Data heterogeneity in the public sector is a serious problem and remains to be a key issue as different naming conventions are used to represent similar data labels. The e-government effort in many countries has provided a platform for government entities and their business partners to exchange data through Information Communication Technologies (ICT) and standards such as RosettaNet (B2B data exchange standard), EDIFACT (Electronic Data Interchange for Administration, Commerce, and Transport), XML (Extensible Markup Language) and EDI (Electronic Data Interchange). However, e-government efforts have not really resolved data heterogeneity problems significantly due to limitation of these standards. One such limitation is the inability of data inheritance. In order to solve this problem with emphasis on Service Oriented Architectures (SOA) and Web Services, a semantically enriched web service for the public sector is needed. Thus we propose an ontology-based solution which allows data inheritance and polymorphism. This goal of this paper is to show how heterogeneous e-government documents can be semantically matched. We propose a shared hierarchical knowledge repository approach and a detailed process methodology for semantic mediation. A two-part semantic mediation approach using SRS (Semantic Relatedness Scores) and SWRL (Semantic Web Rule Language) is highlighted. Both measures are complimentary and provide the semantics necessary for resolving schema heterogeneity. Our approach incorporates a rule-based engine that reads and executes SWRL rules (i.e. RacerPro). We also adopted several tools for proof-of-concept such as Protégé (i.e. ontology editor) and JESS (Java Expert Shell System).
This article introduces a hybrid ontology mediation approach for the Semantic Web. It combines both syntactic and semantic matching measures to provide better results for matching data labels. Although ontologies are meant to provide a shared conceptualization of the world, the development practices, lack of standards, and subjective naming conventions today, create data heterogeneity problems among ontologies. This is a significant problem particularly for ontologies of similar domains. Mediation techniques at present focus mainly on syntactic matching and our premise is that a hybrid approach would be a better solution to this problem. We provide empirical evidence with hypothesis tests and also provide several new measures such as relevance, reliability, and precision to validate our approach. We also introduce a detailed mapping algorithm.
When an organization begins building knowledge-based systems, it has questions. Can knowledge engineering be done by average people? Do we need high-priced knowledge engineers? Do we need new software? Do we need a new system development philosophy? This article contends that the answers to these questions are: yes, no, yes, and yes, respectively. It develops these answers by describing a Knowledge-Based-System Development Life Cycle [KBSDLC] that shows what must be changed and what can be retained from conventional Synchronous Data Link Controls (SDLC).Building computer-based information systems involves some basic tasks: problem detection, identification, and definition; solution definition (functional requirements); system analysis; logical and physical system design; procedure and program design; procedure and program writing; program testing; integrated testing; conversion and installation; and operation. The organization of these tasks may change, but the tasks still must be performed [19, pp. 71-72].Transaction processing systems (TPS], decision support systems (DSS), and knowledge-based systems (KBS), offer different development challenges. TPSs perform routine data processing, are designed around forms, procedures, inputs, and outputs, and often address well-structured problems. Depending on system size, familiarity with the technology, and problem '6^1989 ACM 000]-0782/afl/0'100-04n2/S1.50 structure [11], the uncertainty about project success varies from low to high.System development hfe cycles (SDLCs) originated when most systems were TPSs. Methodologies like Pride, Spectrum, and SDM appeared in the early 1970s. These methodologies and other SDLCs [15, pp, 371-391, 17, 26] are usually variants of Royce's [31] or Boehm's [8] life cycles. One SDLC [15, pp. 371-391] organizes the basic tasks as shown in Figure 1, Integrated testing does not fit into the phases of this SDLC, and the post-audit phase, as important as it is, includes no essential basic tasks.Many organizations execute SDLC phases sequentially, with a sign-off after each phase, an approach that is suitable for many TPSs. This approach assumes that users know their information requirements [1]. that design is straightforward and implementation is the real problem [33], and that sign-offs ensure satisf\ang user specifications. LInfortunately, knowing information requirements in advance is unlikely [5,6,13]; straightforward design is rare, even with TPSs; and sign-offs invite difficulties [12]. Therefore, Royce [31] and Boehm [8] allow for iteration. In practice, however, people resist admitting mistakes [36]; iterating is politically and economically difficult; and people still assume specifications must precede system building.These SDLC shortcomings become more serious with DSSs. DSSs help decision makers address ill-structured problems by using analytical models to manipulate data 482 Communications of the ACM
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