this paper have been discussed in a talk at Pittsburgh Center for the Philosophy of Science during 1998; I wish to thank Horacio Arl'o Costa, Paolo Bouquet, Michael Green, Marcello Frixione and some unknown referees for their very useful comments

This paper discusses aspects of context as applied to ontologies. In particular, we note some formalizations of context that have been applied to ontologies such as Menzel (1999) and Akman & Surov (1996, 1997), that have largely been framed in terms of theories such as Situation Theory (Barwise & Perry, 1983) which originated in natural language semantics. We also mention the notion of labeled deduction (Gabbay, 1996) and speculate on its prospective use in the contextualizing of ontologies. The latter can be viewed as a mechanism for annotating ontological assertions and proofs with contextual information about provenance, security, strength/confidence of assertion, and aspects of policy. Labeled deduction correlates one or more logics, with one logic addressing the primary assertion or

Also: to appear as a chapter P. Bouquet, L. Serafini, "Perspectives on context", CSLI On the dimensions of context dependence

this paper: Horac io Arl Costa, Margherita Benzi, Paolo Bouquet, GillesFaucIhj'I Marc ello Frixione, Mic)j Green, Diego Marc`)j Marina Sbis. Spec.h thanks to the Pittsburgh Center for Philosophy ofSc`j)Ij where I first presented theschH' of this paper, and to theGiuncHhjH researc group in Trento, whic set up the great opportunity for discHHMh suc topic at Context 99. 2 PencM 1998 and 1999a provide an interpretation of Wittgenstein's remarks in t is direc'Hh 3 We ave to take into aco hHM anot er general distincHB) given by Perry 1997, among presemantic semantic and postsemantic costsem (t e term "presemantic) is used by Kaplan 1977, par.XXII). T e presemantic cresema is w at gives an ambiguous epression its linguistic meaning (in "I saw er ducH under t e table" we ave to decjz w et er " er" is a pronoun or an adjec tive, or w et er "duc`j is a noun or a verb). T e semantic cmantic is w at gives t e evaluation of t e terms oncz t eir

Information foraging and sensemaking with heterogeneous information are context-dependent activities. Thus visual analytics tools to support these activities must incorporate context. But, context is a difficult concept to define, model, and represent. Creating and representing context in support of visually-enabled reasoning about complex problems with complex information is a complementary but different challenge than that addressed in context-aware computing. In the latter, the goal is automated adaptation of the system to meet user needs for applications such as mobile location-based services where information about the location, the user, and the user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sensemaking, the user is likely to take an active role in foraging for the contextual information needed to support sensemaking in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytical reasoning in crisis management and humanitarian relief. The challenges stem from the diverse forms of information that can provide context and difficulty in defining and operationalizing context itself. Here, we pay particular attention to document foraging to support construction of the geographic and historical context within which monitoring and sensemaking can be carried out. Specifically, we present the concept of geo-historical context (GHC) and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application, a web-based tool that supports document foraging and sensemaking.

Context interpretation and context-based reasoning are key factors in the development of intelligent autonomous systems in a variety of applications. The ability to represent contextual factors, interpret them and combine them with other sources of knowledge are some of the challenges to enable intelligent systems achieve correct behavior. Much work has been done in application areas that make use of contextual information, such as pervasive computing, logic-based sensor fusion and data integration. As well, the theoretical foundations for context-based reasoning have been studied. However, there is still a great deal to do in context modeling, since generic context models for contextaware application development need to be further explored, as does the role of context reasoning with more recently emerging areas such as ontologies, including the semantic web, and approaches to belief change. Context-dependent data can arise from different sources; for example it may be gathered by sensors or collected from different knowledge- or databases. The incompleteness and heterogenous nature of such data and the need for state-based context interpretation in dynamic systems suggest that nonmonotonic reasoning techniques can