arXiv Open Access 2018

Semantically Enhanced Models for Commonsense Knowledge Acquisition

Ikhlas Alhussien Erik Cambria Zhang NengSheng

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Abstrak

Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding (KGE) framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning.

Topik & Kata Kunci

cs.AI cs.CL

Penulis (3)

Ikhlas Alhussien

Erik Cambria

Zhang NengSheng

Format Sitasi

APA MLA BibTeX

Alhussien, I., Cambria, E., NengSheng, Z. (2018). Semantically Enhanced Models for Commonsense Knowledge Acquisition. https://arxiv.org/abs/1809.04708

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓