Abstract
Knowledge management (KM) has been recognized as one of the most critical factors for obtaining organizational invaluable competitive advantage (Antoniou & Harmelen, 2004). New advances in IT provide novel methods to manage organizational knowledge efficiently. In this way, organizations have developed various systems to create, collect, store, share and retrieve organizational knowledge in order to increase their efficiency and competitiveness. Currently, regard to KM systems must maintain mass amount of data from various systems all over the organization as well as organizational extended value chain. KM systems must be able to integrate structured and unstructured data coming from heterogeneous systems to have a precise management on knowledge. This integration will help to apply useful operations on data such as analyze, taxonomy, retrieve and apply logical inference in order to obtain new knowledge. Nevertheless, there is some limitation in achieving the objectives of KM due to limited ability for semantic integration. Thus, the traditional methods are not responsible for KM systems users needs anymore. So There is a growing need for new methods to be used. To overcome these limitations we need to find a way to express meaning of concepts, the area that semantic techniques can help. This techniques offer novel methods to represent meaning of concepts and applying logical operation to get new knowledge of that concepts. These techniques organize information in a machine-processable manner, which allow machines to communicate directly without human intervention. Therefore, using these emerging techniques in KM systems show us a promising future in managing knowledge. The first step on implementing such systems is application identification of semantic techniques in KM systems and recognizing that in which areas this techniques can help to solve all limitations of current KM systems. In this paper, we aim to identify limitation of KM systems and present a comprehensive view of how adding semantics to data can help to overcome.
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References
Akhgar, B., Siddiqi, J., Hafeez, K., Stevenson, J.: A conceptual architecture for e-knowledge Management. In: ICMLA 2002 Conference, CSREA Press, USA (2002)
Joo, J., Lee, S.M.: Adoption of the Semantic Web for overcoming technical limitationsof knowledge management systems. Expert Systems with Applications 36, 7318–7327 (2008)
Huang, C.-C., Lin, S.-H.: Sharing knowledge in a supply chain using the semantic web. Expert Systems with Applications 37, 3145–3161 (2010)
Chen, M.-Y., Chu, H.-C., Chen, Y.-M.: Developing a semantic-enable information retrieval mechanism. Expert Systems with Applications 37, 322–340 (2010)
Cayzer, S.: Semantic Blogging and Decentralized Knowledge Management system. Communication of the ACMÂ 47(12) (2004)
Aljawarneh, S., Alkhateeb, F., Maghayreh, E.A.: A Semantic Data Validation Service for Web Applications. Journal of Theoretical and Applied Electronic Commerce Research, Electronic version 5, 39–55 (2010)
Ma, Z., Wang, H., (eds.) The semantic web for knowledge and data management. International Journal of Information Management 29, 420–422 (2009)
Qu, Z., Ren, Z.: The Frame of Enterprise Knowledge Management Model Based on Semantic Web. In: ICSP 2008 Proceedings (2008)
Sereno, B., Uren, V., et al.: Semantic Annotation Support in the Absence of Consensus. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 357–371. Springer, Heidelberg (2004)
Bernes-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 34–43 (2001)
Pollock, J.T.: Semantic web for dummies. Wiley Publishing, Inc., Indianapolis (2009)
Beydoun, G., Kultchitsky, R.R., Manasseh, G.: Evolving semantic web with social navigation. Expert Systems with Applications 32, 265–276 (2007)
Damodaran, L., Olphert, W.: Barriers and facilitators to the use of knowledge management systems. Behaviour and Information Technology 19, 405–413 (2000)
Kiryakov, A., Popov, B.: Semantic annotation, indexing, and retrieval. Science, Services and Agents on the World Wide Web 2, 49–79 (2004)
Kiryakov, A., Popov, B., Kitchukov, I., Angelov, K.: Shared Ontology for Knowledge Management. Semantic Knowledge Management (2009)
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Shahmoradi, M.R., Akhgar, B. (2011). Application Identification of Semantic Web Techniques in KM Systems. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds) Conceptual Structures for Discovering Knowledge. ICCS 2011. Lecture Notes in Computer Science(), vol 6828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22688-5_34
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DOI: https://doi.org/10.1007/978-3-642-22688-5_34
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