Deep Hashing-based Retrieval Framework for KBQA

Question answering over knowledge base usually involves three sub-tasks, topic entity recognition, entity linking and relation detection.Given that the knowledge base usually contains enormous entities and relationships, previous approaches prefer to utilize sophisticated rules and inverted index to...

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Bibliographic Details
Published inJi suan ji ke xue Vol. 50; no. 11; p. 227
Main Authors Liu, Shuo, Zhou, Gang, Li, Zhufeng, Wu, Hao
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.01.2023
Subjects
Information retrieval
Knowledge bases (artificial intelligence)
Modules
Questions
Semantics
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Summary:Question answering over knowledge base usually involves three sub-tasks, topic entity recognition, entity linking and relation detection.Given that the knowledge base usually contains enormous entities and relationships, previous approaches prefer to utilize sophisticated rules and inverted index to retrieve candidate items.In this paper, a new approach is proposed to construct a retrieval framework for question answering over knowledge base to address the problems of search space limitations, low recall and the difficulty to incorporate semantic information demonstrated by previous approach.The framework consists of text retrieve module and hash retrieve module.A cascade retrieve model which contains traditional text retrieve and hash retrieve（semantic information remained） is constructed by recalling twice.The experiment, utilizing the datasets provided by KgCLUE and NLPCC2016,demonstrates that this deep hashing-based retrieve framework can acquire high-quality candidates efficiently and access the knowledg
ISSN:1002-137X