Document (#42102)

Author

Tang, X.
Chen, L.
Cui, J.
Wei, B.

Title

Knowledge representation learning with entity descriptions, hierarchical types, and textual relations

Source

Information processing and management. 56(2019) no.3, S.809-822

Year

2019

Abstract

Knowledge representation learning methods usually only utilize triple facts, or just consider one kind of extra information. In this paper, we propose a multi-source knowledge representation learning (MKRL) model, which can combine entity descriptions, hierarchical types, and textual relations with triple facts. Specifically, for entity descriptions, a convolutional neural network is used to get representations. For hierarchical type, weighted hierarchy encoders are used to construct the projection matrixes of hierarchical types, and the projection matrix of an entity combines all hierarchical type projection matrixes of the entity with the relation-specific type constrains. For textual relations, a sentence-level attention mechanism is employed to get representations. We evaluate MKRL model on knowledge graph completion task with dataset FB15k-237, and experimental results demonstrate that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-source information for knowledge representation.

Content

Vgl.: https://doi.org/10.1016/j.ipm.2019.01.005.

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5. Pross, T.: ¬An action-theory-based treatment of temporal individuals (2014) 0.16

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