Knowledge Sharing on the Semantic Web

Kings, Nicholas J.; Gale, Caroline; Davies, John

doi:10.1007/978-3-540-72667-8_21

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…Many tools and proposals have been developed in order to support collaboration and KS using different approaches: ontologies, folksonomies, wikis, and social networks are some models that support KS (Kings, Gale, and Davies 2007). Research projects like SQUIDZ (Kings et al 2007), Palette (Vidou et al 2006 Improving KS by means of automated distribution mechanisms, and using a single-and domain-independent knowledge representation still remains as a research area to be explored. Smart Knowledge Management System, SOEKS, and Decisional DNA Knowledge has been an important asset for individuals, organizations, and society through the ages.…”

Section: Knowledge-sharingmentioning

confidence: 99%

Smart Knowledge-Sharing Platform for E-Decisional Community

Mancilla-Amaya

Sanín

Szczerbicki

2010

Cybernetics and Systems

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Knowledge plays a major role in enterprises, given its importance as a significant organizational asset. In order to solve problems and support complex decision-making processes, knowledge and experience have to be transmitted between diverse individuals and organizations. Thus, knowledge-sharing can be considered a fundamental element in any knowledge-oriented process, because it fosters collaboration, and facilitates experiential knowledge discovery, distribution, and use. We present the E-Decisional Community, a proposal for an integrated knowledge-sharing platform where several entities are able to share experiential knowledge. Its main concern is to promote experiential knowledge evolution and sharing through generations of decision makers, aiming at the creation of a marketplace where knowledge is provided as a service.

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Section: Knowledge-sharingmentioning

confidence: 99%

Smart Knowledge-Sharing Platform for E-Decisional Community

Mancilla-Amaya

Sanín

Szczerbicki

2010

Cybernetics and Systems

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“…Several studies have been conducted to reuse knowledge distributed in social networks by formalizing knowledge in approaches, such as ontologies, which must be carefully created by humans [4], [14], [25]. The authors in [14] developed a system that automatically classifies browsed web pages against an ontology, and allows users to share comments made on these pages.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [14] developed a system that automatically classifies browsed web pages against an ontology, and allows users to share comments made on these pages. As the user browses web pages, recommendations of relevant documents which have already been shared are produced, based upon both the user's social network as well as the semantic content of the page currently in view.…”

Section: Related Workmentioning

confidence: 99%

“…It is essential that the new people be able to utilize the know-who and know-how information created by the original experts as contained in the logs of message systems. Semantic web techniques allow important concepts to be analyzed and extracted as classes within an objective domain, with the goal being to create a domain ontology comprising classes, relationships among classes, and instances that are members of each class [4], [14]. By referring to this ontology, users can acquire detailed knowledge about the objective domain so described.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Know-Who/Know-How Using Development Project-Related Taxonomies

Nakatsuji

Tanaka

Madokoro³

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYProduct developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C, not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method. key words : taxonomy, knowledge management, know-who/know-how

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“…The system guides users to concepts in taxonomies, defined as a concept hierarchy of developed products and tasks related to those products, following the idea of information sharing in semantic web techniques [1]. For example, when preparing a taxonomy of network development project, we can create a taxonomy of network equipment product developed by this project (such as network management systems and Domain Name Systems (DNSs)) and create a taxonomy of developing tasks for those products (such as verifying and coding).…”

Section: Introductionmentioning

confidence: 99%