Knowledge Graph Inference using Tensor Embedding
Links to Fileshttps://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding
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Type of Work2 pages
conference paper and proceedings postprints
Citation of Original PublicationAnkur Padia; Kalpakis, Kostantinos; Ferraro, Francis; Finin, Tim; Knowledge Graph Inference using Tensor Embedding; 17th International Conference on Principles of Knowledge Representation and Reasoning (2020); https://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding
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Axiom based inference provides a clear and consistent way of reasoning to add more information to a knowledge graph. However, constructing a set of axioms is expensive and requires domain expertise, time, and money. It is also difficult to reuse or adapt a set of axioms to a knowledge graph in a new domain or even in the same domain but using a slightly different representation approach. This work makes three main contributions, it (1) provides a family of representation learning algorithms and an extensive analysis on eight datasets; (2) yields better results than existing tensor and neural models; and (3) includes a provably convergent factorization algorithm.