Consolidating Commonsense Knowledge
This work addresses the need for robust AI systems by integrating disparate commonsense knowledge sources, though it is incremental as it builds on existing sources.
The paper tackles the problem of fragmented commonsense knowledge sources by proposing principles and a representation model to consolidate them into a Common Sense Knowledge Graph (CSKG), resulting in the integration of seven sources and initial utility tests on four QA datasets.
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths, and weaknesses. In this paper, we list representative sources and their properties. Based on this survey, we propose principles and a representation model in order to consolidate them into a Common Sense Knowledge Graph (CSKG). We apply this approach to consolidate seven separate sources into a first integrated CSKG. We present statistics of CSKG, present initial investigations of its utility on four QA datasets, and list learned lessons.