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Empirical Linguistics and Computational Language Modeling (LiMo) (Department of Computational Linguistics of Heidelberg University and Leibniz Institute for the German Language)
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Aug 19, 2019
Kotnis, Bhushan, 2019, "Negative Sampling for Learning Knowledge Graph Embeddings", https://doi.org/10.11588/data/YYULL2, heiDATA, V1
Reimplementation of four KG factorization methods and six negative sampling methods. Abstract Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure o...
Aug 19, 2019
Kotnis, Bhushan, 2019, "KGE Algorithms", https://doi.org/10.11588/data/CSXYSS, heiDATA, V1
An updated method for link prediction that uses a regularization factor that models relation argument types Abstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositories relies on processing the available relation instances. Knowledge repos...
Jul 15, 2019
Nastase, Vivi; Kotnis, Bhushan, 2019, "Abstract graphs, abstract paths, grounded paths for Freebase and NELL", https://doi.org/10.11588/data/AVLFPZ, heiDATA, V1
We describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the...
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