Semantic (Web) Technology In Action: Ontology Driven Systems for Search, Integration and Analysis

1. Semantic (Web) Technology In Action: Ontology Driven Systems for Search, Integration and Analysis Written by: Amith Sheth & Cartic Ramakrishnan (IEEE bulletin, Dec 2003) Presented by: Didit

2. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

3. Introduction • Semantic web enable the web to understand and satisfy the request of people • Considered as the next phase of web infrastructure development • How viable the web semantic is?

4. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

5. Semantic Web • Provide standard based interoperability of application • Semantic web vision • Representing knowledge • Acquiring knowledge • Utilizing knowledge • Scalability is the key challenge

6. Semantic Web • Closely supported by web service and web process • Skepticism • related to lofty goal of the semantic web • related to scalability issue

7. Semantic Web • Scalability issue related to • Large ontology creation and maintenance • Large semantic annotation • Inference mechanism • Database technology play role in web semantic • Involving in complex query processing

8. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

9. Semantic Technology Application Nowadays • Semantic search engine, Taalee (Semagic) [Townley 2000] • Provide domain specific search and contextual browsing • Extracting audio, video, and text content from 250 sources • Semantic integration, Equity Analyst Workbench [sheth et al 2002] • Provide text content from site aggregated from 90 international source • Provide an integrated access to heterogeneous content. • Content are classified into small taxonomy and domain specific metadata are automatically extracted

10. Semantic Technology Application Nowadays • Analytics and knowledge discovery, Passenger Threat Assessment [sheth et al 2004] • Knowledge base is populated from many public, licensed and proprietary knowledge source • Periodic metadata extraction from heterogeneous data is performed

11. Semantic Technology Application Nowadays • Web semantic: review from existing application • Application validate the importance of ontology in the current approach • Ontology population is critical • Named entity and semantic ambiguity resolution is important for data quality problem • Semi-formal ontologies (based on limited expressive power) are most practical and useful • Large scale metadata extraction and semantic annotation is possible

12. Semantic Technology Application Nowadays • Web semantic: review from existing application (continue) • Support for heterogeneous data is important factor • Ability to query both ontology and metadata to retrieve heterogeneous content is highly valuable • A majority of semantic web application development rely on ontology creation, semantic annotation, and querying

13. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

14. Ontology • Ontologies come in bewildering variety • Informal • Semiformal (do not use formal semantic, the ontology populated in incomplete knowledge) • Formal • Semiformal ontology demonstrated very high practical value in term of development effort

15. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

16. Scalability Aspect in Ontologies • Avalibility of large and useful ontologies • Must resemble as a database schema and capture some aspect of real world semantic Can be done through: • Social process (a suggestion and revision) • Automatically extract the ontology schema from content • Automatic population of the knowledge base (with respect to human design ontology schema)

17. Scalability Aspect in Ontologies • Semantic metadata extraction of massive content • Annotating heterogeneous content is very challenging The key to face this challenge is • Large scale availability of domain specific ontologies • Scalable technique to annotate content

18. Scalability Aspect in Ontologies • Inference mechanism that scale • Should can deal with massive number of assertion • Deal with decidability and complexity issue by limiting expressiveness of knowledge representation

19. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

20. Semiformal, Less Expensive Ontology • Semiformal ontology more abundant and useful than formal ontology • Can be built to a scale that is useful in real world application • Can accommodate partial and possibly incomplete information • The more semantic consistency required by standard, the sharper the tradeoff between complexity and scale

21. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

22. Semagix Freedom: an Example of Semantic technology • Provide modeling tool to design ontology schema based on the application requirement • Exploits task and domain ontologies that are populated with relevant facts • Perform • automatic classification of content • Ontology driven metadata extraction • Support complex query processing (semantic search, semantic integration, and analytic knowledge discovery)

23. Semagix Freedom: an Example of Semantic technology

24. Semagix Freedom: an Example of Semantic technology • Knowledge agent maintain ontology automatically by traverse trusted source periodically • Content agents have similar duty as knowledge agents

25. Semagix Freedom: an Example of Semantic technology • Semantic enhancement server enhanced incoming content • identifying relevant document feature • Perform entity disambiguation • Tag metadata with relevant knowledge • Produce semantically annotated content • Support two form content processing • Automatic classification (utilize classifier committee based on statistical, learning, and knowledge based classifier) • Metadata extraction (involve named entity identification and semantic disambiguation)

26. Semagix Freedom: an Example of Semantic technology • Metabase store both semantic and syntactic metadata related to content • Semantic query server provide index of metabase so that retrieval of metada can be done quickly

27. Semagix Freedom: an Example of Semantic technology • Performance capability • Typical size of an ontology schema: 10s of classes, 10s of relationship, few hundred properties type • Average size of ontology population: over a million of named entities • Number of instance that can be extracted in a day: up to a million per server per day

28. Semagix Freedom: an Example of Semantic technology • Performance capability • Number of text document that can be processed for metada extraction: hundreds of thousand to a million per server per day • Performance for search engine type keyword queries: over 10 million queries per hour • Query processing requirement in an analytical application: approx 20 complex queries, taking total of 1/3 second for computation

29. Outline • Introduction • Semantic Web • Semantic Technology Application Nowadays • Ontology • Scalability Aspect in Ontologies • Semiformal, Less Expensive Ontology • Semagix Freedom: an Example of Semantic technology • Conclusion

30. Conclusion • Formal ontologies even though supported by deductive inference mechanism may not be the primary mean of addressing major challenge in semantic web vision • Semantic web vision is not one of solving AI problem • instead it can make critical contribution to the semantic web (on issue in supporting heterogeneous data, semantic ambiguity, complex query processing, semiformal ontology, and ability to scale large amount of information)