Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

doi:10.1109/TKDE.2023.3289949

Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

2023-01-01

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DOI：10.1109/TKDE.2023.3289949

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To ease the process of building Knowledge Graphs (KGs) from scratch, a cost-effective method is required to enrich a KG using the triples extracted from a corpus. However, it is challenging to enrich a KG with newly extracted triples since they contain noisy information. This paper proposes to refine a KG by leveraging information extracted from a corpus. In particular, we first formulate the task of building KGs as two coupled sub-tasks, namely join event extraction and knowledge graph fusion. We then propose a collaborative knowledge graph fusion framework, which is composed of an explorer and a supervisor, to allow the involved two sub-tasks to mutually assist each other in an alternative manner. More concretely, an explorer extracts triples from a corpus supervised by both the ground-truth annotation and the KG provided by the supervisor. Furthermore, a supervisor then evaluates the extracted triples and enriches the KG with those that are highly ranked. To implement this evaluation, we further propose a translated relation alignment scoring mechanism to align and translate the extracted triples to the KG. Experimental results verify that this collaboration can improve both the performance of our sub-tasks, and contribute to high-quality enriched knowledge graphs.

Keywords:

Collaborative learning

joint event extraction

knowledge graph enrichment

knowledge graph fusion

Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

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