We study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability.

Pervasive computing aims at providing services for human beings that interact with their environment (encompassing objects and people who reside in it). Pervasive computing applications must be able to take into account the context in which users evolve, for example, physical location, social or hierarchical position, current tasks as well as related information. These applications have to deal with the dynamic integration in the environment of new, and sometimes unexpected, elements (users or devices). In turn, the environment has to provide context information to newly designed applications. This requires a framework which is open, dynamic and minimal. We describe an architecture in which context information is distributed in the environment and context managers use semantic Web technologies in order to identify and characterize available resources. The components in the environment maintain their own context expressed in RDF (Resource Description Framework) and described through OWL ontologies. They may communicate this information to other components, obeying a simple protocol for identifying them and determining the information they can provide. We show how this architecture allows introducing new devices and new applications without interrupting what is working. In particular, the openness of ontology description languages makes possible the extension of context descriptions and ontology matching helps dealing with independently developed ontologies.

A challenging issue to advance the existing P2P semantic routing protocols is related to the capability of developing mechanisms for focused selection of the query recipients by taking into account a semantically rich description of the context of each peer. In this article, we present the H-Link semantic routing approach designed to exploit the results of an ontology matchmaking process for providing a semantic overlay network where peers having similar contexts are recognized and interlinked as semantic neighbors. In particular, H-Link aims at advancing the existing semantic routing protocols by combining ontology-based peer context descriptions and ontology matching techniques for providing query forwarding on a real semantic basis, in a completely decentralized way.

Context modeling has long been acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are (1) the explicit distinction between historic user context and live user context, (2) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and (3) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.

Human categorization is neither a binary nor a context-free process. Rather, the criteria that govern the use and recognition of certain concepts may be satisfied to different degrees in different contexts. In light of this reality, the idealized, static structure of a lexical-ontology like WordNet appears both excessively rigid and unduly fragile when faced with real texts that draw upon different contexts to communicate different world-views. In this paper we describe a syntagmatic, corpus-based approach to redefining the concepts of a lexical-ontology like WordNet in a functional, gradable and context-sensitive fashion. We describe how the most diagnostic properties of concepts, on which these functional definitions are based, can be automatically acquired from the Web, and demonstrate how these properties are more predictive of how concepts are actually used and perceived than properties derived from other sources (such as WordNet itself).