Sa Xu
ABSTRACT
Q&A system plays an increasingly important role in the modern society with information explosion, and knowledge reasoning model (KRM) is the main research content of Q&A system. Existing knowledge reasoning models are mainly divided into text-based information retrieval (IR) and knowledge graph embedding (KGE). KGE is superior to IR in terms of storage and reasoning capabilities for massive factoid information, but lacks the ability to reason non-factoid information, merely focus on the mining of structural information without the semantic information. We proposed a knowledge reasoning model based on non-factoid information enhancement (NFE-KRM) in scenic Q&A. It realizes the KGE integrates semantic information (SIKGE) and the unified semantic embedding space (USES), so that NFE-KRM has the ability to answer both factoid and non-factoid questions. We have used a large number of experiments to prove that SIKGE gets a better performance on Mean Rank and Hits@10. NFE-KRM's F1 score and accuracy on the mixed dataset are both competitive.
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Index Terms
- A Knowledge Reasoning Model Based on Non-Factoid Information Enhancement
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