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GENOMA: GENeric Ontology Matching Architecture

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AI*IA 2015 Advances in Artificial Intelligence (AI*IA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9336))

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Abstract

Even though a few architectures exist to support the difficult ontology matching task, it happens often they are not reconfigurable (or just a little) related to both ontology features and applications needs.

We introduce GENOMA, an architecture supporting development of Ontology Matching (OM) tools with the aims to reuse, possibly, existing modules each of them dealing with a specific task/subtasks of the OM process. In GENOMA flexibility and extendibility are considered mandatory features along with the ability to parallelize and distribute the processing load on different systems. Thanks to a dedicated graphical user interface, GENOMA can be used by expert users, as well as novice, that can validate the resulting architecture.

We highlight as main features of developed architecture:

  • to select, combine and set different parameters

  • to evaluate the matching tool applied to big size ontologies

  • efficiency of the OM tool

  • automatic balancing of the processing load on different systems

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Authors and Affiliations

  1. DII, ART Group, University of Rome, Tor Vergata, Rome, Italy

    Roberto Enea, Maria Teresa Pazienza & Andrea Turbati

Authors
  1. Roberto Enea
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  2. Maria Teresa Pazienza
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  3. Andrea Turbati
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Corresponding author

Correspondence to Andrea Turbati .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria,, Università di Ferrara,, Ferrara, Italy

    Marco Gavanelli

  2. Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy

    Evelina Lamma

  3. Dep di Matem e Informatica, Universita di Ferrara, Ferrara, Italy

    Fabrizio Riguzzi

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Enea, R., Pazienza, M.T., Turbati, A. (2015). GENOMA: GENeric Ontology Matching Architecture. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-24309-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24308-5

  • Online ISBN: 978-3-319-24309-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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