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Article overview
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Schema-Guided Event Graph Completion | | Hongwei Wang
; Zixuan Zhang
; Sha Li
; Jiawei Han
; Yizhou Sun
; Hanghang Tong
; Joseph P. Olive
; Heng Ji
; | | Date: |
6 Jun 2022 | | Abstract: | We tackle a new task, event graph completion, which aims to predict missing
event nodes for event graphs. Existing link prediction or graph completion
methods have difficulty dealing with event graphs because they are usually
designed for a single large graph such as a social network or a knowledge
graph, rather than multiple small dynamic event graphs. Moreover, they can only
predict missing edges rather than missing nodes. In this work, we propose to
utilize event schema, a template that describes the stereotypical structure of
event graphs, to address the above issues. Our schema-guided event graph
completion approach first maps an instance event graph to a subgraph of the
schema graph by a heuristic subgraph matching algorithm. Then it predicts
whether a candidate event node in the schema graph should be added to the
instantiated schema subgraph by characterizing two types of local topology of
the schema graph: neighbors of the candidate node and the subgraph, and paths
that connect the candidate node and the subgraph. These two modules are later
combined together for the final prediction. We also propose a self-supervised
strategy to construct training samples, as well as an inference algorithm that
is specifically designed to complete event graphs. Extensive experimental
results on four datasets demonstrate that our proposed method achieves
state-of-the-art performance, with 4.3% to 19.4% absolute F1 gains over the
best baseline method on the four datasets. | | Source: | arXiv, 2206.02921 | | Services: | Forum | Review | PDF | Favorites | |
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