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Towards Holistic Concept Representations: Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics


Towards Holistic Concept Representations: Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics



Published: 2017 Oktober

Buchtitel: The Semantic Web – ISWC 2017
Verlag: Springer

Referierte Veröffentlichung

BibTeX

Kurzfassung
KnowledgeGraphs(KGs)effectivelycaptureexplicitrelationalknowl- edge about individual entities. However, visual attributes of those entities, like their shape and color and pragmatic aspects concerning their usage in natural lan- guage are not covered. Recent approaches encode such knowledge by learning la- tent representations (‘embeddings’) separately: In computer vision, visual object features are learned from large image collections and in computational linguistics, word embeddings are extracted from huge text corpora which capture their distri- butional semantics. We investigate the potential of complementing the relational knowledge captured in KG embeddings with knowledge from text documents and images by learning a shared latent representation that integrates information across those modalities. Our empirical results show that a joined concept rep- resentation provides measurable benefits for i) semantic similarity benchmarks, since it shows a higher correlation with the human notion of similarity than uni- or bi-modal representations, and ii) entity-type prediction tasks, since it clearly outperforms plain KG embeddings. These findings encourage further research towards capturing types of knowledge that go beyond today’s KGs.

Download: Media:Towards Holistic Concept Representations.pdf



Forschungsgruppe

Web Science


Forschungsgebiet