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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Vydané v:Knowledge and information systems Ročník 63; číslo 10; s. 2741 - 2761
Hlavní autori: Cui, Hai, Peng, Tao, Feng, Lizhou, Bao, Tie, Liu, Lu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.10.2021
Springer Nature B.V
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-021-01609-w