Ten Challenges for Ontology Matching

Shvaiko, Pavel; Euzenat, Jérôme

doi:10.1007/978-3-540-88873-4_18

Cited by 210 publications

(136 citation statements)

References 63 publications

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“…We borrow the concept of mapping used in ontology alignment works ( [25,23]) to represent correspondences among concepts of two ontologies and among the concepts of the application ontology and the schema descriptors. We use two types of mappings: ontological mappings between two concepts of ontologies and ontology-schema mappings linking a ontological concept to a schema descriptor.…”

Section: Mappingsmentioning

confidence: 99%

A Approach to Clinical Proteomics Data Quality Control and Import

Naubourg

Savonnet

Leclercq

et al. 2011

Information Technology in Bio- And Medical Informatics

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Abstract. Biomedical domain and proteomics in particular are faced with an increasing volume of data. The heterogeneity of data sources implies heterogeneity in the representation and in the content of data. Data may also be incorrect, implicate errors and can compromise the analysis of experiments results. Our approach aims to ensure the initial quality of data during import into an information system dedicated to proteomics. It is based on the joint use of models, which represent the system sources, and ontologies, which are use as mediators between them. The controls, we propose, ensure the validity of values, semantics and data consistency during import process.

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Section: Mappingsmentioning

confidence: 99%

A Approach to Clinical Proteomics Data Quality Control and Import

Naubourg

Savonnet

Leclercq

et al. 2011

Information Technology in Bio- And Medical Informatics

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“…However, as underlined in [13] there are still several challenges to address. In this section we focus on the problems for which we provide a substantial improvement:…”

Section: Limitations Of Current Matching Toolsmentioning

confidence: 99%

“…Despite the progress on this topic, a lot of work still has to be done [13]. A recent study [6] has underlined that current matching tools offer poor support to users for the process of creation, validation and maintenance of the correspondences.…”

Section: Introductionmentioning

confidence: 99%

Save Up to 99% of Your Time in Mapping Validation

Maltese

Giunchiglia

Autayeu

2010

On the Move to Meaningful Internet Systems, OTM 2010

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Abstract. Identifying semantic correspondences between different vocabularies has been recognized as a fundamental step towards achieving interoperability. Several manual and automatic techniques have been recently proposed. Fully manual approaches are very precise, but extremely costly. Conversely, automatic approaches tend to fail when domain specific background knowledge is needed. Consequently, they typically require a manual validation step. Yet, when the number of computed correspondences is very large, the validation phase can be very expensive. In order to reduce the problems above, we propose to compute the minimal set of correspondences, that we call the minimal mapping, which are sufficient to compute all the other ones. We show that by concentrating on such correspondences we can save up to 99% of the manual checks required for validation.

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“…Furthermore, new technologies such as GPS, the Internet and smartphones have made it much easier for skilled volunteers to record species observations, resulting in a deluge of potentially useful but often unstructured data (Silvertown, 2009). Before these data can be analysed to derive new knowledge, they must be made accessible at a single location and integrated: translated into a single, easy to understand and machine-readable format with a well-defined meaning, in such a way that the contained information is preserved (Andelman et al, 2004;Shvaiko and Euzenat, 2008).…”

Section: Introductionmentioning

confidence: 99%

A semantically integrated, user-friendly data model for species observation data

Veen

Reenen

Sluiter

et al. 2012

Ecological Informatics

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Recent decades have seen an increasing importance of large-scale ecological research, driven by increased awareness of the global influence of human activities on the biosphere. Such research requires species observation data covering many years, large areas and a broad range of taxonomic groups. As such data sets often cover small areas, and have been collected using varying methods, they can only be combined in a single analysis if they are made available at the same location and translated into a single format. Over the past decade, catalysed by the growth of the Internet, various technologies for data dissemination and data integration have been developed and applied in projects such as the Global Biodiversity Information Facility, the Knowledge Network for Biocomplexity, BioCASE and the British National Biodiversity Network (NBN). In the Netherlands, data are now made available from the National Database of Flora and Fauna (NDFF), which currently contains approximately 40 million observation records covering a broad variety of species. The NDFF uses a standardised, semantically integrated data model to combine effectively species observation data of various kinds. In this paper, we evaluate this approach and the NDFF data model, by comparison with Darwin Core, Access to Biological Collections Data (ABCD) and the Recorder 2000 model used by the NBN. We conclude that the high degree of standardisation in the NDFF data model has led to somewhat increased cost in data conversion, but also to improved semantic integration and ease-ofuse of species observation data. Together with the relative simplicity, completeness and flexibility of the model, this enables effective reuse of species observations in a user-friendly manner.

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Ten Challenges for Ontology Matching

Cited by 210 publications

References 63 publications

A Approach to Clinical Proteomics Data Quality Control and Import

A Approach to Clinical Proteomics Data Quality Control and Import

Save Up to 99% of Your Time in Mapping Validation

A semantically integrated, user-friendly data model for species observation data

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