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Dependencies Between Modularity Metrics Towards Improved Modules

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

Recent years have seen many advances in ontology modularisation. This has made it difficult to determine whether a module is actually a good module; it is unclear which metrics should be considered. The few existing works on evaluation metrics focus on only some metrics that suit the modularisation technique, and there is not always a quantitative approach to calculate them. Overall, the metrics are not comprehensive enough to apply to a variety of modules and it is unclear which metrics fare well with particular types of ontology modules. To address this, we create a comprehensive list of module evaluation metrics with quantitative measures. These measures were implemented in the new Tool for Ontology Module Metrics (TOMM) which was then used in a testbed to test these metrics with existing modules. The results obtained, in turn, uncovered which metrics fare well with which module types, i.e., which metrics need to be measured to determine whether a module of some type is a ‘good’ module.

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Notes

  1. 1.

    An earlier version of this section was presented at [14] and has now been updated with some corrections, refinements, and better descriptions.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cape Town, Cape Town, South Africa

    Zubeida Casmod Khan & C. Maria Keet

  2. Council for Scientific and Industrial Research, Pretoria, South Africa

    Zubeida Casmod Khan

Authors
  1. Zubeida Casmod Khan
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  2. C. Maria Keet
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Corresponding author

Correspondence to Zubeida Casmod Khan .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

A Appendix: Summarised Types of Ontology Modules

A Appendix: Summarised Types of Ontology Modules

  1. T1

    Ontology design pattern modules An ontology is modularised by identifying a part of the ontology for general reuse.

  2. T2

    Subject domain modules A large domain is divided by subdomains present in the ontology.

  3. T3

    Isolation branch modules A subset of entities from an ontology is extracted but entities with weak dependencies to the signature are not to be included in the module.

  4. T4

    Locality modules A subset of entities from an ontology is extracted, including all entities that are dependent on the subset.

  5. T5

    Privacy modules Some information is hidden from an ontology.

  6. T6

    Domain coverage modules A large ontology is partitioned by its graphical structure and placement of entities in the taxonomy.

  7. T7

    Ontology matching modules An ontology is modularised for ontology matching into disjoint modules so that there is no repetition of entities.

  8. T8

    Optimal reasoning modules An ontology is split into smaller modules to aid in overall reasoning over the ontology.

  9. T9

    Axiom abstraction modules An ontology is modularised to have fewer axioms, to decrease the horizontal structure of the ontology.

  10. T10

    Entity type abstraction modules An ontology is modularised by removing a certain type of entity e.g., data properties or object properties.

  11. T11

    High-level abstraction modules An ontology is modularised by removing lower-level classes and only keeping higher-level classes.

  12. T12

    Weighted abstraction modules An ontology is modularised by a weighting decided by the developer.

  13. T13

    Expressiveness sub-language modules An ontology is modularised by using a sub-language of a core ontology language.

  14. T14

    Expressiveness feature modules An ontology is modularised by using limited language features.

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Khan, Z.C., Keet, C.M. (2016). Dependencies Between Modularity Metrics Towards Improved Modules. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_26

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