An overview of current ontology meta-matching solutions

Martinez‐Gil, Jorge; Aldana‐Montes, José F.

doi:10.1017/s0269888912000288

Cited by 18 publications

(7 citation statements)

References 51 publications

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“…The fundamental notion was that it might optimize the precision, recall, or combination of the two, for example a harmonic mean called f-measure. In [9], a survey on ontology matching was presented with emphasis on the aggregation methods used by the different proposals existing to date, as well as the significant differences between the techniques for matcher combination, matcher self-tuning, and meta-matching.…”

Section: Ontology Matching and Semantic Interoperabilitymentioning

confidence: 99%

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Martinez‐Gil¹,

Yin²,

Küng³

et al. 2022

JDI

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The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research efforts on the development of novel methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model (implemented via genetic programming) that has been specifically trained to find the mathematical expression that can solve the ground truth provided by experts accurately. Moreover, our approach offers the possibility of being understood by a human operator and helping the processor to consume as little energy as possible. The experimental evaluation results that we have achieved using several benchmark datasets seem to show that our approach could be promising.

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Section: Ontology Matching and Semantic Interoperabilitymentioning

confidence: 99%

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Martinez‐Gil¹,

Yin²,

Küng³

et al. 2022

JDI

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“…There exist a number of local optimal solutions in the ontology metamatching problem, and infinite candidate solutions can be found in the search space [9]. One of the main concerns in the existing MOPSO in terms of the ontology metamatching domain is how to enhance population diversity to improve the alignment's quality.…”

Section: Swarm Intelligence Algorithm-based Ontology Matching Techniquementioning

confidence: 99%

“…Since only using a single similarity measure fails to ensure the confidence on all heterogeneous scenarios, various similarity measures are integrated to obtain a satisfactory alignment [8]. Ontology metamatching problem is aimed at how to choose the decent similarity measures, assign appropriate weights for them, and how to verify the alignment by removing the incorrect correspondences to enhance the quality of matching results, which is commonly a complex optimization problem with many local optima [9].…”

Section: Introductionmentioning

confidence: 99%

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

Huang

Zhuang

Xue

2022

Wireless Communications and Mobile Computing

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In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.

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“…• The idea of developing methods to automatically select, combine and/or tune algorithms for the maintenance of a KB is of vital importance [31]. The design of novel approaches that attempt to tune and adapt automatically current solutions to the settings in which an user or application operates are vital for a real automatic maintenance of large expert systems become real.…”

Section: Impact Of Future Challengesmentioning

confidence: 99%

Automated knowledge base management: A survey

Martinez‐Gil¹

2017

Preprint

Self Cite

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A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

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An overview of current ontology meta-matching solutions

Cited by 18 publications

References 51 publications

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

Automated knowledge base management: A survey

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