Paper Info
Title
FactQA: question answering over domain knowledge graph based on two-level query expansion
Abstract
Purpose With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG. Design/methodology/approach The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user's question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method. Findings The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches. Originality/value Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user's information query based on the emerging KG.
Year
DOI
Venue
2020
10.1108/DTA-02-2019-0029
DATA TECHNOLOGIES AND APPLICATIONS
Keywords
DocType
Volume
DBpedia,Knowledge graph,Question answering,Question expansion,Question matching,Question understanding
Journal
54.0
Issue
ISSN
Citations 
1.0
2514-9288
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaoming Zhang126335.42
Mingming Meng200.34
Xiaoling Sun300.34
Yu Bai400.34