Application identification of semantic web techniques in KM systems

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. © 2011 Springer-Verlag.