Optimizing Ontology Alignments by Using Genetic Algorithms

Martinez‐Gil, Jorge; Alba, Enrique; Montes, José Francisco Aldana

doi:10.31219/osf.io/ngjc2

Cited by 33 publications

(21 citation statements)

References 34 publications

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“…Another approach in which the system optimally adjusts the weighted factors of basic matchers based on already known results is presented in [36]. This approach has the same problem of ontology uniqueness as the approach described above [35].…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

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

2018

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“…Due to the complex and time-consuming nature of the ontology matching process, EA-based methods could present a good methodology for obtaining ontology alignments and indeed have already been applied to solve the ontology alignment problem by reaching acceptable results [ 10 ]. Different from other EA based approaches [ 11 – 13 ] which models the ontology alignment process as a meta-matching problem, i.e. how to determine the best appropriate weight configuration in ontology matching process in order to obtain a satisfactory alignment, in this work, ontology matching problem is considered as a global entity matching problem.…”

Section: Related Workmentioning

confidence: 99%

Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

Xue

Chen

et al. 2018

Computational Intelligence and Neuroscience

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Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge memory consumption, long runtime, and the bias improvement of the solutions hamper them from efficiently matching biomedical ontologies. To overcome these shortcomings, we propose a compact CoEvolutionary Algorithm to efficiently match the biomedical ontologies. Particularly, a compact EA with local search strategy is able to save the memory consumption and runtime, and three subswarms with different optimal objectives can help one another to avoid the solution's bias improvement. In the experiment, two famous testing cases provided by Ontology Alignment Evaluation Initiative (OAEI 2017), i.e. anatomy track and large biomed track, are utilized to test our approach's performance. The experimental results show the effectiveness of our proposal.

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“…In semantic web ontology creation, management, alignment and integration are the few challenging task. Martinez-Gil et al (2008) proposed Genetic algorithm based approach for alignment of ontology (GOAL). This approach was able to calculate the optimal ontology alignment function for a given input.…”

Section: Genetic Algorithms and Semantic Webmentioning

confidence: 99%

Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

Chaudhary¹,

Singh²,

Kothari³

2018

Journal of Computer Science

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Nature has always inspired us all the waggle dance of Honey bee, the school of whales and the swarm of ants, each element when observed carefully has the abundance of teachings. If we carefully observe nature, we find that although Nature seems to be very simple and systematic, it hides many complexities underneath it. As technology also follows the same principle of 'simple-yet-complex', the researchers have always tried to apply the learning from Nature to complex technological Algorithms used to solve few real life human problems. Since the past decade, there has been a rapid increase of research in this field. Today Nature Inspired algorithms have permeated into almost all areas of sciences. Although it had been applied to various areas of sciences, the scope of this paper is limited to its application in the domain of The Semantic Web. The main objective of Semantic web applications is to obtain, manage and utilize the huge amount of information that is available in either structured semistructured or unstructured databases in distributed environment. This is an emerging domain and is advancing towards more and more intelligent and human oriented applications. This paper presents a survey of vital nature-Inspired techniques that can be used for optimizing various areas of Semantic web applications such as knowledge base, content filtering, Information Retrieval and Inference mechanism.

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Optimizing Ontology Alignments by Using Genetic Algorithms

Cited by 33 publications

References 34 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

Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies

Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

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