Papers › Contextual Semantic Embeddings for Ontology Subsumption Prediction

Contextual Semantic Embeddings for Ontology Subsumption Prediction

20 Feb 2022arXiv:2202.09791archive 2025-07-28

Jiaoyan Chen, Yuan He, Yuxia Geng, Ernesto Jimenez-Ruiz, Hang Dong, Ian Horrocks

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques such as contextual semantic embedding is a promising direction, but the relevant research is still preliminary especially for expressive ontologies in Web Ontology Language (OWL). In this paper, we present a new subsumption prediction method named BERTSubs for classes of OWL ontology. It exploits the pre-trained language model BERT to compute contextual embeddings of a class, where customized templates are proposed to incorporate the class context (e.g., neighbouring classes) and the logical existential restriction. BERTSubs is able to predict multiple kinds of subsumers including named classes from the same ontology or another ontology, and existential restrictions from the same ontology. Extensive evaluation on five real-world ontologies for three different subsumption tasks has shown the effectiveness of the templates and that BERTSubs can dramatically outperform the baselines that use (literal-aware) knowledge graph embeddings, non-contextual word embeddings and the state-of-the-art OWL ontology embeddings.

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KRR-Oxford/DeepOnto officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

gitlab.com/chen00217/bert_subsumption officialmentioned in paperpytorch report

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Knowledge Graph Embeddings Language Modeling Language Modelling Prediction Word Embeddings

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Ontology Residual Connection Softmax Weight Decay WordPiece

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