Triple Classification for Scholarly Knowledge Graph Completion

• URL: http://arxiv.org/abs/2111.11845v1
• Date: Tue, 23 Nov 2021 13:16:31 GMT
• Title: Triple Classification for Scholarly Knowledge Graph Completion
• Authors: Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, S\"oren Auer
• Abstract summary: We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. The evaluation shows that exBERT outperforms other baselines in the tasks of triple classification, link prediction, and relation prediction. We present two scholarly datasets as resources for the research community, collected from public KGs and online resources.
• Score: 1.9322973059079729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources.

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