  Commonsense knowledge graph completion is a new challenge for commonsense
knowledge graph construction and application. In contrast to factual knowledge
graphs such as Freebase and YAGO, commonsense knowledge graphs (CSKGs; e.g.,
ConceptNet) utilize free-form text to represent named entities, short phrases,
and events as their nodes. Such a loose structure results in large and sparse
CSKGs, which makes the semantic understanding of these nodes more critical for
learning rich commonsense knowledge graph embedding. While current methods
leverage semantic similarities to increase the graph density, the semantic
plausibility of the nodes and their relations are under-explored. Previous
works adopt conceptual abstraction to improve the consistency of modeling
(event) plausibility, but they are not scalable enough and still suffer from
data sparsity. In this paper, we propose to adopt textual entailment to find
implicit entailment relations between CSKG nodes, to effectively densify the
subgraph connecting nodes within the same conceptual class, which indicates a
similar level of plausibility. Each node in CSKG finds its top entailed nodes
using a finetuned transformer over natural language inference (NLI) tasks,
which sufficiently capture textual entailment signals. The entailment relation
between these nodes are further utilized to: 1) build new connections between
source triplets and entailed nodes to densify the sparse CSKGs; 2) enrich the
generalization ability of node representations by comparing the node embeddings
with a contrastive loss. Experiments on two standard CSKGs demonstrate that our
proposed framework EntailE can improve the performance of CSKG completion tasks
under both transductive and inductive settings.
