A Clustering-Based Approach for Large-Scale Ontology Matching

Algergawy, Alsayed; Maßmann, Sabine; Rahm, Erhard

doi:10.1007/978-3-642-23737-9_30

Cited by 45 publications

(30 citation statements)

References 15 publications

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“…We have also validated that the mapping accuracy of GEM is significantly higher (by about 15% for each of precision and recall) than that achieved by two (generic) schema-matching software tools with the same datasets. These other tools are Harmony 24 and Coma++ 1 . The GEM system was designed with a focus on the Alzheimer's disease and other medical domain, and is able to maximally leverage information in the data documentation that other generic matching tools cannot.…”

Section: Resultsmentioning

confidence: 99%

Global Data Sharing in Alzheimer Disease Research

Ashish

Bhatt

Toga

2016

Alzheimer Disease &Amp; Associated Disorders

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Many investigators recognize the importance of data sharing, however they lack the capability to share data. Research efforts could be vastly expanded if Alzheimer's disease data from around the world was linked by a global infrastructure that would enable scientists to access and utilize a secure network of data with thousands of study participants at risk for or already suffering from the disease. We discuss the benefits of data sharing, impediments today and solutions to achieving this on a global scale. We introduce the Global Alzheimer's Association Interactive Network (GAAIN), a novel approach to create a global network of Alzheimer's disease data, researchers, analytical tools and computational resources to better our understanding of this debilitating condition. GAAIN has addressed the key impediments to Alzheimer's disease data sharing with its model and approach. It presents practical, promising, yet data owner sensitive data sharing solutions.

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Section: Resultsmentioning

confidence: 99%

Global Data Sharing in Alzheimer Disease Research

Ashish

Bhatt

Toga

2016

Alzheimer Disease &Amp; Associated Disorders

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“…The system provides one-to-many mappings between single concepts. In [7], the author proposed a clustering approach based on structural nodes similarity. Therefore, each cluster of the source ontology has to be aligned with only one subset of the target ontology.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Ontology Knowledge Mining for Ontology Alignment

Idoudi¹,

Ettabaa²,

Solaiman³

et al. 2016

IJCIS

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As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system.

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“…Domain ontologies that use the same foundation ontology to provide a set of basic elements can be merged automatically. There are studies on generalized techniques for merging ontologies or ontology matching [36], but this area of research is still largely theoretical. In addition, ontologies have become common on the World Wide Web.…”

Section: Introductionmentioning

confidence: 99%