CoP-adapted KM

we call explicit knowledge programming. We motivate the use of this approach, and describe a simple implementation based upon it. A survey of recent literature and computer games illustrates its novelty.

to be identified by fallible, post-hoc extraction processes. The WiCK project has investigated the writing process in a Semantic Web environment where knowledge services exist and actively assist the author. In this paper we discuss the need to make knowledge an explicit part of the document representation

for the knowledge engineering task with OWL ontologies. Thus the user is enabled to conveniently reuse them. Macros lead to an enhanced maintainability of the ontology, to a higher level of abstraction in the specification (and thus closer to the human conceptualization), to a faster and less error-prone creation

in the protocol. An agent knowledge can be partitioned into explicit knowledge and procedural knowledge. The explicit knowledge refers to the set of information which is either proper to the agent or directly obtained from other agents through communication. The procedural knowledge relates to the set

significant part of this knowledge is stored in the form of explicit knowledge, that is, structured documents that contain experiences and information of the development of the product. To manage them adequately contributes to the knowledge management effort. In this work the form of recording of explicit

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from

Two experiments examined the claim for distinct implicit and explicit learning modes in the artificial grammar-learning task (Reber, 1967, 1989). Subjects initially attempted to memorize strings of letters generated by a finite-state grammar and then classified new grammatical and nongrammatical

In this paper, we give an overview of a system (CAIMAN) that can facilitate the exchange of relevant documents between geographically dispersed people in Communities of Interest. The nature of Communities of Interest prevents the creation and enforcement of a common organizational scheme for documents, to which all community members adhere. Each community member organizes her documents according to her own categorization scheme (ontology). CAIMAN exploits this personal ontology, which is essentially the perspective of a user on a domain, for information retrieval. Related documents are retrieved on a concept granularity level from a central community document repository. To find the related concepts in the queried ontology, CAIMAN performs an ontology mapping. The ontology mapping in CAIMAN is based on a novel approach, which considers the concepts in an ontology implicitly represented by the documents assigned to each concept. Using machine learning techniques for te...

ABSTRACT: The present study, a partial replication of Erlam, Philp and Elder’s (2009), aims at investigating the level of explicit knowledge in a sample composed by ten Brazilian EFL teachers as well as its implications in their teaching focusing on the form in the classroom. Based