Knowledge Management Framework
The Semantic Web is an extension of the current web in which information is given a well-defined meaning, enabling better cooperation between computers and people. The Semantic Web is the abstract representation of data on the World Wide Web, based on the RDF standards. The Semantic Web provides a powerful platform for developing the knowledge management systems. One main problem is in representing knowledge in the machine-understandable form, so that relevant knowledge can be easily found by machine agents. The Knowledge Management Framework enables efficient management of knowledge sources on the Semantic Web, using presented conditional descriptions for a more effective searching for knowledge. The Knowledge Management Framework features a knowledge management approach based on Resource Description Framework (RDF) compatible format. The Knowledge Management Framework is based on existing Semantic Web tools such as OntOMat, OntOMat-SOEP, OntOMat-REVERSE, OntOMat-CRAWL, and Ontobroker, which should be slightly extended in order to operate with more expressible data format.
The main process in a knowledge management system is the finding of knowledge sources which are uses to solve some knowledge related problems. Based on knowledge formalisation, the knowledge sources can be divided into two categories which include formal expert rules and documents. For more efficient searching of the knowledge contained in the documents, the content of the documents is indexed using some ontology-based statements. Here the statements have a conditional form. The precondition-action statement enables the usage of the same logical mechanisms to manage both categories of knowledge sources. Sometimes, a search for relevant knowledge results in some expert rules and/or some documents.
Knowledge can be collected from various sources and in different formats, then stored in the common representation formalism, processed in order to compute interdependencies between knowledge items or to resolve conflicts, shared/searched and finally used for problem solving. The Knowledge Management Framework encompasses the following processes such as knowledge capturing, knowledge representation, knowledge processing, knowledge sharing, and using of knowledge. All these processes are related somehow to the domain ontology. Since ontology is a domain model, it contains a set of domain axioms which are used for deriving new information.
Four types of knowledge sources can be treated in the knowledge capturing phase which includes expert knowledge, legacy rule-base systems, metadata repositories and documents. Each of the knowledge source is associated with a Semantic Web tool. You can capture the expert knowledge in the form of rules, using a simple ontology editor plug-in OntOMat SOEP. The OntOMat SOEP is an ontology editor that is extended with rule-editing capabilities. The OntOMat SOEP provides structure as well as vocabulary, i.e. lexical layer of the domain ontology, for the rule creation. Although these rules are related to domain ontology, they are not treated as axioms in that ontology. The ontological axioms should be always-true statements, which is not the case for expert rules. The OntOMat SOEP saves the expert rules directly in the RDFRule format.
Legacy rule-base systems are very valuable sources of sharable knowledge, which can be consulted in solving some problems, either for free or for some price. The focus is not on collaborative problem solving via querying the federation of rule bases, but in the creating high-specific expert bases, by importing relevant rule chains from those rule bases. The OntOMat-REVERSE tool which translates the content of a relational database into an ontology represented in the RDF, for the support of this translation into RDFRule.
will be the primary knowledge source for sharing knowledge in the future.
To make the sharing more efficient some mechanisms for knowledge packaging
and knowledge trading/pricing are needed. The OntOMat-CRAWL tool has capabilities
to collect web documents that fit the given knowledge model, so that the
adaptation to rule-crawling is straightforward. Knowledge are informally
represented in the documents, whereas the content of a document can be
formally stated by ontology-based indexes. This process is called semantic
annotation which is supported by the OntOMat annotation framework
The knowledge processing component enables efficient manipulation with the stored knowledge, especially graph-based processing for the knowledge represented in the form of rules. Knowledge sharing is realised by searching for rules that satisfy the query conditions. Rules are related to domain ontology, which contains domain axioms used for deriving new assertions. Therefore the searching is realised as an inferencing process. The Ontobroker tool performs the process of inferencing using RDF inputs. The system treats facts and queries as rules without the rule body and the rule head, respectively. This facility enables using the SOEP editor as a query interface.
The main advantage of using the Knowledge Management Framework, is that a conditional statement is used for the semantic annotation of knowledge sources. As the statements used in the annotation are put into the context of each other, this consequently leads to efficient searching for knowledge. Annotating knowledge resources using Precondition-Action statements enable semantic hyperlinking 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. As a result, 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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