Comparison of Schema Matching Evaluations

Hai, Hong; Melnik, Sergey; Rahm, Erhard

doi:10.1007/3-540-36560-5_17

Cited by 214 publications

(182 citation statements)

References 10 publications

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“…The F-Score combines precision and recall in one value. We also compute the Overall score, an alternative metric that has specifically been developed for measuring the quality of schema matches [14]. Note that all metrics are based on activity pairs.…”

Section: Discussionmentioning

confidence: 99%

The ICoP Framework: Identification of Correspondences between Process Models

Weidlich

Dijkman

Mendling

2010

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

127

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Abstract. Business process models can be compared, for example, to determine their consistency. Any comparison between process models relies on a mapping that identifies which activity in one model corresponds to which activity in another. Tools that generate such mappings are called matchers. This paper presents the ICoP framework, which can be used to develop such matchers. It consists of an architecture and re-usable matcher components. The framework enables the creation of matchers from the reusable components and, if desired, newly developed components. It focuses on matchers that also detect complex correspondences between groups of activities, where existing matchers focus on 1:1 correspondences. We evaluate the framework by applying it to find matches in process models from practice. We show that the framework can be used to develop matchers in a flexible and adaptable manner and that the resulting matchers can identify a significant number of complex correspondences.

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Section: Discussionmentioning

confidence: 99%

The ICoP Framework: Identification of Correspondences between Process Models

Weidlich

Dijkman

Mendling

2010

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

127

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“…1 Table 3 provides some indicators of the complexity of the test schemas. As match quality measures we have used the following indicators: precision, recall, overall, F-measure (from [3]) and time (from [19]). precision varies in the [0,1] range; the higher the value, the smaller is the set of wrong mappings (false positives) which have been computed.…”

Section: A Comparative Evaluationmentioning

confidence: 99%

S-Match: an Algorithm and an Implementation of Semantic Matching

Giunchiglia

Shvaiko

Yatskevich

2004

Lecture Notes in Computer Science

318

225

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Abstract. We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.

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“…Many times during the experiments we faced situations where several runs of an alignment between two ontologies using the same configuration produced different precision and recall [16] values cause of this phenomenon.…”

Section: Desired Propertiesmentioning

confidence: 99%

“…We used the sample implementations found in the API after performing some slight modifications on them, in order to include all the metrics and exclude from the alignment references to external entities. In order to evaluate each metric we have used the classical measures from the field of information retrieval of precision and recall [16]. Algorithms to compute precision and recall, given a proposed and a correct reference alignment, can also be found in the API.…”

Section: Ontology Alignmentmentioning

confidence: 99%

A String Metric for Ontology Alignment

Stoilos

Stamou

Kollias

2005

Lecture Notes in Computer Science

301

165

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Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using stringdistance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems.

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Comparison of Schema Matching Evaluations

Cited by 214 publications

References 10 publications

The ICoP Framework: Identification of Correspondences between Process Models

The ICoP Framework: Identification of Correspondences between Process Models

S-Match: an Algorithm and an Implementation of Semantic Matching

A String Metric for Ontology Alignment

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