Incorporating Knowledge Graph Embeddings into Topic Modeling

By Liang Yao, Baogang Wei, Zhe Jin, Rui Zhang, Yangyang Zhang, Qinfei Chen. 2017

66 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Knowledge Graphs: Opportunities and Challenges (2023, methodology): “These models successfully utilized knowledge graphs in many different domains, including digital libraries (Yao et al. 2017), biomedical (Harnoune et al. 2021), social media (Abu-Salih 2021), and scientific research (Nayyeri et al. 2021).”
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (2018, background): “ther embedded a complicate knowledge graph with the node in a hierarchical structure, like sub-categories, etc. Besides, textual information related to the nodes are incorporated into graph embedding [19, 24, 26, 27]. Moreover, in [5], Chang et al. propose a deep learning framework to simultaneously”