On the Role of Conceptualization in Commonsense Knowledge Graph Construction
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Commonsense knowledge graphs (CKG) like Atomic and ASER are substantially different from conventional KG as they consist of much larger number of nodes formed by loosely-structured texts, which, though, enable them to handle highly diverse queries in natural language regarding commonsense, lead to unique challenges to automatic KG construction methods. Besides identifying relations absent from the KG between nodes, the methods are also expected to explore absent nodes represented by texts, in which different real-world things or entities may appear. To deal with innumerable entities involved with commonsense in real world, we introduce to CKG construction methods conceptualization, i.e., to view entities mentioned in texts as instances of specific concepts or vice versa. We build synthetic triples by conceptualization, and further formulate the task as triple classification, handled by a discriminatory model with knowledge transferred from pretrained language models and fine-tuned by negative sampling. Experiments demonstrate that our methods could effectively identify plausible triples and expand the KG by triples of both new nodes and edges in high diversity and novelty.
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