TransT: Type-based Multiple Embedding Representations for Knowledge Graph Completion

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Dependency: Armadillo, Boost, MKL

Authors shh
Journal/Conference Name Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Paper Category
Paper Abstract Knowledge graph completion with representation learning predicts new entity-relation triples from the existing knowledge graphs by embedding entities and relations into a vector space. Most existing methods focus on the structured information of triples and maximize the likelihood of them. However, they neglect semantic information con- tained in most knowledge graphs and the prior knowledge indicated by the semantic information. To overcome this drawback, we propose an approach that integrates the structured information and entity types which describe the categories of entities. Our approach constructs rela- tion types from entity types and utilizes type-based semantic similarity of the related entities and relations to capture prior distributions of enti- ties and relations. With the type-based prior distributions, our approach generates multiple embedding representations of each entity in different contexts and estimates the posterior probability of entity and relation prediction. Extensive experiments show that our approach outperforms previous semantics-based methods.
Date of publication 2017
Code Programming Language C++

Copyright shh 2022