1. Semantic Web

2. P2 Introduction Information management facilities not keeping pace with the capacity of our information storage. –Information Overload –haphazardly created hierarchical directory structure –diversity of information formats and access methods –Information cannot be shared (stovepipe system) –Poor Content Aggregation Using Semantic Web to improve knowledge management.

3. P3 The future of the Web -- Tim Berners-Lee A more collaborative medium. Understandable, and thus processable, by machines.

4. P4 Need RDF Additional meta data is needed for machines to be able to process Information on the Web.

5. P5 From Applications to Data With the Web, Extensible Markup Language (XML), and now the emerging Semantic Web, the shift of power is moving from applications to data. The path to machine-processable data is to make the data smarter.

6. P6 Smart Data Continuum

7. P7 Smart Data Continuum Text and databases (pre-XML). Most data is proprietary to an application. Thus, the “smarts” are in the application and not in the data. XML documents for a single domain. Data achieves application independence within a specific domain. Data is now smart enough to move between applications in a single domain.

8. P8 Smart Data Continuum Taxonomies and documents with mixed vocabularies. In this stage, data can be composed from multiple domains and accurately classified in a hierarchical taxonomy. In fact, the classification can be used for discovery of data. Simple relationships between categories in the taxonomy can be used to relate and thus combine data. Thus, data is now smart enough to be easily discovered and sensibly combined with other data.

9. P9 Smart Data Continuum Ontologies and rules. In this stage, new data can be inferred from existing data by following logical rules. In essence, data is now smart enough to be described with concrete relationships, and sophisticated formalisms where logical calculations can be made on this “semantic algebra.” This allows the combination and recombination of data at a more atomic level and very fine-grained analysis of data. Thus, in this stage, data no longer exists as a blob but as a part of a sophisticated microcosm. An example of this data sophistication is the automatic translation of a document in one domain to the equivalent (or as close as possible) document in another domain.

10. P10 Definition Semantic Web: a machine processable web of smart data. Smart data: data that is application- independent, composeable, classified, and part of a larger information ecosystem (ontology).

11. P11 XML XML is the syntactic foundation layer of the Semantic Web. All other technologies providing features for the Semantic Web will be built on top of XML. Requiring other Semantic Web technologies (like the Resource Description Framework) to be layered on top of XML guarantees a base level of interoperability.

12. P12 XML The technologies that XML is built upon are Unicode characters and Uniform Resource Identifiers (URIs). The Unicode characters allow XML to be authored using international characters. URIs are used as unique identifiers for concepts in the Semantic Web.

13. P13 XML is not enough XML only provides syntactic interoperability. –sharing an XML document adds meaning to the content; however, only when both parties know and understand the element names. Ex: $12.00 $12.00

14. P14 Web Services Web services are software services identified by a URI that are described, discovered, and accessed using Web protocols. Web services consume and produce XML. Web services fit into the Semantic Web by –furthering the adoption of XML, or more smart data. –solve the Web service discovery problem. –enabling Web services to interact with other Web services.

15. P15 Next Trend The next big trend in Web services will be semantic-enabled Web services, where we can use information from Web services from different organizations to perform correlation, aggregation, and orchestration. Research programs –TAP at Stanford bridging the gap between disparate Web service-based data sources and "creating a coherent Semantic Web from disparate chunks.“ enables semantic search capabilities, using ontology-based knowledge bases of information.

16. P16 Semantic Web services

17. P17 What's after Web Services Logical assertions Classification Formal class models Rules Trust

18. P18 Logical assertions An assertion is the smallest expression of useful information. One way is to model the key parts of a sentence by connecting a subject to an object with a verb. RDF captures these associations between subjects and objects. Example: –HP’s RDF processing software “Jena” –Adobe's Extensible Metadata Platform “XMP”

19. P19 Formal class models A formal representation of classes and relationships between classes to enable inference requires rigorous formalisms even beyond conventions used in current object- oriented programming languages like Java and C#. Ontologies are used to represent such formal class hierarchies, constrained properties, and relations between classes. The W3C is developing a Web Ontology Language (abbreviated as OWL).

20. P20 Ontology Example

21. P21 Rules The Semantic Web can use information in an ontology with logic rules to infer new information. –If a person C is a male and childOf a person A, then person C is a "sonOf" person A. –If a person B is a male and siblingOf a person A, then person B is a "brotherOf" person A. –If a person C is a "sonOf" person A, and person B is a "brotherOf" person A, then person B is the "uncleOf" person C.

22. P22 Trust By allowing anyone to make logical statements about resources, smart applications will only want to make inferences on statements that they can trust. Verifying the source of statements is a key part of the Semantic Web.

23. P23 In the Future "By 2005," the Gartner Group reports, "lightweight ontologies will be part of 75 percent of application integration projects."

24. P24 What Is the Semantic Web Good For? Decision Support Business Development Information Sharing and Knowledge Discovery Administration and Automation

25. P25 Semantic Web Technologies

26. P26 Enterprise Efforts Adobe is reorganizing its software meta data around RDF, and they are using Web ontology-level power for managing documents. Because of this change, "the information in PDF files can be understood by other software even if the software doesn't know what a PDF document is or how to display it.“ IBM is making significant investments in Semantic Web research. Germany's Ontoprise are making a business out of ontologies, creating tools for knowledge modeling, knowledge retrieval, and knowledge integration.