MELT - Matching EvaLuation Toolkit

Hertling, Sven; Portisch, Jan; Paulheim, Heiko

doi:10.1007/978-3-030-33220-4_17

Cited by 22 publications

(23 citation statements)

References 10 publications

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“…The charts at the top allow a filtering of these correspondences by different criteria which can also be combined. The code for generating the dashboard is included in the MELT framework [12] to enable system developers to generate their own analyses.…”

Section: Results From Oaei 2019mentioning

confidence: 99%

The Knowledge Graph Track at OAEI

Hertling

Paulheim

2020

The Semantic Web

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The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools. In 2018, we have started the Knowledge Graph track, whose goal is to evaluate the simultaneous matching of entities and schemas of large-scale knowledge graphs. In this paper, we discuss the design of the track and two different strategies of gold standard creation. We analyze results and experiences obtained in first editions of the track, and, by revealing a hidden task, we show that all tools submitted to the track (and probably also to other tracks) suffer from a bias which we name the golden hammer bias.

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Section: Results From Oaei 2019mentioning

confidence: 99%

The Knowledge Graph Track at OAEI

Hertling

Paulheim

2020

The Semantic Web

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“…MELT [29] presents different tools to assess the similarity between the results of the matchers in order to promote different comparative analyses. MELT is a toolkit for ontology meta-matching whose components have been used individually in other researches [30].…”

Section: Related Workmentioning

confidence: 99%

An experimental analysis on evolutionary ontology meta-matching

2021

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Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.

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“…In order to evaluate and compare matching systems, the Matching EvaLuation Toolkit (MELT) [16] has been developed. MELT allows to develop, package, and evaluate various matching systems and is integrated with the existing tooling that is used within the ontology matching community, i.e., it is compatible with SEALS and HOBBIT.…”

Section: Evaluation Runtimementioning

confidence: 99%

Towards Matching of Domain-Specific Schemas Using General-Purpose External Background Knowledge

Portisch

2020

The Semantic Web: ESWC 2020 Satellite Events

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Schema matching is an important and time consuming part within the data integration process. Yet, it is rarely automatized -particularly in the business world. In recent years, the amount of freely available structured knowledge has grown exponentially. Large knowledge graphs such as BabelNet, DBnary (Wiktionary in RDF format), DBpedia, or Wikidata are available. However, these knowledge bases are hardly exploited for automated matching. One exception is the biomedical domain: Here domain-specific background knowledge is broadly available and heavily used with a focus on reusing existing alignments and on exploiting larger, domain-specific mediation ontologies. Nonetheless, outside the life sciences domain such specialized structured resources are rare. In terms of general knowledge, few background knowledge sources are exploited except for WordNet. In this paper, we present our research idea towards further exploiting general-purpose background knowledge within the schema matching process. An overview of the state of the art is given and we outline how our proposed research approach fits in. Potentials and limitations are discussed and we summarize our intermediate findings. Keywords: Data integration • Schema matching • Ontology matching • Background knowledge • Knowledge graphs • Financial services industry Category: Early Stage Ph.D.

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MELT - Matching EvaLuation Toolkit

Cited by 22 publications

References 10 publications

The Knowledge Graph Track at OAEI

The Knowledge Graph Track at OAEI

An experimental analysis on evolutionary ontology meta-matching

Towards Matching of Domain-Specific Schemas Using General-Purpose External Background Knowledge

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