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

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

A Data-driven Approach to Large Knowledge Graph Matching

In the last decade, a remarkable number of open Knowledge Graphs (KGs) were developed, such as DBpedia, NELL, and YAGO. While some of such KGs are curated via crowdsourcing platforms, others are semi-automatically constructed. This has resulted in a significant degree of semantic heterogeneity and overlapping facts. KGs are highly complementary; thus, mapping them can benefit intelligent applications that require integrating different KGs such as recommendation systems, query answering, and semantic web navigation. 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. KG matching has been a topic of interest in the Semantic Web community since it has been introduced to the Ontology Alignment Evaluation Initiative (OAEI) in 2018. Nonetheless, a major limitation of the current benchmarks is their lack of representation of real-world KGs. This work also highlights a number of limitations with current matching methods, such as: (i) they are highly dependent on string-based similarity measures, and (ii) they are primarily built to handle well-formed ontologies. These features make them unsuitable for large, (semi/fully) automatically constructed KGs with hundreds of classes and millions of instances. Another limitation of current work is the lack of benchmark datasets that represent the challenging task of matching real-world KGs. This work addresses the limitation of the current datasets by first introducing two gold standard datasets for matching the schema of large, automatically constructed, less-well-structured KGs based on common KGs such as NELL, DBpedia, and Wikidata. We believe that the datasets which we make public in this work make the largest domain-independent benchmarks for matching KG classes. As many state-of-the-art methods are not suitable for matching large-scale and cross-domain KGs that often suffer from highly imbalanced class distribution, recent studies have revisited instance-based matching techniques in addressing this task. This is because such large KGs often lack a well-defined structure and descriptive metadata about their classes, but contain numerous class instances. Therefore, inspired by the role of instances in KGs, we propose a hybrid matching approach. Our method composes an instance-based matcher that casts the schema-matching process as a text classification task by exploiting instances of KG classes, and a string-based matcher. Our method is domain-independent and is able to handle KG classes with imbalanced populations. Further, we show that incorporating an instance-based approach with the appropriate data balancing strategy results in significant results in matching large and common KG classes

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

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

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

Evaluating Pre-trained Word Embeddings in domain specific Ontology Matching

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022The ontology matching process focuses on discovering mappings between two concepts from distinct ontologies, a source and a target. It is a fundamental step when trying to integrate heterogeneous data sources that are described in ontologies. This data represents an even more challenging problem since we are working with complex data as biomedical data. Thus, derived from the necessity of keeping on improving ontology matching techniques, this dissertation focused on implementing a new approach to the AML pipeline to calculate similarities between entities from two distinct ontologies. For the implementation of this dissertation, we used some of the OAEI tracks, such as Anatomy and LargeBio, to apply a new algorithm and evaluate if it improves AML’s results against a refer ence alignment. This new approach consisted of using pre-trained word embeddings of five different types, BioWordVec Extrinsic, BioWordVec Intrinsic, PubMed+PC, PubMed+PC+Wikipedia and English Wikipedia. These pre-trained word embeddings use a machine learning technique, Word2Vec, and were used in this work since it allows to carry the semantic meaning inherent to the words represented with the corresponding vector. Word embeddings allowed that each concept of each ontology was represented with a corresponding vector to see if, with that information, it was possible to improve how relations between concepts were determined in the AML system. The similarity between concepts was calculated through the cosine distance and the evaluation of the new alignment used the metrics precision recall and F-measure. Although we could not prove that word embeddings improve AML current results, this implementation could be refined, and the technique can be still an option to consider in future work if applied in some other way

Wiktionary Matcher

In this paper, we introduce Wiktionary Matcher, an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. We show that Wiktionary can be used as external background knowledge source for the task of ontology matching with reasonable matching and runtime performance

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