Adaptive Similarity Aggregation Method for Ontology Matching

Keikha, Mohammad Mehdi; Nematbakhsh, Mohammad Ali; Ladani, Behrouz Tork

doi:10.1109/ems.2010.71

Cited by 2 publications

References 12 publications

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Hybridizing genetic algorithms and hill climbing for similarity aggregation in ontology matching

Kaymak

Loia

Vitiello

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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Ontology Matching aims at finding correspondences between two different ontologies with overlapping parts in order to bring them into a mutual agreement. The set of correspondences, called alignment, is obtained by computing an aggregated similarity value for all pairs of ontology entities through a weighted approach. Unfortunately, the similarity aggregation task is a very complex optimization process, above all, when no information is known about ontology characteristics. This work presents a hybrid approach which aims at efficiently optimizing the weights for the similarity aggregation task without knowing a priori the ontology features. The effectiveness of our approach is shown by aligning ontologies belonging to the well-known OAEI benchmark dataset and by executing a comparison based on the Wilcoxon's signed rank test which highlights that our proposal statistically outperforms both its genetic counterpart and a traditional no evolutionary approach

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Hybridizing genetic algorithms and hill climbing for similarity aggregation in ontology matching

Kaymak

Loia

Vitiello

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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Data mining and machine learning: an Overview of Classifiers

Haghighi¹

2015

CeN

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At the same time of information age, digital revolution has made necessary using some of technologies to analyze most of essential information. Data mining is a technique to make sense to the available data. The aim of data mining is extracting the information from a vast volume of data and transforming them into a comprehensible form for human. For this purpose, machine learning methods are used to classify data.In this study, we discuss six popular and useful classifiers in the data mining process.

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Adaptive Similarity Aggregation Method for Ontology Matching

Cited by 2 publications

References 12 publications

Hybridizing genetic algorithms and hill climbing for similarity aggregation in ontology matching

Hybridizing genetic algorithms and hill climbing for similarity aggregation in ontology matching

Data mining and machine learning: an Overview of Classifiers

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