Towards a Machine of a Process (MOP) ontology to facilitate e-commerce of industrial machinery

Adapting to user's requirements is a key factor for enterprise success. Despite the existence of several approaches that point in this direction, simplifying integration and interoperability among users, suppliers and the enterprise during product lifecycle, is still an open issue. Ontologies have been used in some manufacturing applications and they promise to be a valid approach to model manufacturing resources of enterprises (e.g. machinery and raw material). Nevertheless, in this domain, most of the ontologies have been developed following methodologies based on development from scratch, thus ontologies previously developed have been discarded. Such ontological methodologies tend to hold the interoperability issues in some level. In this paper, a method that integrates ontology reuse with ontology validation and learning is presented. An upper (top-level) ontology for manufacturing was used as a reference to evaluate and to improve specific domain ontology. The evaluation procedure was based on the systemic methodology for ontology learning (SMOL). As a result of the application of SMOL, an ontology entitled Machine of a Process (MOP) was developed. The terminology included in MOP was validated by means of a text mining procedure called Term Frequency–Inverse Document Frequency (TF–IDF) which was carried out on documents from the domain in this study. Competency questions were performed on preexisting domain ontologies and MOP, proving that this new ontology has a performance better than the domain ontologies used as seed.     

ANEMONE: An environment for modular ontology development

Many real-world ontologies contain thousands of terms and are developed by multiple participants. The use of monolithic ontologies can cause problems that affect various stages of the ontology life cycle. Thus, there is an urgent need for tools and methodologies that facilitate modular ontology design. The benefits of a modular approach include division of labor, scalability, partial reuse, and broadened participation. This article presents a methodology for modular ontology development. The main idea is to facilitate an interoperable hierarchical network of ontology modules. Modules are designed as a combination of more abstract modules in higher levels of the hierarchy. This methodology differs from previous methodologies in the way that it defines concrete development steps, to facilitate use by both naive and expert ontology developers. This methodology is also supported by ontology design patterns and a prototypical ontology development tool.     

A high-level electrical energy ontology with weighted attributes

One of the significant application areas of domain ontologies is known to be text analysis applications like information extraction and text classification systems, and semantic portals. In this paper, we present a high-level ontology for the electrical energy domain. This domain ontology has weighted attributes to cover the inherent fuzziness in the textual representations of its concepts. Additionally, we have included in the ontology the necessary attributes to align the ontology concepts to on-line collaborative knowledge bases like Wikipedia and linked open data sources like DBpedia, other attributes to facilitate its use in multilingual applications, and concepts to hold the named entities in the domain. The ultimate ontology is aligned with the previously proposed ontologies for the energy-related subdomains after extending the latter ones with weighted attributes. We make the ultimate form of the electrical energy ontology, as well as the extended versions of the domain ontologies for the subdomains, available for research purposes. Also included in the paper are sample text analysis applications which mainly exploit the weighted attributes within the ontology.     

A software engineering approach to ontology building

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. The availability of large-scale high quality domain ontologies depends on effective and usable methodologies aimed at supporting the crucial process of ontology building. Ontology building exhibits a structural and logical complexity that is comparable to the production of software artefacts. This paper proposes an ontology building methodology that capitalizes the large experience drawn from a widely used standard in software engineering: the Unified Software Development Process or Unified Process (UP). In particular, we propose UP for ONtology (UPON) building, a methodology for ontology building derived from the UP. UPON is presented with the support of a practical example in the eBusiness domain. A comparative evaluation with other methodologies and the results of its adoption in the context of the Athena EU Integrated Project are also discussed.     

Efficient retrieval of ontology fragments using an interval labeling scheme

Nowadays very large domain ontologies are being developed in life-science areas like Biomedicine, Agronomy, Astronomy, etc. Users and applications can benefit enormously from these ontologies in very different tasks, such as visualization, vocabulary homogenizing and data classification. However, due to their large size, they are often unmanageable for these applications. Instead, it is necessary to provide small and useful fragments of these ontologies so that the same tasks can be performed as if the whole ontology is being used. In this work we present a novel method for efficiently indexing and generating ontology fragments according to the user requirements. Moreover, the generated fragments preserve relevant inferences that can be made with the selected symbols in the original ontology. Such a method relies on an interval labeling scheme that efficiently manages the transitive relationships present in the ontologies. Additionally, we provide an interval’s algebra to compute some logical operations over the ontology concepts. We have evaluated the proposed method over several well-known biomedical ontologies. Results show very good performance and scalability, demonstrating the applicability of the proposed method in real scenarios.     

Why evaluate ontology technologies? Because it works!

We deal with two types of ontology evaluation, content evaluation and ontology technology evaluation. Evaluating content is a must for preventing applications from using inconsistent, incorrect, or redundant ontologies. It's unwise to publish an ontology that one or more software applications will use without first evaluating it. A well-evaluated ontology won't guarantee the absence of problems, but it makes its use safer. Similarly, evaluating ontology technology eases its integration with other software environments, ensuring a correct technology transfer from the academic to the industrial world. We also discuss ontology libraries, ontology tool, and formal evaluation of ontology quality.     

Ontologies for conceptual modeling: their creation, use, and management

Although ontologies have been proposed as an important and natural means of representing real world knowledge for the development of database designs, most ontology creation is not carried out systematically. To be truly useful, a repository of ontologies, organized by application domain is needed, along with procedures for creating and integrating ontologies into database design methodologies. This research proposes a methodology for creating and managing domain ontologies. An architecture for an ontology management system is presented and implemented in a prototype. Empirical validation of the prototype demonstrates the effectiveness of the research.     

A comprehensive framework for the evaluation of ontology modularization

The Semantic Web and ontologies have received increased attention in recent years. The delivery of well-designed ontologies enhances the effect of Semantic Web services, but building ontologies from scratch requires considerable time and effort. Modularizing ontologies and integrating ontology modules to a given context help users effectively develop ontologies and revitalize ontology dissemination. Therefore, various tools for modularizing ontologies have been developed. However, selecting an appropriate tool to fit a given context is difficult because the assumptions for the approaches greatly vary. Therefore, a suitable framework is required to compare and help screen the most suitable modularization tool.In this research, we propose a new evaluation framework for selecting an appropriate ontology modularization tool. We present three aspects of tool evaluation as the main dimensions for the assessment of modularization tools: tool performance, data performance, and usability.This study provides an implicit evaluation and an empirical analysis of three modularization tools. It also provides an evaluation method for ontology modularization, enabling ontology engineers to compare different modularization tools and easily choose an appropriate one for the production of qualifying ontology modules.The experimental results indicate that the proposed evaluation criteria for ontology modularization tools are valid and effective. This research provides a useful method for assessing and selecting ontology modularization tools. Modularization performance, data performance, and usability are the three modularization aspects designed and applied to the context of ontology. We provide a new focus on the comprehensive framework to evaluate the performance and usability of ontology modularization tools. The proposed framework should be of value to both ontology engineers, who are interested in ontology modularization, and to practitioners, who need information on how to evaluate and select a specific type of ontology tool in accordance with the requirements of the individual environment.     

Constructing Consensus Ontologies for the Semantic Web: A Conceptual Approach

Organizational knowledge typically comes from numerous independent sources, each with its own semantics. This paper describes a methodology by which information from large numbers of such sources can be associated, organized, and merged. The hypothesis is that a multiplicity of ontology fragments, representing the semantics of the independent sources, can be related to each other automatically without the use of a global ontology. That is, any pair of ontologies can be related indirectly through a semantic bridge consisting of many other previously unrelated ontologies, even when there is no way to determine a direct relationship between them. The relationships among the ontology fragments indicate the relationships among the sources, enabling the source information to be categorized and organized. An evaluation of the methodology has been conducted by relating numerous small, independently developed ontologies for several domains. A nice feature of the methodology is that common parts of the ontologies reinforce each other, while unique parts are deemphasized. The result is a consensus ontology.