Element similarity measures in XML schema matching

Algergawy, Alsayed; Nayak, Richi; Saake, Gunter

doi:10.1016/j.ins.2010.08.022

Cited by 63 publications

(86 citation statements)

References 51 publications

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“…Since our approach focuses on the similarity between RDF Schema elements, we compare our method to similar works such as Leme et al [17], Do et al [18], and Algergawy et al [12]. The precision, recall and F-measure values among R2Sim and related work are presented in Fig.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…We found that other approaches related to measuring the similarity between data types, such as [11,12], often assign the similarity value for each data type pair. In this paper, we propose a novel technique to calculate these values.…”

Section: Data Type Similaritymentioning

confidence: 94%

“…Yang et al [19] use linguistic taxonomy based on entity definitions in WordNet [10] to gain the most accurate semantics for the element names. Some researchers [11,12,17,27], employ supplemental functions to calculate the similarity of a particular feature of a given schema, such as structural similarity, the similarity of leaf nodes or root nodes, data types and constraints. All the partial results are then combined into the final similarity value using a weighted sum function.…”

Section: Related Workmentioning

confidence: 99%

“…Where DSim1 is the data type similarity based on the resemblance of constraining facets; cf is one of the constraining facets described in [6], 12 .. The results of equation (7) are quite acceptable except for some illogical values.…”

Section: Data Type Similaritymentioning

confidence: 99%

“…However, most of these methods only consider the information which describes the entities such as name, definition, and property. Further, the similarity values of some factors such as data type and definition are given by the users' judgment [11,12]. This paper presents a novel method that measures the semantic similarity between entities from different RDF ontologies.…”

Section: Introductionmentioning

confidence: 99%

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An Improvement in Measuring the Semantic Similarity Between RDF Ontologies

Thúy¹,

Tien²

2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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Abstract-RDF (Resource Description Framework) ontologies has been playing an important role for many knowledge applications because they support a source of precisely defined terms. However, the wide-spread of RDF ontologies creates a demand for automatic way of assessing their similarity. In this paper, we present a novel method to measure the semantic similarity between elements in different RDF ontologies. This measure is designed so as to enable extraction of information encoded in RDF element descriptions and to take into account the element relationships with its ancestors and children. We evaluate the proposed measures in the context of matching two RDF ontologies to determine the number of matches between them and then compare with human estimation and the related methods. The experimental results show that our similarity values are better than other approaches with regard to the accuracy of semantics and structure similarities.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Data Type Similaritymentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Data Type Similaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improvement in Measuring the Semantic Similarity Between RDF Ontologies

Thúy¹,

Tien²

2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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Complex Event Processing Methods for Process Querying

Artikis

Weidlich

2021

Process Querying Methods

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Annotation-Based Feature Extraction from Sets of SBML Models

Alm

Waltemath

Wolkenauer

et al. 2014

Lecture Notes in Computer Science

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Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results:In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions:Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

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Element similarity measures in XML schema matching

Cited by 63 publications

References 51 publications

An Improvement in Measuring the Semantic Similarity Between RDF Ontologies

An Improvement in Measuring the Semantic Similarity Between RDF Ontologies

Complex Event Processing Methods for Process Querying

Annotation-Based Feature Extraction from Sets of SBML Models

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