Collaborative ontology engineering: a survey <s> Introduction <s> Building new knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could instead be done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning. In this way, declarative knowledge, problem- solving techniques, and reasoning services could all be shared among systems. This approach would facilitate building bigger and better systems cheaply. The infrastructure to support such sharing and reuse would lead to greater ubiquity of these systems, potentially transforming the knowledge industry. This article presents a vision of the future in which knowledge-based system development and operation is facilitated by infrastructure and technology for knowledge sharing. It describes an initiative currently under way to develop these ideas and suggests steps that must be taken in the future to try to realize this vision. <s> BIB001 </s> Collaborative ontology engineering: a survey <s> Introduction <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> BIB002 </s> Collaborative ontology engineering: a survey <s> Introduction <s> For years, ontologies have been known in computer science as consensual models of domains of discourse, usually implemented as formal definitions of the relevant conceptual entities. Researchers have written much about the potential benefits of using them, and most of us regard ontologies as central building blocks of the semantic Web and other semantic systems. Unfortunately, the number and quality of actual, "non-toy" ontologies available on the Web today is remarkably low. This implies that the semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the semantic Web a reality. Theoretically minded advocates often assume that the lack of ontologies is because the "stupid business people haven't realized ontologies' enormous benefits." As a liberal market economist, the author assumes that humans can generally figure out what's best for their well-being, at least in the long run, and that they act accordingly. In other words, the fact that people haven't yet created as many useful ontologies as the ontology research community would like might indicate either unresolved technical limitations or the existence of sound rationales for why individuals refrain from building them - or both. Indeed, several social and technical difficulties exist that put a brake on developing and eventually constrain the space of possible ontologies <s> BIB003
Several decades ago, ontologies were introduced to information and communication technologies (ICT) as a novel means to represent the kinds of things that can be talked about in a system in a formal and explicit manner. Used in information retrieval, information extraction, as well as data and process integration  , ontologies provide reusable pieces of declarative knowledge, which can be-together with problem-solving methods and reasoning functionality assembled into high-quality technology and application systems in an economical fashion BIB001  . The Semantic Web is one of the most important application areas of ontologies. Initially introduced by Tim Berners Lee  , the originator of the World Wide Web, the idea of extending the current Web into a computer-processable knowledge infrastructure in addition to its actual, semistructured and human-understandable content foresees the usage of knowledge components, which can be easily integrated into and exchanged by ICT systems in an operationalized manner. In this context, the knowledge components, that is, the ontologies, are formalized using Web-suitable, semantically unambiguous representation languages such as Resource Description Framework (RDF) Schema  and Web Ontology Language (OWL;  , and are pervasively accessible and shared across the Web. Notably, the popularity of ontologies on the Semantic Web has led during the past years to the reinforced study of ontology engineering as a consensus-building process, in which a-potentially open and geographically distributed-group of stakeholders, or a community of practice, agrees upon a common view upon a domain of interest, and upon the way their shared knowledge can be structured in terms of concepts, attributes, relationships and constraints. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research in the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering  , it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully fledged environments providing dedicated support for collaboration and user participation BIB002 Tempich, 2006; BIB003 . Wikis, and other similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions, are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies surveyed. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development. The article targets collaborative ontology engineering researchers, but also technology providers and potential adopters, interested in getting a comparative overview of the state of the art in the field, pointers to ongoing projects, and some ideas about directions in which this field could evolve. This article is structured as follows. Section 2 reviews the key aspects of collaborative ontology engineering. In Section 3, we describe several established methodologies, which address these aspects from a process-oriented perspective, while in Section 4 we present software environments typically used when developing and maintaining an ontology by a group of stakeholders. We discuss our findings and based upon them recommend future directions of research and development for the ontology engineering community in Section 5.