Abstract
This paper presents a method for building a large commonsense knowledge graph about attributes of concepts, called CKGAC. CKGAC contains triples that connect concepts (nouns) with attribute values (adjectives) via corresponding attributes like color, taste and age. We first manually construct a seed commonsense knowledge graph for CKGAC based on a limited set of Chinese adjectives. Then we add new triples to this seed commonsense knowledge graph by extracting < concept, adjective> pairs from unstructured Web documents. We further extend the commonsense knowledge graph using isa relations between concepts. Finally, we obtain CKGAC which consists of 4,490,768 high-quality commonsense triples covering 187 concept attributes. Experimental results show the effectiveness of our approach in terms of both precision and coverage through human evaluation. In order to prove the utility of CKGAC, we demonstrate an application of it to commonsense knowledge acquisition. CKGAC is available at https://zenodo.org/record/6084143.
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Wang, Y., Cao, C., Chen, Z., Wang, S. (2022). CKGAC: A Commonsense Knowledge Graph About Attributes of Concepts. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_45
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