OWL2Vec*: Embedding of OWL Ontologies

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies. In this paper, we propose a language model based ontology embedding method named OWL2Vec*, which encodes the semantics of an ontology by taking into account its graph structure, lexical information and logic constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments

Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision

Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new (industrial) alignment tasks. In this paper we present a machine learning based extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We have used the extension together with traditional systems such as LogMap and AML to align two food ontologies, HeLiS and FoodOn, and we found that the extension recalls many additional valid mappings and also avoids some false positive mappings. This is also verified by an evaluation on alignment tasks from the OAEI conference track

BERTMap: a BERT-based ontology alignment system

Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model BERT on text semantics corpora extracted from ontologies, and then refines the mappings through extension and repair by utilizing the ontology structure and logic. Our evaluation with three alignment tasks on biomedical ontologies demonstrates that BERTMap can often perform better than the leading OM systems LogMap and AML

Biomedical ontology alignment with BERT

Existing machine learning-based ontology alignment systems often adopt complicated feature engineering or traditional non-contextual word embeddings. However, they are often outrun by the rule-based sys- tems despite the model complexity. This paper proposes a novel ontology alignment system based on a contextual embedding model named BERT, aiming to suficiently utilize the text semantics implied by ontologies. Our results on two biomedical alignment tasks demonstrate that, despite us- ing the to-be-aligned classes alone as the input, our system outperforms the leading systems: LogMap and AML

Biomedical ontology alignment with BERT

Existing machine learning-based ontology alignment systems often adopt complicated feature engineering or traditional non-contextual word embeddings. However, they are often outrun by the rule-based sys- tems despite the model complexity. This paper proposes a novel ontology alignment system based on a contextual embedding model named BERT, aiming to suficiently utilize the text semantics implied by ontologies. Our results on two biomedical alignment tasks demonstrate that, despite us- ing the to-be-aligned classes alone as the input, our system outperforms the leading systems: LogMap and AML

Augmenting ontology alignment by semantic embedding and distant supervision

Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new (industrial) alignment tasks. In this paper we present a machine learning based extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We have used the extension together with traditional systems such as LogMap and AML to align two food ontologies, HeLiS and FoodOn, and we found that the extension recalls many additional valid mappings and also avoids some false positive mappings. This is also verified by an evaluation on alignment tasks from the OAEI conference track