Incorporating Knowledge Graph Embeddings into Topic Modeling

Probabilistic topic models could be used to extract low-dimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not proce...

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Main Authors: Yao, Liang, Zhang, Yin, Wei, Baogang, Jin, Zhe, Zhang, Rui, Zhang, Yangyang, Chen, Qinfei
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Zhang, Yin
Wei, Baogang
Jin, Zhe
Zhang, Rui
Zhang, Yangyang
Chen, Qinfei
description Probabilistic topic models could be used to extract low-dimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence significantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.
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title Incorporating Knowledge Graph Embeddings into Topic Modeling
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