Managing diversity in knowledge

Abstract

We are facing an unforeseen growth of the complexity of data, content and knowledge. Here we talk of complexity meaning the size, the sheer numbers, the spatial and temporal pervasiveness of knowledge, and the unpredictable dynamics of knowledge change, unknown at design time but also at run time. In knowledge engineering and management the "usual" approach is to take into account, at design time, all the possible future dynamics. The key idea is to design a "general enough" reference representation model, expressive enough to incorporate all the possible future variations of knowledge. The approach proposed here is somewhat opposite. Instead of taking a top-down approach, where the whole knowledge is designed integrated, with a pure a-priori effort, we propose a bottom-up approach where the different knowledge parts are kept distinct and designed independently. The key idea is to consider diversity as a feature which must be maintained and exploited and not as a defect that must be cancelled or absorbed in some general "universal-looking" schema. People, organizations, communities, populations, cultures build diverse representations of the world for a reason, and this reason lies in the local context. What context exactly, is hard to say. However it can be safely stated that context has many dimensions: time, space, contingent goals, short term or long term goals, personal or community bias, environmental conditions, ., and so on. We will present and discuss the ideas above comparing, as an example, how the notions of context and of ontology have been applied in the formalization of knowledge (for instance in the Semantic Web). We will then argue that a context-based approach to managing diversity in knowledge must be studied at three different levels: 1. Representation level, dealing with all the issues related to how local and global knowledge are represented, to their semantics, and to the definition of the operations which allow to manipulate them. 2. Organization level, dealing with the organization and interaction of interconnected knowledge parts and systems manipulating them. 3. Social level, dealing with the problem of how systems (incrementally) reach agreement, thus creating (sub)communities of shared or common knowledge. © Springer-Verlag Berlin Heidelberg 2006.