GAOM: Genetic Algorithm Based Ontology Matching

Wang, Junli; Ding, Zhijun; Jiang, Changjun

doi:10.1109/apscc.2006.59

Cited by 87 publications

(57 citation statements)

References 7 publications

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“…The works described in [9] and [13] present an approach based on genetic algorithms to give an estimate of weights assigned to different strategies used.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Combining the Similarity Values in Ontology Alignment

Benaissa

Khiat

2015

IFIP Advances in Information and Communication Technology

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Abstract. Ontology Alignment is the process of identifying semantic correspondences between their entities. It is proposed to enable semantic interoperability between various knowledge sources that are distributed and heterogeneous. Most existing ontology alignment systems are based on the calculation of similarities and often proceed by their combination. The work presented in this paper consists of an approach denoted PBW (Precision Based Weighting) which estimates the weights to assign to matchers for aggregation. This approach proposes to measure the confidence accorded to a matcher by estimating its precision. The experimental study that we have carried out has been conducted on the Conference 1 track of the evaluation campaign OAEI 2 2012. We have compared our approach with two methods considered as the most performed in recent years, namely those based on the concepts harmony and local confidence trust respectively. The results show the good performance of our approach. Indeed, it is better in terms of precision, than existing methods with which it has been compared.

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“…The works described in [9] and [13] present an approach based on genetic algorithms to give an estimate of weights assigned to different strategies used.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Combining the Similarity Values in Ontology Alignment

Benaissa

Khiat

2015

IFIP Advances in Information and Communication Technology

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“…However, the authors appear to have access to all information in the form of ontology term matching matrices. Other papers, such as [9,17], have used a GA to learn the weight of each matching algorithm for an operator like a weighted sum. Herein, an improved set of constraint preserving GA operators are put forth and more powerful non-linear aggregation operator, the FI, is investigated.…”

Section: Related Work In Ontologiesmentioning

confidence: 99%

Constraints Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching

Boni

Anderson

King

2014

Advance Trends in Soft Computing

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Abstract. Both the fuzzy measure and integral have been widely studied for multi-source information fusion. A number of researchers have proposed optimization techniques to learn a fuzzy measure from training data. In part, this task is difficult as the fuzzy measure can have a large number of free parameters (2 N − 2 for N sources) and it has many (monotonicity) constraints. In this paper, a new genetic algorithm approach to constraint preserving optimization of the fuzzy measure is present for the task of learning and fusing different ontology matching results. Preliminary results are presented to show the stability of the leaning algorithm and its effectiveness compared to existing approaches.

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“…Therefore, not all existing tools are pure meta-matching tools. MaSiMe (Martinez-Gil & Aldana-Montes, 2009), GAOM (Wang et al, 2006), GOAL (Martinez-Gilet al, 2008), eTuner (Lee et al, 2007), APFEL , MatchPlanner (Duchateau et al, 2008), and YAM (Duchateau et al, 2009) could be considered pure tools, while other tools are considered because they implement ontology meta-matching in any of the steps which they follow to solve problems. It should also be taken into account that several tools like Automatch (George Mason University) (Berlin & Motro, 2002), GLUE (University of Washington) (Doan et al, 2003), SemInt (C&C/MITRE Corporation/Oracle) (Li & Clifton, 2000), and Rank Aggregation (Cornell University/Israel Institute of Technology) (Domshlak et al, 2007) can only process classic schemas, and will therefore not be considered in this in this overview.…”

Section: Existing Meta-matching Toolsmentioning

confidence: 99%

An overview of current ontology meta-matching solutions

Martinez‐Gil

Aldana‐Montes

2012

The Knowledge Engineering Review

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Nowadays there are a lot of techniques and tools for addressing the ontology matching problem, however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

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GAOM: Genetic Algorithm Based Ontology Matching

Abstract: Abstract

Cited by 87 publications

References 7 publications

A New Approach for Combining the Similarity Values in Ontology Alignment

A New Approach for Combining the Similarity Values in Ontology Alignment

Constraints Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching

An overview of current ontology meta-matching solutions

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