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

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

Breaking rules: taking Complex Ontology Alignment beyond rule­based approaches

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2021As ontologies are developed in an uncoordinated manner, differences in scope and design compromise interoperability. Ontology matching is critical to address this semantic heterogeneity problem, as it finds correspondences that enable integrating data across the Semantic Web. One of the biggest challenges in this field is that ontology schemas often differ conceptually, and therefore reconciling many real¬world ontology pairs (e.g., in geography or biomedicine) involves establishing complex mappings that contain multiple entities from each ontology. Yet, for the most part, ontology matching algorithms are restricted to finding simple equivalence mappings between ontology entities. This work presents novel algorithms for Complex Ontology Alignment based on Association Rule Mining over a set of shared instances between two ontologies. Its strategy relies on a targeted search for known complex patterns in instance and schema data, reducing the search space. This allows the application of semantic¬based filtering algorithms tailored to each kind of pattern, to select and refine the most relevant mappings. The algorithms were evaluated in OAEI Complex track datasets under two automated approaches: OAEI’s entity¬based approach and a novel element¬overlap–based approach which was developed in the context of this work. The algorithms were able to find mappings spanning eight distinct complex patterns, as well as combinations of patterns through disjunction and conjunction. They were able to efficiently reduce the search space and showed competitive performance results comparing to the State of the Art of complex alignment systems. As for the comparative analysis of evaluation methodologies, the proposed element¬overlap–based evaluation strategy was shown to be more accurate and interpretable than the reference-based automatic alternative, although none of the existing strategies fully address the challenges discussed in the literature. For future work, it would be interesting to extend the algorithms to cover more complex patterns and combine them with lexical approaches

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

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

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

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

Matching Biomedical Knowledge Graphs with Neural Embeddings

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Os grafos de conhecimento são estruturas que se tornaram fundamentais para a organização dos dados biomédicos que têm sido produzidos a um ritmo exponencial nos últimos anos. A abrangente adoção desta forma de estruturar e descrever dados levou ao desenvolvimento de abordagens de prospeção de dados que tirassem partido desta informação com o intuito de auxiliar o progresso do conhecimento científico. Porém, devido à impossibilidade de isolamento de domínios de conhecimento e à idiossincrasia humana, grafos de conhecimento construídos por diferentes indivíduos contêm muitas vezes conceitos equivalentes descritos de forma diferente, dificultando uma análise integrada de dados de diferentes grafos de conhecimento. Vários sistemas de alinhamento de grafos de conhecimento têm focado a resolução deste desafio. Contudo, o desempenho destes sistemas no alinhamento de grafos de conhecimento biomédicos estagnou nos últimos quatro anos com algoritmos e recursos externos bastante trabalhados para aprimorar os resultados. Nesta dissertação, apresentamos duas novas abordagens de alinhamento de grafos de conhecimento empregando Neural Embeddings: uma utilizando semelhança simples entre embeddings à base de palavras e de entidades de grafos; outra treinando um modelo mais complexo que refinasse a informação proveniente de embeddings baseados em palavras. A metodologia proposta visa integrar estas abordagens no processo regular de alinhamento, utilizando como infraestrutura o sistema AgreementMakerLight. Estas novas componentes permitem extender os algoritmos de alinhamento do sistema, descobrindo novos mapeamentos, e criar uma abordagem de alinhamento mais generalizável e menos dependente de ontologias biomédicas externas. Esta nova metodologia foi avaliada em três casos de teste de alinhamento de ontologias biomédicas, provenientes da Ontology Alignment Evaluation Initiative. Os resultados demonstraram que apesar de ambas as abordagens não excederem o estado da arte, estas obtiveram um desempenho benéfico nas tarefas de alinhamento, superando a performance de todos os sistemas que não usam ontologias externas e inclusive alguns que tiram proveito das mesmas, o que demonstra o valor das técnicas de Neural Embeddings na tarefa de alinhamento de grafos do conhecimento biomédicos.Knowledge graphs are data structures which became essential to organize biomedical data produced at an exponential rate in the last few years. The broad adoption of this method of structuring and describing data resulted in the increased interest to develop data mining approaches which took advantage of these information structures in order to improve scientific knowledge. However, due to human idiosyncrasy and also the impossibility to isolate knowledge domains in separate pieces, knowledge graphs constructed by different individuals often contain equivalent concepts described differently. This obstructs the path to an integrated analysis of data described by multiple knowledge graphs. Multiple knowledge graph matching systems have been developed to address this challenge. Nevertheless, the performance of these systems has stagnated in the last four years, despite the fact that they were provided with highly tailored algorithms and external resources to tackle this task. In this dissertation, we present two novel knowledge graph matching approaches employing neural embeddings: one using plain embedding similarity based on word and graph models; the other one using a more complex word-based model which requires training data to refine embeddings. The proposed methodology aims to integrate these approaches in the regular matching process, using the AgreementMakerLight system as a foundation. These new components enable the extension of the system’s current matching algorithms, discovering new mappings, and developing a more generalizable and less dependent on external biomedical ontologies matching procedure. This new methodology was evaluated on three biomedical ontology matching test cases provided by the Ontology Alignment Evaluation Initiative. The results showed that despite both embedding approaches don’t exceed state of the art results, they still produce better results than any other matching systems which do not make use of external ontologies and also surpass some that do benefit from them. This shows that Neural Embeddings are a valuable technique to tackle the challenge of biomedical knowledge graph matching

LEAPME: learning-based property matching with embeddings

Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. We also show that our approach is even effective when training data from another domain (transfer learning) is used.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2019-105471RB-I00Junta de Andalucía P18-RT-106