32 research outputs found
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
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Results of the Ontology Alignment Evaluation Initiative 2023
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. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign
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
A hybrid approach for large knowledge graphs matching
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
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
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
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Results of the Ontology Alignment Evaluation Initiative 2022
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. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign
Recommended from our members
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
Domain-aware ontology matching
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