Abstract
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.
Original language | English |
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Title of host publication | Semantic Technology |
Subtitle of host publication | 8th Joint International Conference, JIST 2018, Awaji, Japan, November 26–28, 2018, Proceedings |
Editors | Ryutaro Ichise, Freddy Lecue, Takahiro Kawamura, Dongyan Zhao, Stephen Muggleton, Kouji Kozaki |
Place of Publication | Cham |
Publisher | Springer |
Pages | 19-35 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-030-04284-4 |
ISBN (Print) | 978-3-030-04283-7 |
DOIs | |
Publication status | Published - 14 Nov 2018 |
Event | The 8th Joint International Semantic Technology Conference - Awaji City, Japan Duration: 26 Nov 2018 → 28 Nov 2018 http://jist2018.knowledge-graph.jp/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer, Cham |
Volume | 11341 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | The 8th Joint International Semantic Technology Conference |
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Abbreviated title | JIST 2018 |
Country/Territory | Japan |
City | Awaji City |
Period | 26/11/18 → 28/11/18 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Knowledge graph
- Embedding
- Schema aware triple classification
- Knowledge representation and reasoning
- Approximate reasoning
- Artificial Intelligence