Blue Sky Ideas

Make Embeddings Semantic Again!

Kiln October 11, 2018 11:15 - 11:30

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Heiko Paulheim.  

Abstract:  In the recent years, vector space embeddings of semantic web knowledge graphs — i.e., projections of a knowledge graph into a lower-dimensional, numerical feature space (a.k.a. latent feature space) — have been shown to yield superior performance in many fields, including relation prediction, recommender systems, or the enrichment of predictive data mining tasks. At the same time, such a representation is as non-semantic as possible, hence, the results are most often not interpretable, and it is hard to obtain a justification for a prediction, e.g., an explanation why an item has been suggested by a recommender system. In this paper, we make a claim for semantic embeddings and discuss a possible (yet unvalidated) approach for their construction.

Keywords:  Knowledge Graphs;  Embeddings;  Representation Learning