Inductive Learning on Commonsense Knowledge Graph Completion
  Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG),
where entities are composed of free-form text. However, most existing CKG
completion methods focus on the setting where all the entities are presented at
training time. Although this setting is standard for conventional KG
completion, it has limitations for CKG completion. At test time, entities in
CKGs can be unseen because they may have unseen text/names and entities may be
disconnected from the training graph, since CKGs are generally very sparse.
Here, we propose to study the inductive learning setting for CKG completion
where unseen entities may present at test time. We develop a novel learning
framework named InductivE. Different from previous approaches, InductiveE
ensures the inductive learning capability by directly computing entity
embeddings from raw entity attributes/text. InductiveE consists of a free-text
encoder, a graph encoder, and a KG completion decoder. Specifically, the
free-text encoder first extracts the textual representation of each entity
based on the pre-trained language model and word embedding. The graph encoder
is a gated relational graph convolutional neural network that learns from a
densified graph for more informative entity representation learning. We develop
a method that densifies CKGs by adding edges among semantic-related entities
and provide more supportive information for unseen entities, leading to better
generalization ability of entity embedding for unseen entities. Finally,
inductiveE employs Conv-TransE as the CKG completion decoder. Experimental
results show that InductiveE significantly outperforms state-of-the-art
baselines in both standard and inductive settings on ATOMIC and ConceptNet
benchmarks. InductivE performs especially well on inductive scenarios where it
achieves above 48% improvement over present methods.
