Task Oriented Evaluation of Module Extraction Techniques

Palmisano, Ignazio; Tamma, Valentina; Payne, Terry R.; Doran, Paul

doi:10.1007/978-3-642-04930-9_9

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…A significant amount of work on modules and module extraction for ontologies is based on the syntactic structure of ontologies rather than on their interpretation as logical theories. We mention [51,52,53,54] and refer also to the references therein.…”

Section: Related Workmentioning

confidence: 99%

Model-theoretic inseparability and modularity of description logic ontologies

Konev

Lutz

Walther

et al. 2013

Artificial Intelligence

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The aim of this paper is to introduce and study model-theoretic notions of modularity in description logic and related reasoning problems. Our approach is based on a generalisation of logical equivalence that is called modeltheoretic inseparability. Two TBoxes are inseparable w.r.t. a vocabulary Σ if they cannot be distinguished by the Σ-reducts of their models and thus can equivalently be replaced by one another in any application where only vocabulary items from Σ are relevant. We study in-depth the complexity of deciding inseparability for the description logics EL and ALC and their extensions with inverse roles. We then discuss notions of modules of a TBox based on model-theoretic inseparability and develop algorithms for extracting minimal modules from acyclic TBoxes. Finally, we provide an experimental evaluation of our module extraction algorithm based on the large-scale medical TBox Snomed ct.

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Section: Related Workmentioning

confidence: 99%

Model-theoretic inseparability and modularity of description logic ontologies

Konev

Lutz

Walther

et al. 2013

Artificial Intelligence

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“…Palmisano et al [50] provided an evaluation of module extraction approaches from the perspective of their suitability for a specific task. The aim of their method is to identify the suitability of an approach for given task and to detect possible boundary cases where other approaches might be more suitable.…”

Section: Ontology Metricsmentioning

confidence: 99%

Cohesion and coupling metrics for ontology modules

Yeom

Ahn

2011

Inf Technol Manag

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In recent years, an increasing number of ontologies and semantic web applications have been developed and used. A conscious effort has been made to develop methods to modularize ontologies. These methods contribute to building a new ontology. However, few studies have focused on the evaluative methods for ontology modules. In this study, we propose novel metrics to measure ontology modularity. To evaluate the ontology modules, we introduce cohesion and coupling based on the theory of software metrics. A cohesion metric and two coupling metrics were used to measure cohesion and coupling for ontology modules. The proposed metrics provide more detailed support in considering the different types of relationships between classes in ontology modules. In addition, the new coupling metrics contribute to checking the consistency between the ontology modules and their original ontology. The proposed metrics were validated using well-known verification frameworks and empirical experiments to complement the previous investigations. The results of this study offer ontology engineers valuable criteria with which to evaluate ontology modules and help ontology users select qualifying ontology modules.

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“…In particular, two different classes of approaches have been proposed [6]: approaches that exploit traversal-based extraction techniques considering graph-based representations of ontologies [31][32][33][34], and logic-based approaches grounded on description logics semantics [35][36][37]. In this paper we adopted a traversal-based extraction approach because, as discussed in [6], it is more useful when ontologies are not axiomatically rich, and when it is important to have control on the size of the resulting module. In the following we briefly discuss the main differences between the approach introduced here and the other traversalbased approaches w.r.t.…”

Section: Data Integration Systemsmentioning

confidence: 99%

“…To extract the GLSO we provide a new ontology module extraction algorithm, named PaNCH (Parametric Neighborhoods based on Concept Hierarchies), which exploits a traversal-based approach [6]. The algorithm, which takes as input a set of ontology concepts, aims to preserve structural similarity between concepts [7] and to be highly configurable with respect to the size of the extracted module.…”

Section: Construction Of the Glsomentioning

confidence: 99%