems; Rapid growth; Knowledge graph
Authors: "Diefenbach D.; Giménez-García J.; Both A.; Singh K.; Maret P.
Title: QAnswer KG: Designing a Portable Question Answering System over RDF Data
Year: 2020
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086138570&doi=10.1007%2f978-3-030-49461-2_25&partnerID=40&md5=4c440c186b245a943ac560c91abea310
Abstract: While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users. Question Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user’s natural language question from an RDF dataset. In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets. In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses non-expert users in QA domain. In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets. To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an on-demand, scalable, multilingual, QA system on top of any RDF dataset. © Springer Nature Switzerland AG 2020.
Author Keywords: Knowledge Graphs; On-demand; Portability; QAnswer; Question Answering; RDF
Index Keywords: Artificial intelligence; Design; Natural language processing systems; Build systems; End users; Expert users; Knowledge graphs; Natural language questions; On demands; Question Answering; Question answering systems; Semantic Web
Authors: "Badenes-Olmedo C.; Corcho O.
Title: Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature
Year: 2023
Source title: Journal of Biomedical Informatics
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160010635&doi=10.1016%2fj.jbi.2023.104382&partnerID=40&md5=0a16db79dfa0e145441413f43b2e65fc
Abstract: The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The work contains best practices for acquiring scientific publications, tuning language models to identify and normalize relevant entities, creating representational models based on probabilistic topics, and formalizing an ontology that describes the associations between domain concepts supported by the scientific literature. All the resources generated in the domain of coronavirus are available openly as part of the Drugs4COVID initiative, and can be (re)-used independently or as a whole. They can be exploited by scientific communities conducting research related to SARS-CoV-2/COVID-19 and also by therapeutic communities, laboratories, etc., wishing to find and understand relationships between symptoms, drugs, active ingredients and their documentary evidence. © 2023 The Author(s)
Author Keywords: Evidences; Knowledge graphs; Ontology; Question-answering
Index Keywords: COVID-19; Humans; Pattern Recognition, Automated; Publications; SARS-CoV-2; Diseases; Knowledge graph; Modeling languages; Natural language processing systems; Ontology; chloroquine; lamivudine; neutralizing antibody; nicotine; Case-studies; Coronaviruses; Evidence; Knowledge graphs; Natural languages; Ontology's; Question Answering; Question answering systems; Scientific publications; Work-flows; algorithm; answering service; Article; cigarette smoking; coronavirus disease 2019; data mining; gene ontology; glycosylation; health care personnel; human; knowledge; knowledge base; language; machine learning; Middle East respiratory syndrome coronavirus; named entity recognition; natural language processing; ontology; open access publishing; pregnancy; publication; question; questionnaire; scientific literature; Severe acute respiratory syndrome coronavirus 2; smoke; therapeutic community; tobacco consumption; traditional medicine; workflow; automated pattern recognition; Coronavirus
Authors: "Amur Z.H.; Kwang Hooi Y.; Bhanbhro H.; Dahri K.; Soomro G.M.
Title: Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
Year: 2023
Source title: Applied Sciences (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152052419&doi=10.3390%2fapp13063911&partnerID=40&md5=4eef64a1fb0ff17bb4952c04c42fa64f
Abstract: In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information. © 2023 by the authors.
Author Keywords: deep learning; natural language processing; semantic similarity; short text; STSS
Index Keywords:
Authors: "Xu L.; Lu L.; Liu M.
Title: Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources
Year: 2023
Source title: Heritage Science
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174503236&doi=10.1186%2fs40494-023-01068-2&partnerID=40&md5=492e82b2b46208a36df69cd12ea8fef0
Abstract: Nanjing Yunjin, one of China's traditional silk weaving techniques, is renowned for its unique local characteristics and exquisite craftsmanship, and was included in the Representative List of the Intangible Cultural Heritage of Humanity by UNESCO in 2009. However, with rapid development in weaving technology, ever-changing market demands, and shifting public aesthetics, Nanjing Yunjin, as an intangible cultural heritage, faces the challenge of survival and inheritance. Addressing this issue requires efficient storage, management, and utilization of Yunjin knowledge to enhance public understanding and recognition of Yunjin culture. In this study, we have constructed an intelligent question-answering system for Nanjing Yunjin digital resources based on knowledge graph, utilizing the Neo4j graph database for efficient organization, storage, and protection of Nanjing Yunjin knowledge, thereby revealing its profound cultural connotations. Furthermore, we adopted deep learning algorithms for natural language parsing. Specifically, we adopted BERT-based intent recognition technology to categorize user queries by intent, and we employed the BERT + BiGRU + CRF model for entity recognition. By comparing with BERT + BILSTM + CRF, BERT + CRF and BILSTM + CRF models, our model demonstrated superior performance in terms of precision, recall, and F1 score, substantiating the superiority and effectiveness of this model. Finally, based on the parsed results of the question, we constructed knowledge graph query statements, executed by the Cypher language, and the processed query results were fed back to the users in natural language. Through system implementation and testing, multiple indices including system response time, stability, load condition, accuracy, and scalability were evaluated. The experimental results indicated that the Nanjing Yunjin intelligent question-answering system, built on the knowledge graph, is able to efficiently and accurately generate answers to user’s natural language queries, greatly facilitating the retrieval and utilization of Yunjin knowledge. This not only reinforces the transmission, promotion, and application of Yunjin culture but also provides a paradigm for constructing other intangible cultural heritage question-answering systems based on knowledge graphs. This has substantial theoretical and practical significance for deeply exploring and uncovering the knowledge structure of human intangible heritage, promoting cultural inheritance and protection. © 2023, Springer Nature Switzerland AG.
Author Keywords: Information retrieval; Intangible cultural heritage; Knowledge graph; Nanjing Yunjin; Question-answering system
Index Keywords:
Authors: "Lei J.S.; Ye S.C.; Yang S.Y.; Song W.; Liang G.M.
Title: An Algorithm of Vocabulary Enhanced Intelligent Question Answering Based on FLAT 1
Year: 2021
Source title: Frontiers in Artificial Intelligence and Applications
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123617255&doi=10.3233%2fFAIA210460&partnerID=40&md5=c35b73d9bae240fde856f823eb5085e0
Abstract: The main purpose of the intelligent question answering system based on the knowledge graph is to accurately match the natural language question and the triple information in the knowledge graph. Among them, the entity recognition part is one of the key points. The wrong entity recognition result will cause the error to be done propagated, resulting in the ultimate failure to get the correct answer. In recent years, the lexical enhancement structure of word nodes combined with word nodes has been proved to be an effective method for Chinese named entity recognition. In order to solve the above problems, this paper proposes a vocabulary-enhanced entity recognition algorithm (KGFLAT) based on FLAT for intelligent question answering system. This method uses a new dictionary that combines the entity information of the knowledge graph, and only uses layer normalization for the removal of residual connection for the shallower network model. The system uses data provided by the NLPCC 2018 Task7 KBQA task for evaluation. The experimental results show that this method can effectively solve the entity recognition task in the intelligent question answering system and achieve the improvement of the FLAT model, and the average F1 value is 94.72 © 2022 The authors and IOS Press.
Author Keywords: Entity recognition; Intelligent question and answer; Knowledge Graph; Residual connection; Vocabulary enhancement
Index Keywords: Natural language processing systems; Entity recognition; Intelligent question and answer; Intelligent question answering systems; Keypoints; Knowledge graphs; Natural language questions; Question Answering; Residual connection; Ultimate failure; Vocabulary enhancement; Knowledge graph
Authors: "Qian R.; Hou X.
Title: A Knowledge Representation Method for Question Answering Service in Mobile Edge Computing Environment
Year: 2022
Source title: Security and Communication Networks
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125837807&doi=10.1155%2f2022%2f1615596&partnerID=40&md5=cd6d10196802010d6f5080061465c514
Abstract: Driven by the rapid development of mobile computing and the Internet of Things, the number of devices connected to Internet has increased dramatically in recent years. Such development has generated massive amounts of data and highlighted the importance and urgency of using the accumulated big data to improve frequently used services. Deploying the question answering service in the mobile edge computing environment is considered a good way to make efficient use of the data and improve user experiences. Powered by the breakthroughs of deep learning technologies, question answering system based on knowledge graph (KBQA) has flourished in recent years. Knowledge representation, as a key technology of KBQA, can express the knowledge graph as the vectors containing more semantic information and thereby improve the accuracy of the question answering system. This paper proposes a knowledge representation method that integrates more features than the traditional methods. In our method, knowledge is represented as a combination of a structured vector reflecting the target triple and the domain information around the entity. By representing richer semantic vectors, our method outweighs TransE, ConvE, and KBAT, in terms of link prediction. © 2022 Rong Qian and Xia Hou.
Author Keywords:
Index Keywords: Deep learning; Knowledge graph; Semantics; Computing environments; Key technologies; Knowledge graphs; Knowledge representation method; Knowledge-representation; Learning technology; Mobile-computing; Question answering systems; Question-answering services; Users' experiences; Mobile edge computing
Authors: "Phan T.H.V.; Do P.
Title: BERT+vnKG: Using deep learning and knowledge graph to improve vietnamese question answering system
Year: 2020
Source title: International Journal of Advanced Computer Science and Applications
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088985283&doi=10.14569%2fIJACSA.2020.0110761&partnerID=40&md5=a42476a896ca180e281da94e02c20014
Abstract: A question answering (QA) system based on natural language processing and deep learning is a prominent area and is being researched widely. The Long Short-Term Memory (LSTM) model that is a variety of Recurrent Neural Network (RNN) used to be popular in machine translation, and question answering system. However, that model still has certainly limited capabilities, so a new model named Bidirectional Encoder Representation from Transformer (BERT) emerged to solve these restrictions. BERT has more advanced features than LSTM and shows state-of-the-art results in many tasks, especially in multilingual question answering system over the past few years. Nevertheless, we tried applying multilingual BERT model for a Vietnamese QA system and found that BERT model still has certainly limitation in term of time and precision to return a Vietnamese answer. The purpose of this study is to propose a method that solved above restriction of multilingual BERT and applied for question answering system about tourism in Vietnam. Our method combined BERT and knowledge graph to enhance accurately and find quickly for an answer. We experimented our crafted QA data about Vietnam tourism on three models such as LSTM, BERT fine-tuned multilingual for QA (BERT for QA), and BERT+vnKG. As a result, our model outperformed two previous models in terms of accuracy and time. This research can also be applied to other fields such as finance, e-commerce, and so on. © 2020 Science and Information Organization.
Author Keywords: Bidirectional encoder representation from transformer (BERT); Deep learning; Knowledge graph; Long short-term memory (LSTM); Natural language processing; Question answering (QA); Vietnamese tourism
Index Keywords: Brain; Knowledge graph; Natural language processing systems; Signal encoding; Bidirectional encoder representation from transformer; Deep learning; Knowledge graphs; Long short-term memory; Question Answering; Question answering systems; Transformer modeling; Vietnamese; Vietnamese tourism; Long short-term memory
Authors: "Ning B.; Zhao D.; Liu X.; Li G.
Title: EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation
Year: 2023
Source title: World Wide Web
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139215747&doi=10.1007%2fs11280-022-01100-8&partnerID=40&md5=16bdf6c7412a7f21328e7d0dfe25836b
Abstract: Multi-turn dialogue generation is an essential and challenging subtask of text generation in the question answering system. Existing methods focused on extracting latent topic-level relevance or utilizing relevant external background knowledge. However, they are prone to ignore the fact that relying too much on latent aspects will lose subjective key information. Furthermore, there is not so much relevant external knowledge that can be used for referencing or a graph that has complete entity links. Dependency tree is a special structure that can be extracted from sentences, it covers the explicit key information of sentences. Therefore, in this paper, we proposed the EAGS model, which combines the subjective pivotal information from the explicit dependency tree with sentence implicit semantic information. The EAGS model is a knowledge graph enabled multi-turn dialogue generation model, and it doesn’t need extra external knowledge, it can not only extract and build a dependency knowledge graph from existing sentences, but also prompt the node representation, which is shared with Bi-GRU each time step word embedding in node semantic level. We store the specific domain subgraphs built by the EAGS, which can be retrieved as external knowledge graph in the future multi-turn dialogue generation task. We design a multi-task training approach to enhance semantics and structure local feature extraction, and balance with the global features. Finally, we conduct experiments on Ubuntu large-scale English multi-turn dialogue community dataset and English Daily dialogue dataset. Experiment results show that our EAGS model performs well on both automatic evaluation and human evaluation compared with the existing baseline models. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Author Keywords: Build knowledge graph; Dependency tree; Knowledge graph; Natural language processing; Question answering systems; Text generation
Index Keywords: Data mining; Forestry; Large dataset; Natural language processing systems; Semantics; Trees (mathematics); Build knowledge graph; Dependency trees; Knowledge graphs; Language processing; Multi-turn; Natural language processing; Natural languages; Question answering systems; Text generations; Knowledge graph
Authors: "Banerjee D.; Chaudhuri D.; Dubey M.; Lehmann J.
Title: PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs
Year: 2020
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096601166&doi=10.1007%2f978-3-030-62419-4_2&partnerID=40&md5=4473240c8b4e6469eddf83b20b0fbc75
Abstract: Question Answering systems are generally modelled as a pipeline consisting of a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first step. Several EL models first perform span detection and then entity disambiguation. In such models errors from the span detection phase cascade to later steps and result in a drop of overall accuracy. Moreover, lack of gold entity spans in training data is a limiting factor for span detector training. Hence the movement towards end-to-end EL models began where no separate span detection step is involved. In this work we present a novel approach to end-to-end EL by applying the popular Pointer Network model, which achieves competitive performance. We demonstrate this in our evaluation over three datasets on the Wikidata Knowledge Graph. © 2020, Springer Nature Switzerland AG.
Author Keywords: Entity Linking; Knowledge Graphs; Question Answering; Wikidata
Index Keywords: Knowledge representation; Pipelines; Competitive performance; Detection phase; Entity disambiguation; Knowledge graphs; Network modeling; Network-based; Overall accuracies; Question answering systems; Semantic Web
Authors: "Kuang H.; Chen H.; Ma X.; Liu X.
Title: A Keyword Detection and Context Filtering Method for Document Level Relation Extraction
Year: 2022
Source title: Applied Sciences (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124991818&doi=10.3390%2fapp12031599&partnerID=40&md5=419c1214ebe004b265807e7185268935
Abstract: Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords: Context extraction; ConvLSTM; Detection; Relation extraction; Self-Attention; Transformer
Index Keywords:
Authors: "Tian X.; Tang H.; Cheng L.; Liao Z.; Li Y.; He J.; Ren P.; You M.; Pang Z.
Title: Evaluation System Framework of Artificial Intelligence Applications in Medical Diagnosis and Treatment
Year: 2022
Source title: Procedia Computer Science
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146110621&doi=10.1016%2fj.procs.2022.11.204&partnerID=40&md5=e1f1f98ec6a0b588d5a1313d6b6f804a
Abstract: Current medical artificial intelligence applications and products are confronted the dilemma of lacking standardized practical evaluation guidelines and management methods. This research adopts the Donabedian medical quality management classic model and the DeLone & Mclean benefit evaluation model to develop a basic framework of the medical artificial intelligence application evaluation system for auxiliary diagnosis and treatment and to design the corresponding evaluation procedures. This paper illustrates the overall project framework, followed by a detailed structure and construction process of the model proposed for fast and accurate medical evaluations, NHCKG. NHCKG provides a holistic view of diseases, medical regulations and evaluations. The proposed NHCKG can provide partial solutions to significant challenges faced by knowledge graphs and natural language processing (NLP). Based on the knowledge graph, the structure of a related medical question answering system is elaborated along with its advantages and disadvantages. The evaluation process can take this procedure as a blueprint for exploring standardized and practical evaluation of medical artificial intelligence applications. The process aims to build a concrete measurement basis for medical artificial intelligence products and to promote the healthy and stable development of the medical artificial intelligence industry. © 2022 The Authors. Published by Elsevier B.V.
Author Keywords: Diagnosis; Evaluation system applications; Medical artificial intelligence; Pharmaceutical agent for gray matter volume damage; Treatment
Index Keywords: Diagnosis; Knowledge graph; Product design; Quality control; Quality management; Evaluation system application; Gray matter volumes; Knowledge graphs; Medical artificial intelligence; Pharmaceutical agent for gray matter volume damage; Pharmaceutical agents; System applications; System framework; Treatment; Volume damage; Natural language processing systems
Authors: "Wiharja K.R.S.; Murdiansyah D.T.; Romdlony M.Z.; Ramdhani T.; Gandidi M.R.
Title: A Questions Answering System on Hadith Knowledge Graph
Year: 2022
Source title: Journal of ICT Research and Applications
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140239147&doi=10.5614%2fitbj.ict.res.appl.2022.16.2.6&partnerID=40&md5=10c677cb1d0462b06b15452d2958d760
Abstract: Several works have presented the Hadith on different digital platforms, ranging from websites to mobile apps. These works were successful in presenting the text of the Hadith to users, but this does not help them to answer any particular questions about religious matters. Therefore, in this work we propose a question-answering system that was built on a Hadith knowledge graph. To interpret the user questions correctly, we used the Levenshtein distance function, and for storing the Hadith in graph format we used Neo4J as the graph database. Our main findings were: (i) a knowledge graph is suitable for representing the Hadith and also for doing the reasoning task, and (ii) our proposed approach achieved 95% for top-1 accuracy. © 2022 Published by IRCS-ITB.
Author Keywords: Hadith; knowledge graph; question-answer system; reasoning; semantic
Index Keywords:
Authors: "Varma S.; Ramkrishnan A.; Shivam S.; Natarajan S.; Biswas S.; Anukriti A.
Title: Graph NLU Enabled Question Answering System
Year: 2021
Source title: Lecture Notes in Networks and Systems
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111410106&doi=10.1007%2f978-3-030-79757-7_21&partnerID=40&md5=4ee70767dcb2314cf75457371fec96a1
Abstract: With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves se-mantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. We extract appropriate answers for different types of queries which have been illustrated in the Results section. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords: Conversational analytics; Graph traversal; Knowledge graph; Natural language query; Question answering; Structured data; Tabular data
Index Keywords:
Authors: "Halike A.; Abiderexiti K.; Yibulayin T.
Title: Semi-automatic corpus expansion and extraction of uyghur-named entities and relations based on a hybrid method
Year: 2020
Source title: Information (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079035010&doi=10.3390%2finfo11010031&partnerID=40&md5=da2537c978af69c328a8600a11cff1c2
Abstract: Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur. © 2020 by the authors.
Author Keywords: Conditional randomfield; Hybrid neural network; Named entity; Relation extraction; Uyghur
Index Keywords: Extraction; Linguistics; Natural language processing systems; Conditional randomfield; Hybrid neural networks; Named entities; Relation extraction; Uyghur; Data mining
Authors: "Agrawal G.; Pal K.; Deng Y.; Liu H.; Chen Y.-C.
Title: CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs
Year: 2024
Source title: Proceedings of the AAAI Conference on Artificial Intelligence
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189648091&doi=10.1609%2faaai.v38i21.30362&partnerID=40&md5=f4f95a6cc7ce9b687a924c14f3a0734f
Abstract: Building a skilled cybersecurity workforce is paramount to building a safer digital world. However, the diverse skill set, constantly emerging vulnerabilities, and deployment of new cyber threats make learning cybersecurity challenging. Traditional education methods struggle to cope with cybersecurity's rapidly evolving landscape and keep students engaged and motivated. Different studies on students' behaviors show that an interactive mode of education by engaging through a question-answering system or dialoguing is one of the most effective learning methodologies. There is a strong need to create advanced AI-enabled education tools to promote interactive learning in cybersecurity. Unfortunately, there are no publicly available standard question-answer datasets to build such systems for students and novice learners to learn cybersecurity concepts, tools, and techniques. The education course material and online question banks are unstructured and need to be validated and updated by domain experts, which is tedious when done manually. In this paper, we propose CyberGen, a novel unification of large language models (LLMs) and knowledge graphs (KG) to generate the questions and answers for cybersecurity automatically. Augmenting the structured knowledge from knowledge graphs in prompts improves factual reasoning and reduces hallucinations in LLMs. We used the knowledge triples from cybersecurity knowledge graphs (AISecKG) to design prompts for ChatGPT and generate questions and answers using different prompting techniques. Our question-answer dataset, CyberQ, contains around 4k pairs of questions and answers. The domain expert manually evaluated the random samples for consistency and correctness. We train the generative model using the CyberQ dataset for question answering task. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Author Keywords:
Index Keywords: Cybersecurity; Education computing; Learning systems; Students; Cyber security; Cyber threats; Cyber-security educations; Digital world; Domain experts; Education methods; Knowledge graphs; Language model; Skill sets; Traditional educations; Knowledge graph
Authors: "Li J.; Luo Z.; Huang H.; Ding Z.
Title: Towards Knowledge-Based Tourism Chinese Question Answering System
Year: 2022
Source title: Mathematics
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125463062&doi=10.3390%2fmath10040664&partnerID=40&md5=43c1b06bcd06540cd480088b94f2a22b
Abstract: With the rapid development of the tourism industry, various travel websites are emerging. The tourism question answering system explores a large amount of information from these travel websites to answer tourism questions, which is critical for providing a competitive travel experience. In this paper, we propose a framework that automatically constructs a tourism knowledge graph from a series of travel websites with regard to tourist attractions in Zhejiang province, China. Backed by this domain-specific knowledge base, we developed a tourism question answering system that also incorporates the underlying knowledge from a large-scale language model such as BERT. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. We also show the effectiveness of each of the question answering components in detail, including the query intent recognition and the answer generation. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords: Intent recognition; Knowledge graph; Language model; Tourism question answering system
Index Keywords:
Authors: "Siciliani L.; Basile P.; Lops P.; Semeraro G.; Sack H.
Title: MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs
Year: 2022
Source title: Semantic Web
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124742736&doi=10.3233%2fSW-210440&partnerID=40&md5=3369deb0045e3d87a1021d201c056e07
Abstract: Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user's question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art. © 2022 - The authors. Published by IOS Press.
Author Keywords: Benchmark; Dataset; Knowledge graphs; Question answering; SPARQL modifiers
Index Keywords:
Authors: "Falke S.
Title: Developing a Knowledge Graph for a Question Answering System to Answer Natural Language Questions on German Grammar
Year: 2019
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075568709&doi=10.1007%2f978-3-030-32327-1_39&partnerID=40&md5=5d665fc1c49fe1221b55ab9624cd956e
Abstract: Question Answering Systems for retrieving information from Knowledge Graphs (KG) have become a major area of interest in recent years. Current systems search for words and entities but cannot search for grammatical phenomena. The purpose of this paper is to present our research on developing a QA System that answers natural language questions about German grammar. Our goal is to build a KG which contains facts and rules about German grammar, and is also able to answer specific questions about a concrete grammatical issue. An overview of the current research in the topic of QA systems and ontology design is given and we show how we plan to construct the KG by integrating the data in the grammatical information system Grammis, hosted by the Leibniz-Institut für Deutsche Sprache (IDS). In this paper, we describe the construction of the initial KG, sketch our resulting graph, and demonstrate the effectiveness of such an approach. A grammar correction component will be part of a later stage. The paper concludes with the potential areas for future research. © Springer Nature Switzerland AG 2019.
Author Keywords: German grammar; Knowledge Graph; Ontology development; Question Answering System
Index Keywords: Knowledge representation; Network security; Ontology; Semantic Web; Area of interest; German grammar; Grammatical information; Knowledge graphs; Natural language questions; Ontology design; Ontology development; Question answering systems; Natural language processing systems
Authors: "Nair L.S.; Shivani M.K.; Sr Jo C.
Title: Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques
Year: 2022
Source title: Procedia Computer Science
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159763775&doi=10.1016%2fj.procs.2022.12.064&partnerID=40&md5=0ad585ada86dd3c24236842077414a67
Abstract: An automated question-answering system allows students to learn as an integral part of digitized learning. This system responds to queries using text. We also include a knowledge graph, which significantly enhances the model's intrigue and improves learners' understanding. The features of knowledge entity extraction, information point evaluation and analysis, knowledge graph construction from unstructured text, and knowledge entity integration are all explored. The question-answering paradigm we suggest in this study uses knowledge graphs and BERT (Bidirectional Encoder Representations from Transformers) to provide diverse learners with quick feedback on the subject. In order to facilitate non-native learners' understanding, we also include an English to Hindi translation. As a result, access to and continued learning can be very beneficial for educators. © 2023 The Authors. Published by Elsevier B.V.
Author Keywords: Bidirectional Encoder Representations from Transformers; Knowledge Graph; Long Short-Term Memory; Machine Translation; Natural Language Processing; Question-Answering; Remote Education
Index Keywords: Deep learning; Learning systems; Natural language processing systems; Signal encoding; Bidirectional encoder representation from transformer; Knowledge graphs; Language processing; Learning techniques; Machine translations; Natural language processing; Natural languages; Question Answering; Remote education; School education; Knowledge graph
Authors: "Xu L.; Lu L.; Liu M.; Song C.; Wu L.
Title: Nanjing Yunjin intelligent question-answering system based on knowledge graphs and retrieval augmented generation technology
Year: 2024
Source title: Heritage Science
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189988524&doi=10.1186%2fs40494-024-01231-3&partnerID=40&md5=fa422be6944ff08e26848af12bba0f7b
Abstract: Nanjing Yunjin, a traditional Chinese silk weaving craft, is celebrated globally for its unique local characteristics and exquisite workmanship, forming an integral part of the world's intangible cultural heritage. However, with the advancement of information technology, the experiential knowledge of the Nanjing Yunjin production process is predominantly stored in text format. As a highly specialized and vertical domain, this information is not readily convert into usable data. Previous studies on a knowledge graph-based Nanjing Yunjin Question-Answering System have partially addressed this issue. However, knowledge graphs need to be constantly updated and rely on predefined entities and relationship types. Faced with ambiguous or complex natural language problems, knowledge graph information retrieval faces some challenges. Therefore, this study proposes a Nanjing Yunjin Question-Answering System that integrates Knowledge Graphs and Retrieval Augmented Generation techniques. In this system, the ROBERTA model is first utilized to vectorize Nanjing Yunjin textual information, delving deep into textual semantics to unveil its profound cultural connotations. Additionally, the FAISS vector database is employed for efficient storage and retrieval of Nanjing Yunjin information, achieving a deep semantic match between questions and answers. Ultimately, related retrieval results are fed into the Large Language Model for enhanced generation, aiming for more accurate text generation outcomes and improving the interpretability and logic of the Question-Answering System. This research merges technologies like text embedding, vectorized retrieval, and natural language generation, aiming to overcome the limitations of knowledge graphs-based Question-Answering System in terms of graph updating, dependency on predefined types, and semantic understanding. System implementation and testing have shown that the Nanjing Yunjin Intelligent Question-Answering System, constructed on the basis of Knowledge Graphs and Retrieval Augmented Generation, possesses a broader knowledge base that considers context, resolving issues of polysemy, vague language, and sentence ambiguity, and efficiently and accurately generates answers to natural language queries. This significantly facilitates the retrieval and utilization of Yunjin knowledge, providing a paradigm for constructing Question-Answering System for other intangible cultural heritages, and holds substantial theoretical and practical significance for the deep exploration and discovery of the knowledge structure of human intangible heritage, promoting cultural inheritance and protection. © The Author(s) 2024.
Author Keywords: Knowledge graphs; Nanjing Yunjin; Question-Answering System; Retrieval augmented generation; Vector retrieval
Index Keywords:
Authors: "Ren X.; Sengupta N.; Ren X.; Wang J.; Cure O.
Title: Finding Minimum Connected Subgraphs With Ontology Exploration on Large RDF Data
Year: 2023
Source title: IEEE Transactions on Knowledge and Data Engineering
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144776252&doi=10.1109%2fTKDE.2022.3225076&partnerID=40&md5=c8a32f421a5c95d45b84b5c371e0a53d
Abstract: In this paper, we study the following problem: given a knowledge graph (KG) and a set of input vertices (representing concepts or entities) and edge labels, we aim to find the smallest connected subgraphs containing all of the inputs. This problem plays a key role in KG-based search engines and natural language question answering systems, and it is a natural extension of the Steiner tree problem, which is known to be NP-hard. We present RECON, a system for finding approximate answers. RECON aims at achieving high accuracy with instantaneous response (i.e., sub-second/millisecond delay) over KGs with hundreds of millions edges without resorting to expensive computational resources. Furthermore, when no answer exists due to disconnection between concepts and entities, RECON refines the input to a semantically similar one based on the ontology, and attempts to find answers with respect to the refined input. We conduct a comprehensive experimental evaluation of RECON. In particular we compare it with five existing approaches for finding approximate Steiner trees. Our experiments on four large real and synthetic KGs show that RECON significantly outperforms its competitors and incurs a much smaller memory footprint. © 1989-2012 IEEE.
Author Keywords: knowledge graph; ontology; reasoning; search
Index Keywords: Job analysis; Natural language processing systems; Resource Description Framework (RDF); Search engines; Trees (mathematics); Cognition; Index; Keyword search; Knowledge graphs; Ontology's; Reasoning; Resources description frameworks; Steiner trees; Task analysis; Ontology
Authors: "Guo S.; Yang W.; Han L.; Song X.; Wang G.
Title: A multi-layer soft lattice based model for Chinese clinical named entity recognition
Year: 2022
Source title: BMC medical informatics and decision making
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135501546&doi=10.1186%2fs12911-022-01924-4&partnerID=40&md5=776d0bd42350676f9378ed84ff443ccb
Abstract: OBJECTIVE: Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities from electronic health records (EHRs), NER models mostly apply long short-term memory (LSTM) and have surprising performance in clinical NER. However, increasing the depth of the network is often required by these LSTM-based models to capture long-distance dependencies. Therefore, these LSTM-based models that have achieved high accuracy generally require long training times and extensive training data, which has obstructed the adoption of LSTM-based models in clinical scenarios with limited training time. METHOD: Inspired by Transformer, we combine Transformer with Soft Term Position Lattice to form soft lattice structure Transformer, which models long-distance dependencies similarly to LSTM. Our model consists of four components: the WordPiece module, the BERT module, the soft lattice structure Transformer module, and the CRF module. RESULT: Our experiments demonstrated that this approach increased the F1 by 1-5% in the CCKS NER task compared to other models based on LSTM with CRF and consumed less training time. Additional evaluations showed that lattice structure transformer shows good performance for recognizing long medical terms, abbreviations, and numbers. The proposed model achieve 91.6% f-measure in recognizing long medical terms and 90.36% f-measure in abbreviations, and numbers. CONCLUSIONS: By using soft lattice structure Transformer, the method proposed in this paper captured Chinese words to lattice information, making our model suitable for Chinese clinical medical records. Transformers with Mutilayer soft lattice Chinese word construction can capture potential interactions between Chinese characters and words. © 2022. The Author(s).
Author Keywords: Clinical named entity recognition; Clinical text mining; Fine-tuning BERT; Medical information processing; Transformer; Word-character lattice
Index Keywords: China; Electronic Health Records; Humans; Natural Language Processing; China; electronic health record; human; natural language processing
Authors: "Li A.; Wei Q.; Han C.; Xing X.
Title: Research on the construction of smart care question answering system based on knowledge graph
Year: 2022
Source title: Procedia Computer Science
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146115234&doi=10.1016%2fj.procs.2022.11.348&partnerID=40&md5=e4c4983a2fb1596512d9187d8411fdb1
Abstract: With the deepening of aging in China, the demand of intelligent care system for the elderly is increasing day by day. The rapid development of information technology and artificial intelligence technology have gradually promoted the transformation of traditional care services from artificial to intelligent. Based on knowledge graph technology, this study built a knowledge graph model for elderly chronic disease smart care. The specific process includes ontology design, knowledge extraction, knowledge graph construction and question answering system design. The uniqueness of the knowledge graph in this paper is that it uses specific information about the elderly and data from professional care books. It is well guaranteed in practicality and reliability.With the help of the knowledge map constructed in this paper, primary care staff can effectively query high-quality and customized chronic disease care knowledge. © 2022 The Authors. Published by Elsevier B.V.
Author Keywords: Knowledge graph; Question answering system; Smart care
Index Keywords: Diseases; Query processing; Artificial intelligence technologies; Chronic disease; Design knowledge extraction; Graph construction; Graph model; Knowledge graphs; Ontology design; Question answering systems; Smart care; Specific information; Knowledge graph
Authors: "Yang Y.; Yin X.; Yang H.; Fei X.; Peng H.; Zhou K.; Lai K.; Shen J.
Title: KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph
Year: 2021
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104812030&doi=10.1007%2f978-3-030-73194-6_13&partnerID=40&md5=079ac5806d65f19022161618b0a92aeb
Abstract: Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (OOV) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named KGSynNet. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed fusion gate to adaptively absorb the entities’ knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our KGSynNet in leveraging the knowledge graph. The experimental results show that the KGSynNet improves the state-of-the-art methods by 14.7% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3% in the positive feedback rate of an online A/B test on the entity linking module of a question answering system. © 2021, Springer Nature Switzerland AG.
Author Keywords: Entity synonyms discovery; Knowledge graph
Index Keywords: Database systems; Embeddings; Knowledge representation; Natural language processing systems; Semantics; Discovery frameworks; Knowledge graphs; Knowledge information; Offline evaluation; Question answering systems; Semantic features; State-of-the-art methods; Surface matching; Knowledge management
Authors: "Schneider P.; Klettner M.; Jokinen K.; Simperl E.; Matthes F.
Title: Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs
Year: 2024
Source title: International Conference on Agents and Artificial Intelligence
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185571476&doi=10.5220%2f0012394300003636&partnerID=40&md5=92a06e0bd93f02d28da475df1318e695
Abstract: Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input. For information-seeking conversations about facts stored within a knowledge graph, dialogue utterances are transformed into graph queries in a process that is called knowledge-based conversational question answering. This paper evaluates the performance of large language models that have not been explicitly pre-trained on this task. Through a series of experiments on an extensive benchmark dataset, we compare models of varying sizes with different prompting techniques and identify common issue types in the generated output. Our results demonstrate that large language models are capable of generating graph queries from dialogues, with significant improvements achievable through few-shot prompting and fine-tuning techniques, especially for smaller models that exhibit lower zero-shot performance. © 2024 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords: Conversational Question Answering; Knowledge Graphs; Large Language Models; Semantic Parsing
Index Keywords:
Authors: "Xiaoxue L.; Xuesong B.; Longhe W.; Bingyuan R.; Shuhan L.; Lin L.
Title: Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases
Year: 2019
Source title: IEEE Access
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066821672&doi=10.1109%2fACCESS.2019.2915987&partnerID=40&md5=1f93d64681297d3ccb372095f6892edb
Abstract: Current techniques of knowledge management have some common defects in efficiency, scalability, and applicability. Knowledge graph provides a new way for knowledge management and is a more flexible knowledge management method. Considering the specific features of crop diseases and pest data, this paper analyzed and classified the key techniques and methods of knowledge graph technology in the field of crop diseases and pest in recent years. We introduced the definition and connotation of the crop diseases and pest knowledge, and the current construction method of it was analyzed in detail from four aspects: knowledge representation, extraction, fusion, and reasoning. Furthermore, the application of crop diseases and pest knowledge graph was introduced in detail in the expert system, search engine, and knowledge question-answering system. At last, this paper summarized the challenges and important problems of diseases and pest knowledge graph and forecasted the prospect of knowledge graph according to the key points and difficulties of current knowledge graph research. © 2013 IEEE.
Author Keywords: crop disease and pest; knowledge extraction; knowledge fusion; Knowledge graph; knowledge reasoning
Index Keywords: Crops; Expert systems; Extraction; Knowledge management; Knowledge representation; Search engines; Crop disease; Knowledge extraction; Knowledge fusion; Knowledge graphs; Knowledge reasoning; Data mining
Authors: "Li Z.; Yan H.; Long Y.; Shi Y.; Guo H.; Yu P.
Title: Research on Named Entity Recognition and Knowledge Graph Construction Based on BERT-BiLSTM-CRF
Year: 2024
Source title: Advances in Transdisciplinary Engineering
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189534658&doi=10.3233%2fATDE231193&partnerID=40&md5=08b9503ea0a42cb08a25ffcf2c45279e
Abstract: With the continuous improvement and development of question answering systems, combining them with knowledge graphs in relevant professional fields can relatively improve their practicality. Most existing knowledge graph models are general knowledge graphs with wide coverage, but their quality is poor, leading to issues such as loose data and low coverage, resulting in low coverage of professional knowledge matching in the Q&A process and often unsatisfactory answers. To address this drawback, this article proposes a named entity recognition method based on the BERT-BiLSTM-CRF model. This method can fully combine BERT's context aware embedding, BiLSTM's sequence modeling ability, and CRF's label dependency modeling to improve the accuracy of identifying and annotating entities in text. Comparative experiments with different models have shown that the BERT-BiLSTM-CRF model outperforms the BERT-BiLSTM and BERT-CRF models in terms of accuracy. Therefore, the combination of knowledge graph and BERT-BiLSTM-CRF model applied to question answering systems can greatly improve the accuracy of knowledge extraction, make question answering scenarios more anthropomorphic, and reduce the probability of non answering situations. © 2024 The Authors.
Author Keywords: Annotate Entities; BERT BiLSTM CRF model; Knowledge graph; Named entity recognition; Q&A system
Index Keywords: Annotate entity; BERT BiLSTM CRF model; Continuous development; Continuous improvements; Graph construction; Knowledge graphs; Named entity recognition; Professional fields; Q&A system; Question answering systems; Knowledge graph
Authors: "Agrawal G.; Bertsekas D.; Liu H.
Title: Auction-Based Learning for Question Answering over Knowledge Graphs
Year: 2023
Source title: Information (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163371913&doi=10.3390%2finfo14060336&partnerID=40&md5=4db794372de494bf503e810b18522a04
Abstract: Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. In this research work, we propose using new auction algorithms for question answering over knowledge graphs. These algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. The prices are initially assigned arbitrarily and updated dynamically based on defined rules. The algorithm navigates the graph from the high-price to the low-price nodes. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide”: to steer it toward the lower-priced nodes. Our approach reduces the search computational effort by 60% in our experiments, thus making the algorithm computationally efficient. The resulting path given by the algorithm can be mapped to the attributes of entities and relations in knowledge graphs to provide an explainable answer to the query. We discuss some applications for which our method can be used. © 2023 by the authors.
Author Keywords: auction algorithms; knowledge graph question answering (KGQA); knowledge graphs
Index Keywords: Computational efficiency; Costs; Directed graphs; Graphic methods; Natural language processing systems; Query processing; Auction algorithms; Graph-based; Knowledge graph question answering; Knowledge graphs; Natural language questions; Off-line training; Question Answering; Question answering systems; Real-time data; Knowledge graph
Authors: "Oduro-Afriyie J.; Jamil H.M.
Title: Enabling the Informed Patient Paradigm with Secure and Personalized Medical Question Answering
Year: 2023
Source title: ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175859728&doi=10.1145%2f3584371.3613016&partnerID=40&md5=ee953356ef7f31dbf974e9c67dcfe914
Abstract: Quality patient care is a complex and multifaceted problem requiring the integration of data from multiple sources. We propose Medicient, a knowledge-graph-based question answering system that processes heterogeneous data sources, including patient health records, drug databases, and medical literature, into a unified knowledge graph with zero training. The knowledge graph is then utilized to provide personalized recommendations for treatment or medication. The system leverages the power of large language models for question understanding and natural language response generation, while hiding sensitive patient information. We compare our system to a large language model (ChatGPT), which does not have access to patient health records, and show that our system provides better recommendations. This study contributes to a growing body of research on knowledge graphs and their applications in healthcare. © 2023 ACM.
Author Keywords: data integration; informed patients; knowledge graphs; large language models; personal health library; semantic graph search
Index Keywords: Computational linguistics; Graphic methods; Knowledge graph; Medical informatics; Natural language processing systems; Semantics; Graph search; Informed patient; Knowledge graphs; Language model; Large language model; Patient health; Personal health; Personal health library; Semantic graph search; Semantic graphs; Data integration
Authors: "Yani M.; Krisnadhi A.A.; Budi I.
Title: A better entity detection of question for knowledge graph question answering through extracting position-based patterns
Year: 2022
Source title: Journal of Big Data
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132355752&doi=10.1186%2fs40537-022-00631-1&partnerID=40&md5=92c167b539e730dcb9e710cc1b81cfb6
Abstract: Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect entities and their position on triples mentioned in a question. Unlike existing approaches that only focus on detecting the entity name, our method can determine in which triple an entity is located. Furthermore, our approach can also define if an entity is a head or a tail of a triple mentioned in a question. We tested our approach to SimpleQuestions, LC-QuAD 2.0, and QALD series benchmarks. The experiment result demonstrates that our model outperforms the previous works on SimpleQuestions and QALD series datasets. 99.15% accuracy and 96.15% accuracy on average, respectively. Our model can also improve entity detection performance on LC-QuAD 2.0 with a merged dataset, namely, 97.4% accuracy. This paper also presents Wikidata QALD series version that is helpful for researchers to assess the knowledge graph question answering system they develop. © 2022, The Author(s).
Author Keywords: Complex question; Entity detection; Entity recognition; Knowledge graph question answering; Question pattern
Index Keywords: Pattern recognition; Complex questions; Detection tasks; Entity detection; Entity recognition; Knowledge graph question answering; Knowledge graphs; Question Answering; Question answering systems; Question pattern; Simple++; Knowledge graph
Authors: "Liu S.; Tan N.; Yang H.; Lukač N.
Title: An Intelligent Question Answering System of the Liao Dynasty Based on Knowledge Graph
Year: 2021
Source title: International Journal of Computational Intelligence Systems
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116103480&doi=10.1007%2fs44196-021-00010-3&partnerID=40&md5=9c55d0a77615ba1c3007041e67c6df22
Abstract: The Liao Dynasty was a minority regime established by the Khitan on the grasslands of northern China. To promote and spread the cultural knowledge of the Liao Dynasty, an intelligent question-and-answer system is constructed based on the knowledge graph in the historical and cultural field of the Liao Dynasty. In the traditional question answering system, the quality of answers was not high due to incomplete data and distinctive vocabulary. To solve this problem, a combination method of Liao Dynasty question-and-answer database and KB is proposed to realize knowledge graph question answering, and a joint model of Siamese LSTM and fusion MatchPyramid is proposed for semantic matching between questions in the question-and-answer database. With the joint model, it is easy to perform semantic matching by fusing sentence-level and word-level interactive features through LSTM and MatchPyramid. Furthermore, the question sentence with the same semantics as the user input question sentence is retrieved in the question-and-answer database, and the answer corresponding to the question sentence is returned as the result. The experimental results show that our proposed method has achieved relatively good performance in the historical domain of the Liao Dynasty and the open-domain knowledge graph, and improved the accuracy of question and answer. © 2021, The Author(s).
Author Keywords: Deep semantic matching; Knowledge graph; MatchPyramid; Question answering; Siamese LSTM
Index Keywords: Database systems; Long short-term memory; Semantics; Answer database; Deep semantic matching; Intelligent question answering systems; Joint models; Knowledge graphs; Matchpyramid; Northern China; Question Answering; Semantic matching; Siamese LSTM; Knowledge graph
Authors: "Kanwal S.; Malik M.K.; Nawaz Z.; Mehmood K.
Title: Urdu Wikification and Its Application in Urdu News Recommendation System
Year: 2022
Source title: IEEE Access
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139382006&doi=10.1109%2fACCESS.2022.3208666&partnerID=40&md5=c72c5b01c44919123649b8666f37c942
Abstract: Wikification is the process of linking the entities found in a sample text to their individual Wikipedia or Wikidata pages. Many natural language processing applications, including question-answering systems, information retrieval, fraud detection, and recommendation systems(RS), can benefit from this information extraction technique. There has been a great deal of effort put towards entity-linking(EL) for both Asian and Western languages, with several datasets and numerous proposed methodologies. Despite millions of Urdu language users globally, relatively little entity-linking research has been done for Urdu. This work proposes an Urdu EL pipeline to identify named entities in text and link them to Wikidata. Secondly, a dataset of 550 Urdu news titles relating to their respective Wiki-ids has been prepared for the examination. Third, utilizing the proposed EL pipeline, 16738 news articles from the first-ever Urdu news RS dataset of 100 users are annotated. Fourthly, a sub Knowledge graph (KG) of 8439 entities and 23080 relationship tuples is retrieved from Wikidata. The Trans-E algorithm is then used to create KG embeddings so that the extracted KG may be used in an Urdu news RS. The final accuracy of Urdu news RS is 60.8%. © 2013 IEEE.
Author Keywords: Information retrieval; recommender systems; RNN
Index Keywords: Knowledge graph; Natural language processing systems; Online systems; Pipelines; Recommender systems; Search engines; Web services; Annotation; Encyclopedia; ITS applications; Knowledge graphs; News recommendation; On-line service; Task analysis; Text analysis; Text categorization; Wikipedia; Semantics
Authors: "Zhao C.; Xiong C.; Qian X.; Boyd-Graber J.
Title: Complex Factoid Question Answering with a Free-Text Knowledge Graph
Year: 2020
Source title: The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086574858&doi=10.1145%2f3366423.3380197&partnerID=40&md5=179e203a17272f1988e279c274f69dbe
Abstract: We introduce delft, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. delft builds a free-text knowledge graph from Wikipedia, with entities as nodes and sentences in which entities co-occur as edges. For each question, delft finds the subgraph linking question entity nodes to candidates using text sentences as edges, creating a dense and high coverage semantic graph. A novel graph neural network reasons over the free-text graph - combining evidence on the nodes via information along edge sentences - to select a final answer. Experiments on three question answering datasets show delft can answer entity-rich questions better than machine reading based models, bert-based answer ranking and memory networks. delft's advantage comes from both the high coverage of its free-text knowledge graph - more than double that of dbpedia relations - and the novel graph neural network which reasons on the rich but noisy free-text evidence. © 2020 ACM.
Author Keywords: Factoid Question Answering; Free-Text Knowledge Graph; Graph Neural Network
Index Keywords: Semantics; World Wide Web; Factoid questions; Free texts; Graph neural networks; Knowledge graphs; Memory network; Question Answering; Semantic graphs; Wikipedia; Graph theory
Authors: "Liu S.; Tan N.; Ge Y.; Lukač N.
Title: Research on automatic question answering of generative knowledge graph based on pointer network
Year: 2021
Source title: Information (Switzerland)
Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103184572&doi=10.3390%2finfo12030136&partnerID=40&md5=b7d6f1efbcf602de7ad7684072584228
Abstract: Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Outof- vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods. © 2021 by the authors.
Author Keywords: Answer generative model; Entity recognition; Knowledge base question answering; Pointer generator network; Pre-trained language model
Index Keywords: Data handling; Graphic methods; Knowledge based systems; Knowledge representation; Semantics; Automatic question answering; Data preprocessing; Knowledge graphs; NAtural language processing; Question Answering; Question answering systems; Semantic features; Word frequencies; Natural language processing systems