A theoretical and formal model for knowledge management systems

Knowledge management is now a huge domain, where it is difficult to have a clear view of the manipulated concepts and their crossed-relations. The development of that domain requires now a theoretical framework including concepts from various theories as Knowledge Economy, Information Systems, Knowledge theories (in particular Nonaka's theory), Communities of practice (Wenger's theory), General System Theory, Semiotic, Information theory, Knowledge Worker concept …This paper is an attempt to provide sound basis for such a framework, with a mathematical formalism. The formalism is inspired by the one used in Information System Theory, based on General System Theory (OID Model). The proposed model is structured by the set of networks (or communities) of Knowledge Workers, A, the Information System, I, and the Knowledge Capital, K (AIK model). Different morphisms, functions and operators provide classical KM links (or knowledge flows) and KM combinations for those subsystems.KM formalism, Knowledge management, KM framework , Knowledge theories

Cognitive modelling of language acquisition with complex networks

ABSTRACT Cognitive modelling is a well-established computational intelligence tool, which is very useful for studying cognitive phenomena, such as young children's first language acquisition. Specifically, linguistic modelling has recently benefited greatly from complex network theory by modelling large sets of empirical linguistic data as complex networks, thereby illuminating interesting new patterns and trends. In this chapter, we show how simple network analysis techniques can be applied to the study of language acquisition, and we argue that they reveal otherwise hidden information. We also note that a key network parameter -the ranked frequency distribution of the links -provides useful knowledge about the data, even though it had been previously neglected in this domain

Актуалізація прагматичного компонента слова у політичному дискурсі (Political discourse actualising a word pragmatic component)

У центрі даного дослідження- причини актуалізації/активації прагматичного компонента значення слова ‘consultation’ у структурі політичного дискурсу. Семантична взаємодія лексеми та дискурсу обумовлює деякі зміни у лексичному значенні слова. (The actualization/activisation of a pragmatic component in the lexical meaning of the word ‘consultation’ under its discourse influence is in the focus of the present investigation. There are two main modes for exploring word meaning: in relation to other words and in relation to the world. First, ttraditional method used in dictionaries is to define a word in terms of other words, second, a foundational theory,which is interested in how lexical expressions acquire properties necessary for the user in discourse The semantic correlation of the lexeme and the discourse stimulates some shifts in the word meaning. We focus our investigation on of the lexeme consultation functioning in political discourse. The professional (political) discourse is usually represented by a semantic net [11, p.3-18] which makes it cohesive and determines the topic. The lexemes in the net share a common component that links them into a semantic domain, for instance, party, negotiation, information, agreement, decision, etc. encode the political discourse. The notion of «Semantic domain» is inspired by «The Theory of Semantic Fields,» a structural model for lexical semantics introduced by Jost Trier at the beginning of the last century. The basic assumption is that lexicon is structured into Semantic Domains: semantic relations among concepts belonging to the same domain are very dense. To reveal a pragmatic component in the semantic domain of ‘consultation’ linking other registers of discourse, for instance, legal, academic, banking, medical, and family is another step forward in semantic pragmatics.

Causality between Oil Prices and Tourist Arrivals

open access journalThis paper investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain, and information theory domain perspectives. Data relating to the US and nine European countries are exploited in this paper with causality tests which include the time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method and via the uncovering of significant causal links from oil prices to tourist arrivals

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

Visualizing latent domain knowledge

Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain