CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

Gulić, Marko; Vrdoljak, Boris; Banek, Marko

doi:10.1016/j.websem.2016.09.001

Cited by 33 publications

(12 citation statements)

References 17 publications

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“…Although each system is unique, the architecture of all ontology matching systems consists of similar components. In general, the matching process can be divided into three main types of components [1,4]: (1) basic matchers, (2) compositions of basic matchers and aggregation methods for basic matchers' results, and (3) methods for final alignment.…”

Section: Ontology Matching Systemmentioning

confidence: 99%

“…For example, if the value of particular highest correspondence is 0.1, it can be assumed that this highest correspondence is not trustworthy because the similarity between two entities is only 0.1 and none of the values of remaining correspondences, in which one of the compared entities is involved, is not higher than this value. Therefore, we improved this method and proposed Autoweight++ method in [1]. This method does not consider the highest correspondences with lower values into calculation of the aggregation results.…”

Section: Related Workmentioning

confidence: 99%

“…An ontology enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. Therefore, the use of ontologies facilitates the integration of heterogeneous data sources that belong to the same domain [1]. In computer science and information science, ontology is a formal, explicit specification of a shared conceptualization [2].…”

Section: Introductionmentioning

confidence: 99%

“…A basic matcher computes correspondences between entities using information obtained from one or more components of the entire ontology. Consequently, the most common practice is to employ multiple basic matchers in order to utilize all information held within the ontologies [1]. The most common composition for integration of multiple matchers into the matching system is a parallel composition of matchers where basic matchers are executed independently of each other, while the aggregated correspondence for all basic matchers is computed afterwards.…”

Section: Introductionmentioning

confidence: 99%

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Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

2018

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Today, there is a rapid increase of the available data because of advances in information and communications technology. Therefore, many mutually heterogeneous data sources that describe the same domain of interest exist. To facilitate the integration of these heterogeneous data sources, an ontology can be used as it enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. Ontology matching is a key issue in integrating heterogeneous data sources described by ontologies as it eases the management of data coming from various sources. The ontology matching system consists of several basic matchers.To determine high-quality correspondences between entities of compared ontologies, the matching results of these basic matchers should be aggregated by an aggregation method. In this paper, a new weighted aggregation method for parallel composition of basic matchers based on genetic algorithm is presented. The evaluation has confirmed a high quality of the new aggregation method as this method has improved the process of matching two ontologies by obtaining higher confidence values of correctly found correspondences and thus increasing the quality of matching results.

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Section: Ontology Matching Systemmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

2018

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“…YAM++ has recently been extended as YAM-BIO dedicatedly used for biomedical ontology matching using existing mappings as background knowledge. Gulić et al (2016) CroMatcher is an ontology matching framework that conveys different developments to the automated weight estimation process which is used here. They displayed another technique that can create the last balanced arrangement of ontology structures and is a vital change over other non-iterative strategies.…”

Section: Ontology Matchingmentioning

confidence: 99%

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Makwana

Ganatra

2018

Journal of Computer Science

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Knowledge representation is a crucial area of work in the intelligent system, especially in query answering system development. Ontology is used to represent shared knowledge of a particular domain for query answering system. Domain-specific ontology can be designed and developed by many groups and researchers, because of which there is heterogeneity in the knowledgebase. Ontology integration or merging is necessary in order to solve this problem of mixed knowledge. Finding similarity between two ontologies is crucial to achieve integration or merging of ontology. In this study, we present a method to generate a cluster of ontologies using global similarity measure of two ontologies. Ontology matching tools are used to find matched classes between two ontologies. Output of ontology matching tool is mapping between two ontologies and is used for generating clusters of ontology. We use Jaccard Similarity Index as a global similarity measure for clustering. Based on this measure, the popular k-means clustering algorithm is used to perform clustering of ontologies. Bins of ontologies are generated from each cluster. From each bin, all ontologies are finally merged into a single ontology, which helps us in reducing search effort in querying knowledge in query processing. The outcome of this research paper to provide better solution for merging ontology. Here, we use agriculture domain ontology corpus from the standard dataset for experimentation.

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Matching Sensor Ontologies Through Compact Evolutionary Tabu Search Algorithm

Xue

Liu

2018

Security, Privacy, and Anonymity in Computation, Communication, and Storage

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CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

Cited by 33 publications

References 17 publications

Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Matching Sensor Ontologies Through Compact Evolutionary Tabu Search Algorithm

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