Publication: Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
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Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019, February 15). Interaction Embeddings for Prediction and Explanation in Knowledge Graphs. International Conference on Web Search and Data Mining (WSDM), Melbourne. https://doi.org/10.1145/3289600.3291014
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Knowledge graph embedding aims to learn distributed representations for entities and relations, and are proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but hasn't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as in most
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- Zhang, Wen
- Paudel, Bibek
- Zhang, Wei
- Chen, Huajun
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Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019, February 15). Interaction Embeddings for Prediction and Explanation in Knowledge Graphs. International Conference on Web Search and Data Mining (WSDM), Melbourne. https://doi.org/10.1145/3289600.3291014
