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the semantic web a gide to the future of XML, Web Services and Knowledge Management - Daconta M,C.

Daconta M,C. the semantic web a gide to the future of XML, Web Services and Knowledge Management - Wiley publishing , 2003. - 304 p.
ISBN 0-471-43257-1
Download (direct link): thesemanticwebguideto2003.pdf
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Search and Retrieval
Because data is stored in an easily accessible format (Web services) and is associated with an ontology and a taxonomy, retrieval of information is much easier than the haphazard process described in our earlier "typical organization" section. Integration with all Web services in the organization is easy—they all have a SOAP interface, and since all Web services are registered in a corporate Web service registry, it is easy for an application to find what it is looking for. Because all information is linked with an ontology and taxonomy, searches will provide results that otherwise would be unseen. Figure 9.4 provides a view of the types of searches that can be done with such an infrastructure.
Crafting Your Company's Roadmap to the Semantic Web
Web Services with Corporate Ontology and Web Service Registry
Data Searches Search by Association Taxonomy/Classification
Rule-Based Orchestration
Pattern-Based Searches:
Automated Inferences On-Demand Mining
General Data Searches Search by Association Taxonomy Searches Pattern/Event Searches Rule-Based Orchestration Automated Inferences
Figure 9.4 The search and retrieval process.
Chapter 9
Because of the hard work that was done in the discovery and production
process, our search and retrieval process is simpler and provides important
Discovery of knowledge via taxonomies. Because each Web service can be classified in various taxonomies, taxonomic searches can be done across the Web services of an organization. A good example would be, "I'm looking for all Web services classified in the corporate taxonomy as related to 'Coal Mining.'"
Web service-based data searches. Using standard SOAP interfaces, any application can query Web services in the enterprise.
Search by association. Because our data is mapped into an ontology, semantic searches can be made across the entire knowledge base. We have traditionally left associations out of the search equation. This is the newfound power and possibly the killer app of the Semantic Web—mining associations. A good example of such a search would be, "I would like to perform a query on all relatives of the terrorist Mohammad Atta, their closest friends, and their closest friends' friends." In the world of electronic commerce, associations offer additional buying opportunities to customers. For example, if a potential customer searches for a particular machine or commodity, once that product's representation is found in the ontology, its associations can be selectively displayed—as related equipment, components, and services.
Pattern-based searches. Because all data can be semantically linked by relationships in the ontology, patterns that would only be seen in the past—by old data mining techniques that did not directly utilize meaning—can now be dynamically found with semantic searches. An example of such a search would be, "Of all grocery stores listed in our corporate ontology, which stores have had revenue growth combined with an increased demand for orange juice?"
Manual and agent-based searches. Although all of the searches can be manual, software agents can be equipped with rules to continually search the knowledge base and provide you with up-to-the-second results and alerts. An example of such an agent rule-based query would be, "Alert me via pager/email whenever a new document is registered discussing a new computer virus."
Rule-based orchestration queries. Because Web services can be combined to provide modular functionality, rules can be used in order to combine various searches from different Web services to perform complicated tasks. An example of such a query would be, "Find me the lead engineer of the top-performing project in the company. Based on his favorite vacation spot from his response in the Human Resources survey, book him two tickets to that location next week, grant him vacation time, and cancel all of his work-related appointments."
Crafting Your Company's Roadmap to the Semantic Web
Automated inference support. Because the corporate ontology explicitly represents concepts and their relationships in a logical and machine-interpretable form, automated inference over the ontology and its knowledge bases becomes possible. Given a specific query, an ontology-based inference engine can perform deduction and other forms of automated reasoning to generate the possible implications of the query, thus returning much more meaningful results. In addition, of course, the inference engine may discover inconsistencies or even contradictions in the ontology or knowledge bases. As the corporate ontology and the knowledge bases it spans are elaborated over time, more complicated automated reasoning can be performed (for example, induction of new knowledge based on old knowledge, the incorporation of probabilistic techniques). This automated inference itself can be considered a Web service or set of Web services, and utilized by software agents or human users.
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