CONGAS: a collaborative ontology development framework based on Named GrAphS

The process of ontology development involves a range of skills and know-how often requiring team work of different people, each of them with his own way of contributing to the definition and formalization of the domain representation. For this reason, collaborative development is an important feature for ontology editing tools, and should take into account the different characteristics of team participants, provide them with a dedicated working environment allowing to express their ideas and creativity, still protecting integrity of the shared work. In this paper we present CONGAS, a collaborative version of the Knowledge Management and Acquisition platform Semantic Turkey which, exploiting the potentialities brought by recent introduction of context management into RDF triple graphs, offers a collaborative environment where proposals for ontology evolution can emerge and coexist, be evaluated by team users, trusted across different perspectives and eventually converged into the main development stream

Exploiting Transitivity in Probabilistic Models for Ontology Learning

Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as ontologies are knowledge repositories used in a variety of applications. To be effectively used, these ontologies have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on ontology learning models by covering different aspects of the task. We propose probabilistic models for learning ontologies that expands existing ontologies taking into accounts both corpus-extracted evidences and structure of the generated ontologies. The model exploits structural properties of target relations such as transitivity during learning. We then propose two extensions of our probabilistic models: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that put human validations in the learning loop. This latter provides a graphical user interface and a human-computer interaction workflow supporting the incremental leaning loop

Gobbleing over the web with semantic turkey

In this work we introduce Semantic Turkey, a Semantic Extension for the popular web browser Mozilla Firefox. Semantic Turkey can be used to annotate information from visited web sites and organize this information according to a personally defined ontology. Clear separation between knowledge data (the WHAT) and web links (the WHERE) is established into the knowledge model of the system, which allows for innovative navigation of the acquired information and of the pages where it has been collected. This paper describes the architecture of the Semantic Turkey extension for Firefox, discusses its development in the context of the FILAS technophore project, shows its most interesting features and presents our plans for future improvements of the tool

Gobbleing over the web with semantic turkey

In this work we introduce Semantic Turkey, a Semantic Extension for the popular web browser Mozilla Firefox. Semantic Turkey can be used to annotate information from visited web sites and organize this information according to a personally defined ontology. Clear separation between knowledge data (the WHAT) and web links (the WHERE) is established into the knowledge model of the system, which allows for innovative navigation of the acquired information and of the pages where it has been collected. This paper describes the architecture of the Semantic Turkey extension for Firefox, discusses its development in the context of the FILAS technophore project, shows its most interesting features and presents our plans for future improvements of the tool