Results of the ontology alignment evaluation initiative 2019

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge

Alinhamento de vocabulário de domínio utilizando os sistemas AML e LogMap

Introduction: In the context of the Semantic Web, interoperability among heterogeneous ontologies is a challenge due to several factors, among which semantic ambiguity and redundancy stand out. To overcome these challenges, systems and algorithms are adopted to align different ontologies. In this study, it is understood that controlled vocabularies are a particular form of ontology. Objective: to obtain a vocabulary resulting from the alignment and fusion of the Vocabularies Scientific Domains and Scientific Areas of the Foundation for Science and Technology, - FCT, European Science Vocabulary - EuroSciVoc and United Nations Educational, Scientific and Cultural Organization - UNESCO nomenclature for fields of Science and Technology, in the Computing Sciences domain, to be used in the IViSSEM project. Methodology: literature review on systems/algorithms for ontology alignment, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA methodology; alignment of the three vocabularies; and validation of the resulting vocabulary by means of a Delphi study. Results: we proceeded to analyze the 25 ontology alignment systems and variants that participated in at least one track of the Ontology Alignment Evaluation Initiative competition between 2018 and 2019. From these systems, Agreement Maker Light and Log Map were selected to perform the alignment of the three vocabularies, making a cut to the area of Computer Science. Conclusion: The vocabulary was obtained from Agreement Maker Light for having presented a better performance. At the end, a vocabulary with 98 terms was obtained in the Computer Science domain to be adopted by the IViSSEM project. The alignment resulted from the vocabularies used by FCT (Portugal), with the one adopted by the European Union (EuroSciVoc) and another one from the domain of Science & Technology (UNESCO). This result is beneficial to other universities and projects, as well as to FCT itself.Introdução: No contexto da Web Semântica, a interoperabilidade entre ontologias heterogêneas é um desafio devido a diversos fatores entre os quais se destacam a ambiguidade e a redundância semântica. Para superar tais desafios, adota-se sistemas e algoritmos para alinhamento de diferentes ontologias. Neste estudo, entende-se que vocabulários controlados são uma forma particular de ontologias. Objetivo: obter um vocabulário resultante do alinhamento e fusão dos vocabulários Domínios Científicos e Áreas Científicas da Fundação para Ciência e Tecnologia, - FCT, European Science Vocabulary - EuroSciVoc e Organização das Nações Unidas para a Educação, a Ciência e a Cultura - UNESCO nomenclature for fields of Science and Technology, no domínio Ciências da Computação, para ser usado no âmbito do projeto IViSSEM. Metodologia: revisão da literatura sobre sistemas/algoritmos para alinhamento de ontologias, utilizando a metodologia Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA; alinhamento dos três vocabulários; e validação do vocabulário resultante por meio do estudo Delphi. Resultados: procedeu-se à análise dos 25 sistemas de alinhamento de ontologias e variantes que participaram de pelo menos uma track da competição Ontology Alignment Evaluation Iniciative entre 2018 e 2019. Destes sistemas foram selecionados Agreement Maker Light e LogMap para realizar o alinhamento dos três vocabulários, fazendo um recorte para a área da Ciência da Computação. Conclusão: O vocabulário foi obtido a partir do Agreement Maker Light por ter apresentado uma melhor performance. Ao final foi obtido o vocabulário, com 98 termos, no domínio da Ciência da Computação a ser adotado pelo projeto IViSSEM. O alinhamento resultou dos vocabulários utilizados pela FCT (Portugal), com o adotado pela União Europeia (EuroSciVoc) e outro do domínio da Ciência&Tecnologia (UNESCO). Esse resultado é proveitoso para outras universidades e projetos, bem como para a própria FCT

Contextualized Structural Self-supervised Learning for Ontology Matching

Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources. Recent advancements in deep OM models have harnessed the power of transformer-based language models and the advantages of knowledge graph embedding. Nevertheless, these OM models still face persistent challenges, such as a lack of reference alignments, runtime latency, and unexplored different graph structures within an end-to-end framework. In this study, we introduce a novel self-supervised learning OM framework with input ontologies, called LaKERMap. This framework capitalizes on the contextual and structural information of concepts by integrating implicit knowledge into transformers. Specifically, we aim to capture multiple structural contexts, encompassing both local and global interactions, by employing distinct training objectives. To assess our methods, we utilize the Bio-ML datasets and tasks. The findings from our innovative approach reveal that LaKERMap surpasses state-of-the-art systems in terms of alignment quality and inference time. Our models and codes are available here: https://github.com/ellenzhuwang/lakermap

Results of the ontology alignment evaluation initiative 2020

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign

Proceedings of the 15th ISWC workshop on Ontology Matching (OM 2020)

15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference (ISWC 2020)International audienc

LogMap Family Participation in the OAEI 2023

We present the participation of LogMap and its variants in the OAEI 2023 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system

Results of the Ontology Alignment Evaluation Initiative 2021

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaign

Results of the Ontology Alignment Evaluation Initiative 2021

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaig