ECCKG: An Eventuality-Centric Commonsense Knowledge Graph

Abstract

Eventuality-centric knowledge graphs are essential resources for many downstream applications. However, current knowledge graphs mainly focus on knowledge about entities while ignoring the real-world eventualities (including events and states). To fill this gap, we propose to build ECCKG, a high-quality eventuality-centric commonsense knowledge graph. We argue that rule-based methods are of great value for knowledge graph construction, but must be used in conjunction with other techniques such as crowdsourcing. We thus create ECCKG by combining rule-based reasoning with crowdsourcing. We first acquire seed ECCKG by manually filtering out the incorrect and duplicate eventuality-related commonsense assertions in ConceptNet 5.5. Then we enrich the seed ECCKG with a set of logical rules iteratively. Finally, we generate new commonsense assertions by instantiating the existing eventualities. The resulting ECCKG contains more than 1.3 million eventuality-centric commonsense knowledge tuples which is about 15 times larger than ConceptNet 5.5. A manual evaluation shows that ECCKG outperforms other eventuality-centric commonsense knowledge graphs in terms of both quality and quantity. We also demonstrate the usefulness of ECCKG by the extrinsic use case of commonsense knowledge acquisition. ECCKG is available at https://zenodo.org/record/6084081.