Applying ONTOCOM to DILIGENT

Ontology Engineering is currently advancing from a pure research topic to real applications. This state of the art is emphasized by the wide range of European projects with major industry involvement and, in the same time, by the evergrowing interest of small and medium size enterprizes asking for consultancy in this domain. A core requirement in all of these efforts is, however, the availability of proved and tested methods which allow an efficient engineering of high-quality ontologies, be that by reuse, new building or automatic extraction methods. Several elaborated methodologies, which aid the development of ontologies for particular application requirements, emerged in the last decades. Nevertheless, in order for ontologies to be built and deployed at a large scale, beyond the boundaries of the academic community, one needs not only technologies and tools to assist the engineering process, but also means to estimate and control its overall costs. These issues are addressed only marginally by current engineering approaches though their importance is well recognized in the community. Different approaches exist to estimate costs for engineering processes. We will present the parametric cost estimation model ONTOCOM and its alignment with the DILIGENT engineering methodology. Based on the resulting cost function some analytical evaluations of application scenarios for the DILIGENT model are provided

An Editorial Workflow Approach For Collaborative Ontology Development

The widespread use of ontologies in the last years has raised new challenges for their development and maintenance. Ontology development has transformed from a process normally performed by one ontology engineer into a process performed collaboratively by a team of ontology engineers, who may be geographically distributed and play different roles. For example, editors may propose changes, while authoritative users approve or reject them following a well defined process. This process, however, has only been partially addressed by existing ontology development methods, methodologies, and tool support. Furthermore, in a distributed environment where ontology editors may be working on local copies of the same ontology, strategies should be in place to ensure that changes in one copy are reflected in all of them. In this paper, we propose a workflow-based model for the collaborative development of ontologies in distributed environments and describe the components required to support them. We illustrate our model with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO)

Developing Ontologies withing Decentralized Settings

This chapter addresses two research questions: “How should a well-engineered methodology facilitate the development of ontologies within communities of practice?” and “What methodology should be used?” If ontologies are to be developed by communities then the ontology development life cycle should be better understood within this context. This chapter presents the Melting Point (MP), a proposed new methodology for developing ontologies within decentralised settings. It describes how MP was developed by taking best practices from other methodologies, provides details on recommended steps and recommended processes, and compares MP with alternatives. The methodology presented here is the product of direct first-hand experience and observation of biological communities of practice in which some of the authors have been involved. The Melting Point is a methodology engineered for decentralised communities of practice for which the designers of technology and the users may be the same group. As such, MP provides a potential foundation for the establishment of standard practices for ontology engineering

The Requirements for Ontologies in Medical Data Integration: A Case Study

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

A Semantic Deliberation Model for e-Participation

There have been very few attempts so far to develop a comprehensive and rigorous conceptualization for deliberations in e-participation. Without a rigorous and formal conceptualization of deliberation, consistent content descriptions creation, deliberation records sharing and seamless exploration is difficult. In addition, no e-participation deliberation ontology exists to support citizen-led e-participation particularly when considering contributions made on the social media platforms. This work bridges this gap by providing a rich conceptualization and corresponding formal and executable ontology for deliberation in the context of e-participation. The semantic model covers the core concepts of technology-mediated political discussion and explicitly supports the integrated citizen- and government-led model of e-Participation enabled by social media. Results from the use of the ontology in describing e-Participation deliberation information at Local Government projects are also presented

requirements and use cases

In this report, we introduce our initial vision of the Corporate Semantic Web as the next step in the broad field of Semantic Web research. We identify requirements of the corporate environment and gaps between current approaches to tackle problems facing ontology engineering, semantic collaboration, and semantic search. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. We propose an initial layout for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

A formal ontology for industrial maintenance

International audienceThe rapid advancement of information and communication technologies has resulted in a variety of maintenance support systems and tools covering all sub-domains of maintenance. Most of these systems are based on different models that are sometimes redundant or incoherent and always heterogeneous. This problem has lead to the development of maintenance platforms integrating all of these support systems. The main problem confronted by these integration platforms is to provide semantic interoperability between different applications within the same environment. In this aim, we have developed an ontology for the field of industrial maintenance, adopting the METHONTOLOGY approach to manage the life cycle development of this ontology, that we have called IMAMO (Industrial MAintenance Management Ontology). This ontology can be used not only to ensure semantic interoperability but also to generate new knowledge that supports decision making in the maintenance process. This paper provides and discusses some tests so as to evaluate the ontology and to show how it can ensure semantic interoperability and generate new knowledge within the platform