18 research outputs found
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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
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
Ontology Matching: OM-2018: Proceedings of the ISWC Workshop
International audienceno abstrac
A gold standard dataset for large knowledge graphs matching
In the last decade, a remarkable number of Knowledge Graphs (KGs) were developed, such as DBpedia, NELL and Google knowledge graph. These KGs are the core of many web-based applications such as query answering and semantic web navigation. The majority of these KGs are semi-automatically constructed, which has resulted in a significant degree of heterogeneity. KGs are highly complementary; thus, mapping them can benefit intelligent applications that require integrating different KGs such as recommendation systems and search engines. Although the problem of ontology matching has been investigated and a significant number of systems have been developed, the challenges of mapping large-scale KGs remain significant. In 2018, OAEI has introduced a specific track for KG matching systems. Nonetheless, a major limitation of the current benchmark is their lack of representation of real-world KGs. In this work we introduce a gold standard dataset for matching the schema of large, automatically constructed, less-well structured KGs based on DBpedia and NELL. We evaluate OAEI's various participating systems on this dataset, and show that matching large-scale and domain independent KGs is a more challenging task. We believe that the dataset which we make public in this work makes the largest domain-independent gold standard dataset for matching KG classes
ATBox results for OAEI 2020
ATBox matcher is a scalable system for instance (Abox) and schema (Tbox) matching.
It uses two pipelines for generating candidates for the schema and instance matching,
and utilizes the schema matches to further improve the instance correspondences.
Using a string blocking method, ATBox is able to align large ontologies and can run on OAEI
tracks like largebio and knowledge graph. The results look promising, but further features
for better finding correct instance matches can be developed
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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
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