Abstract. On the Semantic Web ontologies are most commonly treated as artifacts created by knowledge engineers for a particular community. The task of the engineers is to forge a common understanding within the community and to formalize the agreements, prerequisites of reusing domain knowledge in information systems. However, the process of objectifying ontologies results in ontological drift over time (as the community and its understanding evolves independently of the agreement) and results in the loss of local views over the domain. Several authors have suggested emergent semantics as a solution [1]. The idea of emergent semantics is to define the ontology as an emergent feature of a system of autonomous agents acting in dynamic, open environments. Besides an agreement over the kind of environment in which emergence could be observed, there is little common ground in what emergence would constitute. We have proposed elsewhere that the first step towards emergent semantics

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a webbased presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.

Abstract. Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications. 1

A Community of Interest is a specific type of Community of Practice. It is formed by a group of individuals who share a common interest or passion. These people exchange ideas and thoughts about the given passion. However, they are often not aware of their membership to the community, and they may know or care little about each other outside of this clique. This paper describes a proposal to automatically identify Communities of Interest from the tastes and preferences expressed by users in personal ontology-based profiles. The proposed strategy clusters those semantic profile components shared by the users, and according to the clusters found, several layers of interest networks are built. The social relations of these networks might then be used for different purposes. Specifically, we outline here how they can be used to model group profiles and make semantic content-based collaborative recommendations.

Abstract. The call for a “focus on content ” in ontology research by Nicola Guarino and Mark Musen in their launching statement of the journal Applied Ontology has quite some implications and ramifications. We reflectively discuss ontology engineering as a scientific discipline, and we put this into the wider perspective of debates in other fields, including the methodology of social and natural sciences, and of Information Systems and design science research. We outline how ontologies provide us with a (new) scientific method for theory formation. This positioning allows for stronger concepts and techniques for theoretical, empirical and practical validation that in our view are now needed in the field. A prerequisite for this is an emphasis on ontology as a (domain) content oriented concept, rather than as primarily a computer representation notion. Taking application domain theories and the associated content reference of ontologies really seriously as first-class citizens will actually increase the contribution of ontology engineering to the development of scientific method in general. Next, ontologies should develop from the current static representations of relatively stable domain content into actionable theories-in-use, and a possible way forward is to build in capabilities for self-organization of ontologies as service-oriented knowledge utilities (SOKUs) that can be delivered over the Web.

In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.

A Community of Interest is a specific type of Community of Practice. It is formed by a group of individuals who share a common interest or passion. These people exchange ideas and thoughts about the given passion. However, they are often not aware of their membership to the community, and they may know or care little about each other outside of this clique. This paper describes a proposal to automatically identify Communities of Interest from the tastes and preferences expressed by users in personal ontology-based profiles. The proposed strategy clusters those semantic profile components shared by the users, and according to the clusters found, several layers of interest networks are built. The social relations of these networks might then be used for different purposes. Specifically, we outline here how they can be used to model group profiles and make semantic content-based collaborative recommendations.

In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.

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