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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Organization Unit
Authors
  • Wen Zhang
  • Bibek Paudel
  • Wei Zhang
  • Abraham Bernstein
  • Huajun Chen
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Event Title International Conference on Web Search and Data Mining (WSDM)
Event Type conference
Event Location Melbourne
Event Start Date February 11 - 2019
Event End Date February 15 - 2019
Place of Publication New York, NY
Publisher Association of Computing Machinery (ACM)
Abstract Text 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 previous methods, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate the embeddings on typical link prediction task and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate the embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, explanations for a triple are regarded as reliable closed-paths between head and tail entity. Compared to other baselines, we show experimentally that CrossE is more capable of generating reliable explanations to support its predictions, benefiting from interaction embeddings.
Digital Object Identifier 10.1145/3289600.3291014
Other Identification Number merlin-id:16969
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