  Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially
different from conventional KGs as they consist of much larger number of nodes
formed by loosely-structured text, which, though, enables them to handle highly
diverse queries in natural language related to commonsense, leads to unique
challenges for automatic KG construction methods. Besides identifying relations
absent from the KG between nodes, such methods are also expected to explore
absent nodes represented by text, in which different real-world things, or
entities, may appear. To deal with the innumerable entities involved with
commonsense in the real world, we introduce to CKG construction methods
conceptualization, i.e., to view entities mentioned in text 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 can effectively identify plausible triples and expand the KG
by triples of both new nodes and edges of high diversity and novelty.
