Scholarlink

<p>Reasoning to answer complex questions is a difficult task and is receiving research attention in natural language processing today. The purpose of this study is to propose a model, QUERY2RULE, that automatically generates logic rules representing the reasoning process and learn how to find answer entities from logical reasoning rules in the graph embedding space. Specifically, for generating automatic logical reasoning rules, we improved the REBEL model by combining LSTM to create automatic logical reasoning rules for each complex question. To learn how to find answers to logic rules, we used the node2vec algorithm to convert the knowledge graph to the embedding space. We added descriptive information about each entity to the graph for more semantics. Then, the K-D tree technique indexed embedded entities for fast retrieval. Finally, we combined the question embedding and the logical reasoning rule embedding and gave them input to the decoder layer to rank the highest candidate entities as the answer. We tested the proposed model on benchmark datasets such as FreeBaseQA, WebQSP, CWQ, and FB15k-237. Experimental results showed that the QUERY2RULE model achieved better than previous models when comparing classification, link prediction, and multi-hop reasoning tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

QUERY2RULE: automatically generating logical query and reasoning from complex questions over large knowledge graph embedding

  • 期刊论文

  • 发表日期：

    2025年6月21日

查看全文
  • Truong H. V. Phan,
  • Phuc Do

摘要

Reasoning to answer complex questions is a difficult task and is receiving research attention in natural language processing today. The purpose of this study is to propose a model, QUERY2RULE, that automatically generates logic rules representing the reasoning process and learn how to find answer entities from logical reasoning rules in the graph embedding space. Specifically, for generating automatic logical reasoning rules, we improved the REBEL model by combining LSTM to create automatic logical reasoning rules for each complex question. To learn how to find answers to logic rules, we used the node2vec algorithm to convert the knowledge graph to the embedding space. We added descriptive information about each entity to the graph for more semantics. Then, the K-D tree technique indexed embedded entities for fast retrieval. Finally, we combined the question embedding and the logical reasoning rule embedding and gave them input to the decoder layer to rank the highest candidate entities as the answer. We tested the proposed model on benchmark datasets such as FreeBaseQA, WebQSP, CWQ, and FB15k-237. Experimental results showed that the QUERY2RULE model achieved better than previous models when comparing classification, link prediction, and multi-hop reasoning tasks.