Survey: Models and Prototypes of Schema Matching

Sutanta, Edhy; Wardoyo, Retantyo; Mustofa, Khabib; Winarko, Edi

doi:10.11591/ijece.v6i3.pp1011-1022

Cited by 13 publications

(6 citation statements)

References 69 publications

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“…Current surveys in the ontology matching [14,19,223,238] and schema matching [12,318] domain classify matching systems according to their matching technique (strongly influenced by Euzenat and Shvaiko [74,290] as well as Rahm and Bernstein [265]) with minor or no emphasis at all on the background knowledge used.…”

Section: Introductionmentioning

confidence: 99%

Background knowledge in ontology matching: A survey

Portisch

Hladik

Paulheim

2022

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Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

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Section: Introductionmentioning

confidence: 99%

Background knowledge in ontology matching: A survey

Portisch

Hladik

Paulheim

2022

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“…Schema matching has got a lot of attention from the research community over the past three decades (see [10,18] for surveys). It is very critical for applications that manipulate data across schemas of distinct data sources, examples of areas where this kind of applications are used include mainly data integration on the World Wide Web, data warehousing, e-commerce, scientific collaboration and bioinformatics.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Holistic Schema Matching Approach

Yousfi

Yazidi

Zellou

2021

Communications in Computer and Information Science

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Schema matching allows a certain way of communication between heterogeneous, autonomous and distributed data sources. We choose the matching approach depending on the number of sources we wish to integrate: pairwise matching approaches for a small to a medium total number of data sources, and holistic matching approaches for a big to a huge number of data sources. Nevertheless, current matching approaches were proven to achieve a very moderate matching accuracy. Moreover, holistic matching approaches operate in a series of two-way matching steps. In this paper, we present hMatcher, an efficient holistic schema matching approach. To execute holistic schema matching, hMatcher captures frequent schema elements in the given domain prior to any matching operation. To achieve high matching accuracy, hMatcher uses a context-based semantic similarity measure. Experimental results on real-world domain show that hMatcher performs holistic schema matching properly, and outperforms current matching approaches in terms of matching accuracy.

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“…Based on another previous study [51], we have proposed a hybrid schema matching [52]. Our model is combining constraint-based and instance-based methods [34], [53].…”

Section: Introductionmentioning

confidence: 99%

Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching

Sutanta¹,

Nurnawati²,

Kumalasanti³

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Schema matching plays a vital role in the information integration process from heterogeneous databases. Generally, the process of schema matching is to receive input, which are two databases (one as the source and another as a target), to match similarity attributes, and generate output in the form of mapping the similarity of the attribute pairs that are declared suitable. Furthermore, the user will assess these attribute pairs to determine whether the results obtained are correct or still need to be revised. Our previous study developed a model and software prototype of hybrid schema matching using a combination of constraint-based method and instancebased method. In this study, the model improved by adding new features. This paper discusses the increasing effectiveness of adding the features to customize the weight of matching criteria and string sizes matching. The hybrid model's best effectiveness is obtained when the weight of instance is 0.286, the type is 0.238, width is 0.190, nullable is 0.143, unique is 0.095, and the domain is 0.048. The matching process using a bigger string size increases the model effectiveness with the highest precision of 97.66 when the string size interval is between (length-100) and (length+100). The best combination of weight and string size variation obtains 97.66% precision, a 99.90% recall, and an f-measure of 98.74%.

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Survey: Models and Prototypes of Schema Matching

Cited by 13 publications

References 69 publications

Background knowledge in ontology matching: A survey

Background knowledge in ontology matching: A survey

An Efficient Holistic Schema Matching Approach

Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching

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