Distributed Knowledge Management is an approach to Knowledge Management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations should not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are defined to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers).

become a new fashioned managerial practice. Though KM theories seem to benefit from a "contamination" with cognitive and social sciences, which emphasize a subjective, contextual, and distributed approach to knowledge representation and integration, current technologies support what we may call a "god's eye" paradigm, in which knowledge is viewed as an objective resource. In this paper we discuss artificial intelligence theories and technologies that can support a shift to a new paradigm, called the "distributed intelligence" paradigm, in designing KM systems. Using the evolution of KM systems within Arthur Andersen Consulting as a motivating case study, we propose the framework of MultiContext Systems as a specification language for distributed intelligence KM systems, and sketch an agent-based architecture as an example of a KM system which embodies the assumptions of the distributed intelligence paradigm.

The goal of this paper is to present a new family of formal systems, so called multilanguage systems (ML-systems), which allow the use of multiple distinct first order languages and inference rules whose premises and consequences need not belong to the same language. ML-systems are argued to formalize naturally and elegantly notions like belief, knowledge and, more in general, various forms of propositional attitudes. Some instances of ML-systems are defined and proved equivalent to the modal logic K and some of Konolige's logics for belief.

Research in cognitive linguistics has yielded valuable semantic and pragmatic insights that should be incorporated by language understanding systems. These insights have not been expressed in a rigorous enough form to be implemented.

In this paper we present a new notion of formal system, so called multilanguage system (ML-system) which allows the use of multiple distinct languages, each language being associated with its theory. ML-systems allow the use of inference rules, called bridge rules, whose premises and consequences need not belong to the same language. Bridge rules allow the propagation of results among theories, thus making them "partially" dependent on one another. Some examples of ML-systems are proposed and argued to formalize naturally and elegantly propositional attitudes and, in particular, belief.

This paper presents a new method of analyzing Japanese noun phrases of the form N1 no N2. The Japanese postposition no roughly corresponds to of, but it has much broader age. The method exploits a definition of N2 in a dictionary. For example, rugby no coach can be interpreted as a person who teaches technique in rugby. We illustrate the effectiveness of the method by the analysis of 300 test noun phrases.

This dissertation examines the role of salience in natural language generation (NLG). The salience of an entity, in intuitive terms, refers to its prominence, and is interpreted as a measure of how well an entity stands out from other entities and biases the preference of the generator in selecting words and complex constructs. Through an analysis of previous work in diverse disciplines, we show the variety of salience effects in NLG. Next, we classify several important determinants of salience, corresponding to different factors contributing to salience. We then delineate two theoretically-significant categories: canonical salience and instantial salience. The former is characterized as a built-in preference in the general conceptual- and linguistic knowledge of the speaker. The latter refers to the salience of specific objects in the context of NLG, and may accrue through such determinants as vividness and recency of mention. Psycholinguistic results of Osgood and Bock are highligh...

. In this paper we propose to re-read the past work on formalizing context as the search for a logic of the relationships between partial, approximate, and perspectival theories of the world. The idea is the following. We start from a very abstract analysis of a context dependent representation into three basic elements. We briey show that all the mechanisms of contextual reasoning that have been studied in the past fall into three abstract forms: expand/contract, push/pop, and shifting. Moreover we argue that each of the three forms of reasoning actually captures an operation on a dierent dimension of variation of a context dependent representation, partiality, approximation, and perspective. We show how these ideas are formalized in the framework of MultiContext Systems, and briey illustrate some applications. 1

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Distributed Knowledge Management is an approach to knowledge management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations is not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are introduced to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers). A first version of the system, called KEx, is implemented as a knowledge exchange level on top of JXTA.