A Semantic Web based Framework for Knowledge Management System

Report

Following Figure shows the proposed framework for knowledge management system on the semantic web, which reflects the variety of knowledge transformations in this distributed environment: knowledge can be collected from various sources in different formats, and then stored in the common representation formalism, processed in order to compute interdependencies between knowledge (Example: relationship between bird and Kiwi) items or to resolve conflicts (Example: Kiwi is a bird. Birds can fly. Kiwi can not fly.), shared/searched and finally used for problem solving. Therefore this approach has following processes:

  • Knowledge Capturing
  • Knowledge Representation
  • Knowledge Processing
  • Knowledge Sharing
  • Using of Knowledge

Knowledge Capturing: We identify four types of knowledge sources, which could be treated in knowledge capturing phase: (a) expert knowledge, (b) legacy (rule-base) systems, (c) metadata repositories and (d) documents. For knowledge capturing DSpace can be used. DSpace is an open source and combined project of HP Labs and MIT. DSpace has the ability of indexing and crawling the captured metadata. Because of high flexibility DSpace can be modified further to capture the expert knowledge (editor) as well as to convert the legacy system. Further this information will be converted to RDF Rules using a converter.
Knowledge Repository: Knowledge repository is a relational database organized in a way that enables efficient storing and access to RDF metadata. This repository can be seen as a RDF repository.
Knowledge Processing: Knowledge processing component enables efficient manipulation with the stored knowledge, especially graph-based processing for the knowledge represented in the form of rules, e.g. deriving dependency graph or consistency checking
Knowledge Sharing: Knowledge sharing is realized by searching for rules that satisfy the query conditions. In the RDF repository rules are represented as reified RDF statements and while in RDF any statement is considered to be an assertion, we can view an RDF repository as a set of ground assertions in the form (subject, predicate, and object). Rules are also related to domain ontology, which contains domain axioms used for deriving new assertions. Therefore the searching is realized as an inferencing process.
Using of Knowledge: The main advantage of this approach is using a conditional statement for the semantic annotation of knowledge sources. In that way we put statements used in the annotation into the context of each other, which consequently leads to efficient searching for knowledge. Moreover, annotating knowledge resources using Precondition-Action statements enables semantic hyper linking of each two resources, which satisfies the condition that the Precondition part of one annotation subsumes the Action part of the annotation of another resource. In that way querying for a problem can result in a composition of documents, which cover problem solving. This is a very important process in knowledge management or e-learning search.

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