How do we measure and improve the quality of a hierarchical ontology?
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.
Available from: Lijuan Wang
- "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 . 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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ABSTRACT: This paper argues for placing ontologies at the centre of the software development life cycle for distributed component-based systems and, in particular, for service-oriented systems. It presents an ontology-based development process which relies on three levels of abstraction using ontologies: architecture layer, application layer and domain layer. The paper discusses the key roles of ontologies with respect to the various abstraction layers and their corresponding impact on the concomitant workproducts. In addition, a peer-to-peer-based service selecting and composing tool is suggested as a way of supporting the process. The paper presents the architecture of the proposed tool and illustrates the whole process in the development of a mobile banking application based on dynamic Web services.
Future Generation Computer Systems 03/2014; 32(1):263–273. DOI:10.1016/j.future.2013.08.005 · 2.79 Impact Factor
Available from: Federica Cena
- "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  . A conceptual hierarchy can be seen as a simple ontology where the properties of concepts are not taken into account. "
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ABSTRACT: This work contributes to the development of ontology-based user models, devised as overlays over conceptual hierarchies derived from domain ontologies. We tackle the problem of propagation of user interests in such a conceptual hierarchy. In addition to accounting for the hierarchical structure of the domain and the type and amount of feedback provided by the user, the principal contributions introduced in this work are: (i) horizontal propagation which enables propagation among siblings, in addition to vertical propagation among ancestors and descendants; (ii) anisotropic vertical propagation which permits user interests to be propagated differently upward and downward; (iii) context-dependance which introduces the possibility to propagate differently according to various contexts for specific applications; (iv) support for dynamic ontology maintenance, i.e. preserving the user interest values when adding or removing a node from the conceptual hierarchy. Our approach supports finer recommendation modalities and contributes to the resolution of the cold start problem, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the conceptual hierarchy of the domain. A field evaluation confirmed the effectiveness of our approach w.r.t. the traditional vertical propagation
Information Sciences 11/2013; 250:40-60. DOI:10.1016/j.ins.2013.07.006 · 4.04 Impact Factor
Available from: Tengku Adil Tengku Izhar
- "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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