Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search

Abstract

Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of the exact logic relations in question. Therefore, we propose a framework (abbreviated as NLQSK for title of this paper) that introduces keyword search into natural language question answering to compensate for the defects mentioned above. First, we translate a natural language question into top-k SPARQL statements by using the existing methods. Second, we transform the valuable information that cannot be identified and mapped into keywords, and then, return the neighboring information in a knowledge graph by keyword index. Third, we combine the SPARQL block (i.e., the SPARQL statement and its result) and keyword search to produce the answer to the natural language question. Finally, the experiments on the benchmark dataset confirm that keyword search can compensate for the defects of natural language question answering and that NLQSK can answer more questions than the existing state-of-the-art question answering systems.