Leveraging Knowledge Graph Embeddings for Natural Language Question Answering

• Published in: Database Systems for Advanced Applications Vol. 11446; pp. 659 - 675
• Main Authors: Wang, Ruijie, Wang, Meng, Liu, Jun, Chen, Weitong, Cochez, Michael, Decker, Stefan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Knowledge graph; Knowledge graph embedding; Natural language question answering
• ISBN: 9783030185756; 3030185753
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-18576-3_39

A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.