Visible to the public On Integrating Knowledge Graph Embedding into SPARQL Query Processing

TitleOn Integrating Knowledge Graph Embedding into SPARQL Query Processing
Publication TypeConference Paper
Year of Publication2018
AuthorsKang, Hyunjoong, Hong, Sanghyun, Lee, Kookjin, Park, Noseong, Kwon, Soonhyun
Conference Name2018 IEEE International Conference on Web Services (ICWS)
KeywordsApproximation algorithms, Covariance matrices, graph theory, KGE, knowledge graph embedding, Mathematical model, Measurement, Metrics, nearest neighbor search, Nearest Neighbor Searching, nearest neighbour methods, NN search algorithm, optimisation, pubcrawl, query language, query languages, query processing, research problems, search problems, SPARQL query processing, Standards, Time factors
AbstractSPARQL is a standard query language for knowledge graphs (KGs). However, it is hard to find correct answer if KGs are incomplete or incorrect. Knowledge graph embedding (KGE) enables answering queries on such KGs by inferring unknown knowledge and removing incorrect knowledge. Hence, our long-term goal in this line of research is to propose a new framework that integrates KGE and SPARQL, which opens various research problems to be addressed. In this paper, we solve one of the most critical problems, that is, optimizing the performance of nearest neighbor (NN) search. In our evaluations, we demonstrate that the search time of state-of-the-art NN search algorithms is improved by 40% without sacrificing answer accuracy.
Citation Keykang_integrating_2018