Contextual language models for knowledge graph completion
Published: 2021 November
Herausgeber: Mehwish Alam, Mehdi Ali, Paul Groth, Pascal Hitzler, Jens Lehmann, Heiko Paulheim, Achim Rettinger, Harald Sack, Afshin Sadeghi, Volker Tresp
Buchtitel: Machine Learning with Symbolic Methods and Knowledge Graphs co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)
Verlag: CEUR Workshop Proceedings
Organisation: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)
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 fine-tuning of the GPT-2 model for triple classification 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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