Collaborative ontology engineering: a survey <s> Collaboration process <s> Purpose – Aims to present the ontology engineering methodology DILIGENT, a methodology focussing on the evolution of ontologies instead of the initial design, thus recognizing that knowledge is a tangible and moving target. Design/methodology/approach – First describes the methodology as a whole, then detailing one of the five main steps of DILIGENT. The second part describes case studies, either already performed or planned, and what we learned (or expect to learn) from them. Findings – With the case studies it was discovered the strengths and weaknesses of DILIGENT. During the evolution of ontologies, arguments need to be exchanged about the suggested changes. Identifies those kind of arguments which work best for the discussion of ontology changes. Research implications – DILIGENT recognizes ontology engineering methodologies like OnToKnowledge or Methontology as proven useful for the initial design, but expands them with its strong focus on the user-centric further development of the ontology and the provided integration of automatic agents in the process of ontology evolution. Practical implications – With DILIGENT the experience distilled from a number of case studies and this offers the knowledge manager a methodology to work in an ever-changing environment. Originality/value – DILIGENT is the first methodology to put focus not on the initial development of the ontology, but on the user and his usage of the ontology, and on the changes introduced by the user. We take the user’s own view seriously and enable feedback towards the evolution of the ontology, stressing the ontology’s role as a shared conceptualisation. <s> BIB001 </s> Collaborative ontology engineering: a survey <s> Collaboration process <s> Most of the current methodologies for building ontologies rely on specialized knowledge engineers. This is in contrast to real-world settings, where the need for maintenance of domain specific ontologies emerges in the daily work of users. But in order to allow for participatory ontology engineering, we need to have a more realistic conceptual model of how ontologies develop in the real world. We introduce the ontology maturing processes which is based on the insight that ontology engineering is a collaborative informal learning process and for which we analyze characteristic evolution steps and triggers that have users engage in ontology engineering within their everyday work processes. This model integrates tagging and folksonomies with formal ontologies and shows maturing pathways between them. As implementations of this model, we present two case studies and the corresponding tools. The first is about image-based ontology engineering (introducing so-called imagenotions), the second about ontology-enabled social bookmarking (SOBOLEO). Both of them are inspired by lightweight Web 2.0 approaches and allow for realtime collaboration. <s> BIB002
A common characteristic of early ontology engineering methodologies has been the notion of a predefined process model that guides the ontology engineering activities and yields a dedicated set of roles, policies and tools to perform it in a consistent fashion-examples, can be found, for instance in  ,  and BIB001 . With increasing adoption of collaborative ontology engineering principles and practices, the trend moved toward greater flexibility for the engineering team in defining their own models to optimize the results of the project, and providing the tools to support this flexibility BIB002 .  provide several examples of such projects, which have been publishing and refining their workflows for years, including the Gene Ontology project discussed above. In other cases, knowledge and ontology engineers have been actively working on how to formalize the process models they follow. Given proper tool support, the availability of such formal models promise to further enhance the flexibility of the underlying approach by allowing changes to be made during the (now adaptable) ontology development life cycle. An instance thereof is the NeOn project 9 developing an ontology for the United Nations Food and Agriculture Organization (UN FAO). Factors to be taken into account in collaborative engineering scenarios are the organizational structure underlying the project, the size and the openness of the community of contributors, the required level of rigor in quality control and the complexity of the representation  . At the same time, a proper balance must be struck between the formal representation of the process model and the added value of this representation as per automatic use in the project. VoCamps 10 denote informal bar-camp-style events organized by the Semantic Web community, in which a group of stakeholders and enthusiasts (usually around 20) meet at a physical location to develop lightweight vocabularies capturing domains of interests that are proposed democratically by the participants. From a methodological point of view, the VoCamp approach is the most unstructured from all examples presented in this survey. It does not introduce any process model, neither for the ontology development activities nor for the deployment, maintenance and use of the resulting ontology. Moreover, there is no explicit model of roles and policies. Nevertheless, the appeal of the approach lies precisely in its participatory and informal nature. It builds upon the real needs of the community interested in developing ontologies in specific domains. With respect to reaching consensus, generic techniques that found applicability in the ontology engineering field include the Nominal Group  and the Delphi  approaches, supported by elementary communication channels such as discussion forums and chats   . Further examples of collaboration are provided in the following sections.