32 research outputs found

    Contextualized Structural Self-supervised Learning for Ontology Matching

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    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

    A Data-driven Approach to Large Knowledge Graph Matching

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    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

    A hybrid approach for large knowledge graphs matching

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    Matching large and heterogeneous Knowledge Graphs (KGs) has been a challenge in the Semantic Web research community. This work highlights a number of limitations with current matching methods, such as: (1) they are highly dependent on string-based similarity measures, and (2) they are primarily built to handle well-formed ontologies. These features make them unsuitable for large, (semi-) automatically constructed KGs with hundreds of classes and millions of instances. Such KGs share a remarkable number of complementary facts, often described using different vocabulary. Inspired by the role of instances in large-scale KGs, we propose a hybrid matching approach. Our method composes an instance-based matcher that casts the schema matching process as a two-way 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 unbalanced population. Our evaluation on a real-world KG dataset shows that our method obtains the highest recall and F1 over all OAEI 2020 participants

    Alinhamento de vocabulário de domínio utilizando os sistemas AML e LogMap

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    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

    Exploiting general-purpose background knowledge for automated schema matching

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    The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

    Results of the Ontology Alignment Evaluation Initiative 2021

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    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

    Domain-aware ontology matching

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    During the last years, technological advances have created new ways of communication, which have motivated governments, companies and institutions to digitalise the data they have in order to make it accessible and transferable to other people. Despite the millions of digital resources that are currently available, their diversity and heterogeneous knowledge representation make complex the process of exchanging information automatically. Nowadays, the way of tackling this heterogeneity is by applying ontology matching techniques with the aim of finding correspondences between the elements represented in different resources. These approaches work well in some cases, but in scenarios when there are resources from many different areas of expertise (e.g. emergency response) or when the knowledge represented is very specialised (e.g. medical domain), their performance drops because matchers cannot find correspondences or find incorrect ones. In our research, we have focused on tackling these problems by allowing matchers to take advantage of domain-knowledge. Firstly, we present an innovative perspective for dealing with domain-knowledge by considering three different dimensions (specificity - degree of specialisation -, linguistic structure - the role of lexicon and grammar -, and type of knowledge resource - regarding generation methodologies). Secondly, domain-resources are classified according to the combination of these three dimensions. Finally, there are proposed several approaches that exploit each dimension of domain-knowledge for enhancing matchers’ performance. The proposals have been evaluated by matching two of the most used classifications of diseases (ICD-10 and DSM-5), and the results show that matchers considerably improve their performance in terms of f-measure. The research detailed in this thesis can be used as a starting point to delve into the area of domain-knowledge matching. For this reason, we have also included several research lines that can be followed in the future to enhance the proposed approaches
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