Personal knowledge management for knowledge workers using social semantic technologies.

IJIIDS 01/2009; 3:28-43. DOI: 10.1504/IJIIDS.2009.023036
Source: DBLP

ABSTRACT Knowledge workers have different applications and resources in heterogeneous environments for doing their knowledge tasks and they often need to solve a problem through combining several resources. Typical personal knowledge management (PKM) systems do not provide effective ways for representing knowledge worker's unstructured knowledge or idea. In order to provide better knowledge activity for them, we implement Wiki-based sociAl Network Thin client (WANT) that is a wiki-based semantic tagging system for collaborative and communicative knowledge creation and maintenance for a knowledge worker. And also, we suggest the social semantic cloud of tags (SCOT) ontology to represent tag data at a semantic level and combine this ontology in WANT. WANT supports a wide scope of social activities through online mash-up services and interlink resources with desktop and web environments. Our approach provides basic functionalities such as creating, organising and searching knowledge at individual level, as well as enhances social connections among knowledge workers based on their activities.

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    ABSTRACT: Ontology mapping is critical for semantic interoperability between information systems in ontology-based distributed environments. Manual ontology mapping by human experts has been studied as traditional approach. However, these manual tasks are usually expensive, so that it is difficult to obtain mapping results between all possible pairs in a large-scale distributed information system. Thereby, in this paper, we propose a system to estimate the ontology mappings in an indirect manner by making the existing mappings collaboratively sharable and exchangeable, and more importantly, efficiently composing the collected existing mappings. In particular, this work focuses on query propagation for searching for relevant resources on the distributed networks. Once indirect mapping from source system to destination is obtained, the queries can be efficiently transformed to automatically exchange knowledge between them by referring to the mappings, even though they do not have direct connection. In order to evaluate the proposed mapping composition method, we have measured the ratio (i.e., precision and recall) of the indirect mappings to reference mappings which were acquired from human experts. It means that we have regarded information loss by query transformation as an important indicator to knowledge sharing in ontology-based distributed environment.
    Journal of Intelligent and Fuzzy Systems 01/2010; 21:187-195. · 0.94 Impact Factor

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