How do we measure and improve the quality of a hierarchical ontology?

Journal of Systems and Software (Impact Factor: 1.35). 12/2011; 84(12):2363-2373. DOI: 10.1016/j.jss.2011.07.010
Source: DBLP


Hierarchical ontologies enable organising information in a human–machine understandable form, but constructing them for reuse and maintainability remains difficult. Often supporting tools available lack formal methodological underpinning and their developers are not supported by any concomitant metrics. The paper presents a formal underpinning to provide quality metrics of a taxonomy hierarchical ontology and proposes a methodology for semi-automatic building of maintainable taxonomies. Users provide terms to be used to describe different ontological elements as well as their attributes and their ranges of values. The methodology uses the formalised metrics to assess the quality of the users input and proposes changes according to given quality constraints. The paper illustrates the metrics and the methodology in constructing and repairing two medium size well-known taxonomies.

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    • "It is challenging to apply a hierarchy of ontologies in developing such applications. For example, concerns remain about how to measure the quality of the ontologies and the alignments among them [18]. This paper proposes an intelligent and supportive software development environment where developers can focus on semantic enrichment of business requirements and proper alignment with business processes rather than the time consuming task of service identification and integration. "
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    Future Generation Computer Systems 03/2014; 32(1):263–273. DOI:10.1016/j.future.2013.08.005 · 2.79 Impact Factor
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    • "In our work, we focus on a particular type of ontology, namely conceptual hierarchy derived from the domain ontology, also known as hierarchical ontology. This kind of ontology is a taxonomy of concepts where concepts are organized based on the partial order relation IS-A, through which entities are grouped into or subsumed by a higher level classes [9] [43]. A conceptual hierarchy can be seen as a simple ontology where the properties of concepts are not taken into account. "
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    Information Sciences 11/2013; 250:40-60. DOI:10.1016/j.ins.2013.07.006 · 4.04 Impact Factor
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    • "At the same time, for many researches in organization ontology, there is limited study has been carried out on the organization goals (Fox et al., 1998; Jimeno-Yepes et al., 2010; Park et al., 2011; Sharma & Osei-Bryson, 2008). Most of the recent studies focused on system ontology (Beydoun et al., 2011; Chandra & Tumanyan, 2007; Jimeno-Yepes et al., 2010) and enterprise ontology (Kang et al., 2010a,b; Park et al., 2011; Sharp et al., 2011; Zacarias et al., 2010). In summary, ontology is important tool to specify the relationship of the knowledge domain within the organization. "
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    ABSTRACT: Organizational goals serve as the most important achievement target in every organization. Even though some researchers have developed the concept of the organization goals, but structuring the organization goals model is always questionable by the way it is being used. In this paper, we propose ontology to develop a unified model for the organization goals structure. We review the recent literature on the organization modelling and ontology development as an effort to evaluate the organization goals using a metrics for the achievement of the organization goals. We suggest that the metrics is important to identify the relevant organization data in relation to the organization goals conformance. In order to achieve this purpose, we investigate various associated concepts and organize the literature based on the organization goals, organization ontology and metrics model. We observe our proposed models are important for domain experts and entrepreneurs to evaluate the relevant organization data and to assist them in decision making. In summary, the contribution of this survey may serve as a first step in understanding the evaluation of the organization data for the achievement of the organization goals.
    Expert Systems with Applications 08/2013; 40(10):4252–4267. DOI:10.1016/j.eswa.2013.01.025 · 2.24 Impact Factor
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