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.

翻译：常识知识对于建立智能系统至关重要。 通常,常识知识被描述为隐含和模糊,从而阻碍其获取的自动化。为应对这些挑战,本文件提出经精密强化的模型,以便通过解决部分常识模糊性来进行推理。拟议模型在知识图嵌入(KGE)框架内强化知识基础完成框架。实验结果显示新语义模型在常识推理中的有效性。