Constructing and maintaining large-scale good quality knowledge graphs present many challenges. Knowledge graph completion has been regarded a promising direction in the knowledge graph community. The majority of current work for knowledge graph completion approaches do not take the schema of a target knowledge graph as input. As a result, the triples generated by these approaches are not necessarily consistent with the schema of the target knowledge graph. This paper proposes to improve the correctness of knowledge graph completion based on Schema Aware Triple Classification (SATC), which enables sequential combinations of knowledge graph embedding approaches. Extensive experiments show that our proposed approaches can significantly improve the correctness of the new triples produced by knowledge graph embedding methods.
|Title of host publication
|Semantic Technology - 8th Joint International Conference, JIST 2018, Proceedings
|Number of pages
|Published - 14 Nov 2018
|8th Joint International Semantic Technology Conference, JIST 2018 - Awaji, Japan
Duration: 26 Nov 2018 → 28 Nov 2018
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|8th Joint International Semantic Technology Conference, JIST 2018
|26/11/18 → 28/11/18
Bibliographical noteThis work was supported by IBM Faculty Award and the EU Marie Currie K-Drive project (286348). Kemas Wiharja was also supported by the Lembaga Pengelola Dana Pendidikan (LPDP), the Ministry of Finance of Indonesia.
- Approximate reasoning
- Artificial Intelligence
- Knowledge graph
- Knowledge representation and reasoning
- Schema aware triple classification