Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs
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AbstractRDF 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 work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. 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 a 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 of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
Acceptance Date26/10/2017
All Author(s) ListHu S, Zou L, Yu JX, Wang HX, Zhao DY
Journal nameIEEE Transactions on Knowledge and Data Engineering
Volume Number30
Issue Number5
Pages824 - 837
LanguagesEnglish-United Kingdom
KeywordsRDF,graph database,question answering
Web of Science Subject CategoriesComputer Science, Artificial Intelligence;Computer Science, Information Systems;Engineering, Electrical & Electronic;Computer Science;Engineering

Last updated on 2020-31-05 at 02:01