6533b7dbfe1ef96bd127147a
RESEARCH PRODUCT
Structured query construction via knowledge graph embedding
R. WangM. WangJ. LiuM. CochezS. Deckersubject
FOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Computation and LanguageComputer Science - Artificial Intelligenceknowledge graph embeddingnatural language question answeringkyselykieletMachine Learning (cs.LG)luonnollinen kieliArtificial Intelligence (cs.AI)knowledge graphquery constructionComputation and Language (cs.CL)tietomallitdescription
In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |