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Erschienen in:
A Joint Embedding Method for Entity Alignment of Knowledge Bases Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Knowledge Base Completion via Rule-Enhanced Relational Learning

verfasst von : Shu Guo, Boyang Ding, Quan Wang, Lihong Wang, Bin Wang

Erschienen in: Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data

Verlag: Springer Singapore

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Abstract

Traditional relational learning techniques perform the knowledge base (KB) completion task based solely on observed facts, ignoring rich domain knowledge that could be extremely useful for inference. In this paper, we encode domain knowledge as simple rules, and propose rule-enhanced relational learning for KB completion. The key idea is to use rules to further refine the inference results given by traditional relational learning techniques, and hence improve the inference accuracy of them. Facts inferred in this way will be the most preferred by relational learning, and at the same time comply with all the rules. Experimental results show that by incorporating the domain knowledge, our approach achieve the best overall performance in the CCKS 2016 competition.

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Vorheriges Kapitel Domain-Specific Entity Discovery and Linking Task
Nächstes Kapitel Knowledge Graph Embedding for Link Prediction and Triplet Classification
Fußnoten
1
If the correct answer is not included in the 200 candidates, we give it a rank of 201.
 
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Metadaten
Titel
Knowledge Base Completion via Rule-Enhanced Relational Learning
verfasst von
Shu Guo
Boyang Ding
Quan Wang
Lihong Wang
Bin Wang
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data

Print ISBN: 978-981-10-3167-0

Electronic ISBN: 978-981-10-3168-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3168-7_22

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