S-Match: An open source framework for matching lightweight ontologies

Giunchiglia, Fausto; Autayeu, Aliaksandr; Pane, Juan

doi:10.3233/sw-2011-0036

Cited by 63 publications

(34 citation statements)

References 17 publications

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“…These matching systems are based on the kind of data that are utilized, i.e. (i) strings (terminological or lexical systems), (ii) structure (structural systems), (iii) instances (extensional systems), and (iv) models (semantics methods) [8,31,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Fig.…”

Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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In this review the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources pave the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools support the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues which arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed towards a roadmap in cohort harmonization for clinical medicine.

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Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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“…The goal of the prototype is to have full control over the alignment process to determine the causes that lead to a(n) (in)correct correspondence, and then provide solutions or improvements for the problems found. Several tools enable (partially or fully-) automatic alignment among which we consider the following: H-Match (Castano et al, 2003), S-Match (Giunchiglia et al,2012), TaxoMap (Safar and Reynaud, 2009), Logmap (Jiménez-Ruiz et al, 2012), (Faria et al, 2013) for the complementary types of alignment they provide (Otero-Cerdeira et al, 2015).…”

Section: Ontology Alignmentmentioning

confidence: 99%

Avionics Maintenance Ontology Building for Failure Diagnosis Support

Palacios

Lortal

Laudy

et al. 2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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In the aviation industry, the delay in maintaining or recovering aircrafts heavily impacts the profit of an airline company. Consequently the maintenance actions identification and planning of aircrafts is crucial. However, due to the complexity of the domain in terms of data sources, distributed systems and information availability, it is hard to provide automatic maintenance support. We propose to use semantic technologies to model the domain at a conceptual level through ontology, thus abstracting from the data sources and the maintenancers'uses and jobs. In this manner the information relevant for characterizing failures and maintenance events is encapsulated and provided to end users via an easier access, which otherwise would be inaccessible or would require expert analysis to obtain. Such a formal model of the domain can furthermore enable automated reasoning for maintenance discovery and failure causes detection by integrating a large amount of background contextual information scattering in different resources. In this paper we provide the rationale of the Avionics Maintenance ontology i.e. how we built it through expert knowledge and alignment of different sources and an ontology alignment evaluation tool.

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“…Falcon [13] applies a divide-and-conquer approach to ontology matching. Several other systems, such as DSSim [22], S-Match [11], Anchor-Flood [12], Agreement-Maker [3], ATOM [26] and SAMBO [17] tackle the alignment for ontologies and schemas relying on lexical, structural and semantical similarity measures. In a recent survey, [29] analyze in more details well-established frameworks and outline future directions and challenges in this field.…”

Section: Related Workmentioning

confidence: 99%

Complex Matching of RDF Datatype Properties

Nunes

Mera

Casanova

et al. 2013

Lecture Notes in Computer Science

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Property mapping is a fundamental component of ontology matching, and yet there is little support that goes beyond the identification of single property matches. Real data often requires some degree of composition, trivially exemplified by the mapping of "first name", "last name" to "full name" on one end, to complex matchings, such as parsing and pairing symbol/digit strings to SSN numbers, at the other end of the spectrum. In this paper, we propose a two-phase instance-based technique for complex datatype property matching. Phase 1 computes the estimate mutual information matrix of the property values to (1) find simple, 1:1 matches, and (2) compute a list of possible complex matches. Phase 2 applies genetic programming to the much reduced search space of candidate matches to find complex matches. We conclude with experimental results that illustrate how the technique works. Furthermore, we show that the proposed technique greatly improves results over those obtained if the estimate mutual information matrix or the genetic programming techniques were to be used independently.

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S-Match: An open source framework for matching lightweight ontologies

Cited by 63 publications

References 17 publications

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Avionics Maintenance Ontology Building for Failure Diagnosis Support

Complex Matching of RDF Datatype Properties

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