Knowledge Graph Refinement based on Triplet BERT-Networks - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Conference Papers Year : 2022

Knowledge Graph Refinement based on Triplet BERT-Networks


Knowledge graph embedding techniques are widely used for knowledge graph refinement tasks such as graph completion and triple classification. These techniques aim at embedding the entities and relations of a Knowledge Graph (KG) in a low dimensional continuous feature space. This paper adopts a transformer-based triplet network creating an embedding space that clusters the information about an entity or relation in the KG. It creates textual sequences from facts and fine-tunes a triplet network of pre-trained transformer-based language models. It adheres to an evaluation paradigm that relies on an efficient spatial semantic search technique. We show that this evaluation protocol is more adapted to a few-shot setting for the relation prediction task. Our proposed GilBERT method is evaluated on triplet classification and relation prediction tasks on multiple well-known benchmark knowledge graphs such as FB13, WN11, and FB15K. We show that GilBERT achieves better or comparable results to the state-of-theart performance on these two refinement tasks.
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hal-03950704 , version 1 (22-01-2023)




  • HAL Id : hal-03950704 , version 1


Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Saïs, Gianluca Quercini. Knowledge Graph Refinement based on Triplet BERT-Networks. Deep Learning meets Ontologies and Natural Language Processing, May 2022, Hersonissos, Greece. ⟨hal-03950704⟩
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