Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in...

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Published in:	Knowledge and information systems Vol. 63; no. 10; pp. 2741 - 2761
Main Authors:	Cui, Hai, Peng, Tao, Feng, Lizhou, Bao, Tie, Liu, Lu
Format:	Journal Article
Language:	English
Published:	London Springer London 01.10.2021 Springer Nature B.V
Subjects:	Candidates Classification Computer Science Data Mining and Knowledge Discovery Database Management Decomposition Deep learning Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Knowledge Knowledge representation Methods Natural language Neural networks Pattern classification Questions Regular Paper Semantics Entity linking Question answering Entity detection Relation detection Simple questions Knowledge graph
ISSN:	0219-1377, 0219-3116
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Abstract	Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates. Substantial experiments on the SimpleQuestions benchmark dataset show that our proposed method could achieve better performance than many existing state-of-the-art methods on accuracy, top- N recall and other evaluation metrics.
AbstractList	Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates. Substantial experiments on the SimpleQuestions benchmark dataset show that our proposed method could achieve better performance than many existing state-of-the-art methods on accuracy, top- N recall and other evaluation metrics. Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates. Substantial experiments on the SimpleQuestions benchmark dataset show that our proposed method could achieve better performance than many existing state-of-the-art methods on accuracy, top-N recall and other evaluation metrics.
Author	Bao, Tie Feng, Lizhou Cui, Hai Peng, Tao Liu, Lu
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Snippet	Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...
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SubjectTerms	Candidates Classification Computer Science Data Mining and Knowledge Discovery Database Management Decomposition Deep learning Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business Knowledge Knowledge representation Methods Natural language Neural networks Pattern classification Questions Regular Paper Semantics
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Title	Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification
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