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A Framework for Knowledge Management on the Semantic Web | Resource Description Framework | Semantic Web

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this document provides an abstract for the project knowledge management through semantic web.
  A Framework for Knowledge Management on the Semantic Web  Nenad Stojanovic, Siegfried HandschuhAIFB InstituteUniversity of KarlsruheEnglerstrasse 11D-76131 KarlsruheGermany{nst, sha}@aifb.uni-karlsruhe.de ABSTRACT The Semantic Web can be a very promising platform for developing knowledge managementsystems. However, the problem is how to represent knowledge in the machine-understandableform, so that relevant knowledge can be found by machine agents. In this paper we present a   knowledge management approach based on RDF-compatible format for representing rulesand on a novel technique for the annotation of knowledge sources by using conditionalstatements. The approach is based on our existing Semantic Web tools. The main benefit is highimprovement in the precision by searching for knowledge, as well as the possibility to retrieve acomposition of knowledge sources which are relevant for the problem solving. Keywords Semantic Web, Conditional statements, Knowledge Management 1. INTRODUCTION The Semantic Web is the new generation of the World Wide Web, based on the semantic network knowledge representation formalism, which enables packaging information in the form of object-attribute-value statements, so called triplets. By assuming that terms used in these statements are based on the   formally specified meaning (for the community of interest), i.e.ontologies, these triplets can be semantically processed by machine agents. Most of the currentSemantic Web applications are based on using such atomic statements as pure facts, which wecan reason about. So, a machine agent can understand information that a concrete patient, whosuffers from disease X, is treated by medicine Y. Moreover, the agent can use this information inthe communication with other machine agents (e.g. to make an appointment with the doctor W),making the vision of the Semantic Web real [1]. But useful statements, which can be exchanged between agents, are not always related to concrete individuals - instances (e.g. patient X, diseaseY), but also to a group of individuals with some common characteristics (e.g. statements aboutfemale patients older than 60 who suffer from disease Y). Moreover, atomic statements could becombined in a more expressive way as simple conjunction, for example in the conditional form(e.g. Precondition: the patient is male and suffers from X; Action: he has to be treated bymedicine Y). On the implementation level this form can be represented using the If-Thenstatements, forming in that way reasoning atoms for inference- and trust- services on theSemantic Web. From the web-user point of view, the existence of more expressible statements ina machine-readable format means possibility to find more easily such conditional statements that  are relevant for current problem solving. Moreover, conditional statements can be used for “indexing” the content of web resources in a more expressive way than conjunction of keywords/concepts or general metadata (incl. relations). That will enable the formation of moresophistic, context -aware, queries and consequently, it can improve precision and recall in thesearching for knowledge. For example, the well-known document-indexing problem whether thedocument indexed by keywords “aspirin” and “headache” is about how aspirin curesheadache or how aspirin causes headache, can be resolved very easily by using proposedconditional statements. In order to use conditional statements in a knowledge managementscenario on the Semantic Web, one needs a machine understandable representation of suchstatements as well as powerful mechanisms for creating and manipulating them. In our  previous work we analysed the requirements for representing conditional information (i.e.knowledge) in a machineunderstandable format and proposed an RDFS format for representingrules, RDFRule [2]. Briefly, that format enables the representation of Horn rules, extended by theuncertainty factor and some model/context information. Since each rule contains a set of  premises and a set of conclusions (both of these sets can be empty), the proposed format can beused for the description of conditional statement in the form Precondition-Action. In this paper    we present an application framework for managing knowledge sources on the Semantic Web, by   using presented conditional descriptions for a more effective searching for knowledge. Theframework is based on our existing Semantic Web tools (OntOMat, OntOMat-SOEP,OntOMat-REVERSE, OntOMat-CRAWL, Ontobroker)1, which should be slightly extended inorder to operate with more expressible data format (conditional statements). This framework iselaborated in the next section. 2. THE KNOWLEDGE MANAGEMENT FRAMEWORK  The main process in a knowledge management system is the possibility to find knowledgesources, which are relevant for the problem at hand, as well as the process of providingknowledge sources, which can be used in resolving some problems. From the point of view of the knowledge formalisation, these knowledge sources can be divided into two categories   :formal expert rules and (multi-media) documents. In order to enable more efficient searching for    the knowledge that is contained in this second category, the content of the documents is indexed    by using some ontology-based statements. In our approach these statements have conditionalform: Precondition-Action, which enables us to use the same logical mechanisms in themanagement of both categories of knowledge sources. Moreover, a searching for relevantknowledge can result in some expert rules and/or some documents. The Figure 1 sketches the proposed framework for knowledge management on the Semantic Web, which reflects thevariety of knowledge transformations in this distributed environment:   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. Therefore,our knowledge management approach encompasses the following processes [3]:1.   Knowledge Capturing, 2. Knowledge Representation, 3. Knowledge Processing, 4.Knowledge Sharing and 5. Using of Knowledge.All processes are related somehow to domain ontology. Since ontology is a domain model, itcontains a set of domain axioms which are used for deriving new information – that is the task of an inference engine. In the following we describe these processes. Figure 1: Proposed knowledge management framework   1. We identify four types of knowledge sources, which could be treated in the knowledgecapturing phase: expert knowledge, legacy (rule -base) systems, metadata repositories anddocuments. For each of them we associate our Semantic Web tools:a) Expert knowledge in the form of rules can be captured using our Simple Ontology Editor Plug-In – OntOMat SOEP, an ontology editor that is extended with rule -editing capabilities.SOEP provides structure as well as vocabulary, i.e. lexical layer of the domain ontology, for therule creation. Although these rules are related to domain ontology, they are not treated as axiomsin that ontology. The ontological axioms should be always-true statements, which is not the casefor expert rules. SOEP saves rules directly in the RDFRule format. b) Legacy rule-bases are very valuable sources of sharable knowledge, which can be consulted insolving some problems, either for free or for some price. The focus is not on collaborative problem solving via querying the federation of 1 http://kaon.semanticweb.org,http://ontobroker.semanticweb.orgrule bases, but in the creating high-specific expert bases, byimporting relevant (for the given task) rule chains from those rule bases. The prerequisite is tohave a mechanism to convert legacy rule-bases into rule -interlingua. The potential candidate for the common-accepted rule markup is RuleML [4]. We plan to use our OntOMat-REVERSE, thetool which translates the content of a relational database into an ontology represented in the RDF,for the support of this translation into RDFRule.c) Metadata dispread on the web should be the primary knowledge source for sharing knowledgein the future. In order to make that sharing more efficient some mechanisms for knowledge packaging and knowledge trading/pricing are needed [5]. Our OntOMat-CRAWL, ontology-focused and metadata-aware crawler already has the capabilities to collect web documents(metadata) that fit the given “knowledge” model, so that the adaptation to rule-crawling isstraightforward.  d) The previous three types are related to formally stated knowledge, which can be processed bymachine agents. Knowledge in the documents is informally represented, but the content of adocument can be formally stated by ontologybased indexes. The underlying process is calledsemantic annotation and it is supported by our OntOMat annotation framework [6]. By usingsome information extraction (IE) techniques it is possible to make annotation semi-automatically. For example, we plan to extend OntOMat with techniques for the extraction of thetabular content in order to convert tabular information into a set of rules automatically. 2. Knowledge repository is a relational database organised in a way that enables efficient storingand access to RDF metadata. This repository can be seen as a RDF repository. 3. 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 4. Knowledge sharing is realised by searching for rules that satisfy the query conditions. In theRDF repository rules are represented as reified RDF statements and while in RDF any statementis considered to be an assertion, we can view an RDF repository as a set of ground assertions inthe form (subject, predicate, object). Rules are also related to domain ontology, which containsdomain axioms used for deriving new assertions. Therefore the searching is realised as aninferencing process. We use Ontobroker, main memory, deductive, object oriented databasesystem, which inferences using RDF inputs also. To note that our system treats facts and queriesas rules without the rule body and the rule head, respectively. This facility enables using theSOEP editor as a query interface. 5. Using of the knowledge is related to our semantic web-enabled knowledge portals scenarios[3], [7]. The main advantage of our approach is using a conditional statement for the semanticannotation of knowledge sources. In that   way we put statements used in the annotation into thecontext of each other, which consequently leads to efficient searching for knowledge. Moreover,annotating knowledge resources using Precondition-Action statements enables 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. In that wayquerying 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. 3. CONCLUSION In this paper we presented an application framework for realizing a knowledge managementsystem on the Semantic Web. The proposed framework is mainly based on our existing SemanticWeb tools. The approach introduces two new aspects, which could enhance applicability of theSemantic Web tools in real-world applications: (i) rules as the first class citizens on the SemanticWeb and (ii)   semantic annotation by using conditional statements. The benefits of the proposedapproach are manifold: an integration platform for various rules sources and rules format, more precise search for knowledge sources by using conditional statements, machine-processabledescription of the content of the tabular- and graphic-based resources, a possibility to composevarious knowledge sources in solving some rare difficult tasks. 4. REFERENCES 1. T. Berners-Lee, J, Handler, O Lassila. The Semantic Web, Scientific American , May, 20012. N. Stojanovic, Lj. Stojanovic. Searching knowledge in the Semantic Web, accepted for FLAIRS 2002, Special track on Semantic Web3. S. Staab, H.-P. Schnurr, R. Studer, Y. Sure: Knowledge Processes and Ontologies. IEEEIntelligent Systems. 16(1)
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