The notion of context appears in computer science, as well as in several other disciplines, in various forms. In this paper, we present a general framework for representing the notion of context in information modeling. First, we define a context as a set of objects, within which each object has a set of names and possibly a reference: the reference of the object is another context which "hides" detailed information about the object. Then, we introduce the possibility of structuring the contents of a context through the traditional abstraction mechanisms, i.e. classification, generalization, and attribution. We show that, depending on the application, our notion of context can be used as an independent abstraction mechanism, either in an alternative or a complementary capacity with respect to the traditional abstraction mechanisms. We also study the interactions between contextualization and the traditional abstraction mechanisms, as well as the constraints that govern such interactions. Finally, we present a theory for contextualized information bases. The theory includes a set of validity constraints, a model theory, as well as a set of sound and complete inference rules. We show that our core theory can be easily extended to support embedding of particular information models in our contextualization framework.

Although semantic data models provide expressive conceptual

No abstract

Although semantic data models provide expressive conceptual modeling mechanisms, they do not support context, i.e. providing controlled partial information on conceptual entities by viewing them from di erent viewpoints or in di erent situations. In this paper, we present a m o d e l f o r representing contexts in information bases along with a set of operations for manipulating c o n texts. These operations support context creation, update, copy, union, intersection, and di erence. In particular, our operations of context union, intersection, and di erence are di erent from these of set theory as they take i n to account the notion of context. However, they also satisfy the important properties of commutativity, associativity, and distributivity. Our model contributes to the e cient handling of information, especially in distributed, cooperative e n vironments, as it enables (i) representing (possibly overlapping) partitions of an information base (ii) partial representations of objects, (iii) exible naming (e.g. relative names, synonyms and homonyms), (iv) focusing attention, and (v) combining and comparing di erent partial representations. This work advances towards the development o f a formal framework intended to clarify several theoretical and practical issues related to the notion of context. The use of context in a cooperative e n vironment is illustrated through a detailed example.

In information bases following semantic and object-oriented data models, logical names are used for the external identification of objects. Yet the naming schemes employed are not "natural" enough and several problems often arise: logical names can be ambiguous, excessively long, unrelated to or unable to follow the changes of the environment of the named object. In natural language, similar problems are resolved by the context within which words are used. An approach to introducing a notion of context in an information base is to provide structuring mechanisms for decomposing it into possibly overlapping parts. This paper focuses on developing a context mechanism for an information base and, in particular, exploiting this mechanism for naming purposes. Rules are developed for generating meaningful names for objects by taking their context into account. This context-based naming enhances name readability, resolves name ambiguities, saves a lot of redundant name substrings, and it localizes and thus facilitates consistency checking, query processing and update operations. In modeling, it supports systematic naming of objects, and thus enhances cooperation between the designers and the end-users in the sense that the contents of the information base are more understandable by both of them.