Multi-hop reasoning over paths in knowledge graphs has attracted rising research interest in the field of knowledge graph completion. Entity types and relation types both contain various kinds of information content though only a subset of them are helpful in the specific triples. Although significant progress has been made by existing models, they have two major shortcomings. First, these models seldom learn an explicit representation of entities and relations with semantic information. Second, they reason without discriminating distinct role types that the same entity with multiple types plays in different triples. To address these issues, we develop a novel path-based reasoning with constrained type attention model, which tries to identify entity types by leveraging relation type constraints in the corresponding triples. Our experimental evaluation shows that the proposed model outperforms the state of the art on a real-world dataset. Further analyses also confirm that both word-level and triple-level attention mechanisms of our model are effective.
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This work was financially supported by the National Natural Science Foundation of China (No. 61602013), Natural Science Foundation of Guangdong (No. 2018A030313017) and the Shenzhen Fundamental Research Project (No. JCYJ20170818091546869).
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Lei, K., Zhang, J., Xie, Y. et al. Path-based reasoning with constrained type attention for knowledge graph completion. Neural Comput & Applic 32, 6957–6966 (2020). https://doi.org/10.1007/s00521-019-04181-1
- Neural network
- Knowledge graph completion
- Multi-hop reasoning
- Attention mechanism