Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs (Extended Abstract)

RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing works focus on question understanding to deal with the disambiguation of phrases linking, which ignore the query composition a...

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Bibliographic Details
Published in2018 IEEE 34th International Conference on Data Engineering (ICDE) pp. 1815 - 1816
Main Authors Sen Hu, Lei Zou, Yu, Jeffery Xu, Haixun Wang, Dongyan Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Graph database
Knowledge based systems
Knowledge engineering
Motion pictures
Natural languages
Query processing
Question Answering
RDF
Resource description framework
Semantics
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Summary:RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing works focus on question understanding to deal with the disambiguation of phrases linking, which ignore the query composition and execution. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose the (super) semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity both of phrases and structures at the time when matches of query are found. To build the super semantic query graph, we propose a node-first framework which has high robustness and can tackle with complex questions. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
ISSN:2375-026X
DOI:10.1109/ICDE.2018.00265