Contextual Language Models for Knowledge Graph Completion

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Date
2021
Volume
2997
Issue
Journal
Series Titel
Book Title
Publisher
Aachen, Germany : RWTH Aachen
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Abstract

Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the _ne-tuning of the GPT-2 model for triple classi_cation strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed.

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Keywords
GPT-2, Knowledge Graph Embedding, Triple Classification, Konferenzschrift
Citation
Russa, B., Sofronova, R., Alam, M., & Sack, H. (2021). Contextual Language Models for Knowledge Graph Completion (A. Mehwish, M. Ali, P. Groth, P. Hitzler, J. Lehmann, H. Paulheim, et al., eds.). Aachen, Germany : RWTH Aachen.
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License
CC BY 4.0 Unported