Ontology Modularization with OAPT
- PDF / 4,478,762 Bytes
- 31 Pages / 595.276 x 790.866 pts Page_size
- 46 Downloads / 204 Views
ORIGINAL ARTICLE
Ontology Modularization with OAPT Alsayed Algergawy1 · Samira Babalou1 · Friederike Klan2 · Birgitta König-Ries1 Received: 1 June 2018 / Revised: 23 December 2019 / Accepted: 8 August 2020 © The Author(s) 2020
Abstract Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT ). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.
1 Introduction Ontologies are the backbone of the Semantic Web, which provides facilities for integrating, searching, and sharing information on the Web by making it understandable for machines [25,26]. According to a study by d’Aquin et al. [10] already in 2007, at least 7000 ontologies existed in the Semantic Web, providing an unprecedented set of resources Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13740-020-00114-7) contains supplementary material, which is available to authorized users.
B
Alsayed Algergawy [email protected] Samira Babalou [email protected] Friederike Klan [email protected] Birgitta König-Ries [email protected]
1
2
Heinz-Nixdorf Chair for Distributed Information Systems, Institute for Computer Science, Friedrich Schiller University of Jena, Jena, Germany Institute of Data Science, German Aerospace Center, Jena, Germany
for developers of semantic applications. However, this large number of available ontologies makes it hard for users to determine which ontologies suitable for their needs. Even, if the user settles on an ontology (or a set of ontologies), she might be interested in a subset of concepts of the ontology, only. For example, if a user plans to use the CHEBI o
Data Loading...
