Peer - Mediated Distributed Knowledge Management

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 imple-mented as a knowledge exchange level on top of JXTA

Knowledge management support for enterprise distributed systems

Explosion of information and increasing demands on semantic processing web applications have software systems to their limits. To address the problem we propose a semantic based formal framework (ADP) that makes use of promising technologies to enable knowledge generation and retrieval. We argue that this approach is cost effective, as it reuses and builds on existing knowledge and structure. It is also a good starting point for creating an organisational memory and providing knowledge management functions

A Peer-to-Peer Architecture for Distributed Knowledge Management.

Most of the knowledge management systems of complex organizations are based on technological architectures that are in contradiction with the social processes of knowledge creation. In particular, centralized architectures are adopted to manage a process that is intrinsically distributed. In this paper, assuming a Distributed approach to Knowledge Management (DKM), is proposed that technological and social architectures must be reciprocally consistent. Moreover, in the domain of Knowledge Management, technological architectures should be designed in order to support the interplay between two qualitatively different processes: the autonomous management of knowledge of individuals and groups - here called Knowledge Nodes (KNs) -, and the coordination required in order to exchange knowledge among them. Finally a peer to peer architecture to support knowledge exchange across distributed and autonomous KNs is presented

Enabling Distributed Knowledge Management: Managerial and Technological Implications

In this paper we show that the typical architecture of current KM systems re.ects an objectivistic epistemology and a traditional managerial control paradigm. We argue that such an objectivistic epistemology is inconsistent with many theories on the nature of knowledge, in which subjectivity and sociality are taken as essential features of knowledge creation and sharing. We show that adopting such a new epistemological view has dramatic consequences at an architectural, managerial and technological level

Knowledge Nodes: the Building Blocks of a Distributed Approach to Knowledge Management

Abstract: In this paper we criticise the objectivistic approach that underlies most current systems for Knowledge Management. We show that such an approach is incompatible with the very nature of what is to be managed (i.e., knowledge), and we argue that this may partially explain why most knowledge management systems are deserted by users. We propose a different approach - called distributed knowledge management - in which subjective and social (in a word, contextual) aspects of knowledge are seriously taken into account. Finally, we present a general technological architecture in which these ideas are implemented by introducing the concept of knowledge node

Is a Semantic Web Agent a Knowledge-Savvy Agent?

The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike

EVALUATING DISTRIBUTED COLLABORATIVE SYSTEMS FROM A KNOWLEDGE MANAGEMENT PERSPECTIVE

This paper presents the evaluation of distributed and collaborative systems from the knowledge point of view, the most important asset of these kinds of systems. The paper analyses the quality characteristics of distributed collaborative systems and proposes a metric to evaluate the aspects of knowledge management process.distributed systems, collaborative systems, knowledge management

Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems

A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM

Knowledge Management: Are We Missing Something?

As commercial organisations face up to modern pressures to downsize and outsource they have begun to realise that they have lost knowledge as people leave and take with them what they know. This knowledge is increasingly being recognised as an important resource and organisations are now taking steps to manage it. In addition, as the pressures for globalisation increase, collaboration and co-operation is becoming more distributed and international. Knowledge sharing in a distributed international environment is becoming an essential part of Knowledge Management (KM), although this area does not yet appear to be given much attention. In this paper we make a distinction between hard and soft knowledge within an organisation and argue that much of what is called KM deals with hard knowledge and emphasises capture-codify-store. This is a major weakness of the current approach to KM, equating more with Information Management than Knowledge Management. Soft knowledge is concerned more with the social and cultural aspects of knowledge, its construction and the processes through which it is sustained and shared. This paper addresses this weakness by exploring the sharing of 'soft' knowledge using the concept of communities of practice.Knowledge Management, Lost Knowledge, Distributed Working, Communities of Practice