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Conference Paper
On Integrating Knowledge Graph Embedding into SPARQL Query Processing
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- Authors
- Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, Noseong Park, Soonhyun Kwon
- Issue Date
- 2018-07
- Citation
- International Conference on Web Services (ICWS) 2018, pp.371-374
- Language
- English
- Type
- Conference Paper
- Abstract
- SPARQL 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.
- KSP Keywords
- Critical problems, Query Language, SPARQL query processing, Search Algorithm(GSA), Search time, knowledge graph embedding, nearest neighbor(NN), state-of-The-Art