1. Semantic web Bootstrapping & Annotation Hassan Sayyadi sayyadi@ce.sharif.edu Semantic web research laboratory Computer department Sharif university of technology

2. 2 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

3. 3 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

4. 4 What is annotation? People make notes to themselves in order to preserve ideas that arise during a variety of activities The purpose of these notes is often to summarize, criticize, or emphasize specific phrases or events Semantic annotations are to tag ontology class instance data and map it into ontology classes.

5. 5 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

6. 6 Why use annotation? To have the world knowledge at one's finger tips seems possible. The Internet is the platform for information. Unfortunately most of the information is provided in an unstructured and non- standardized form.

7. 7 Why use annotation? (continue)

8. 8 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

9. 9 Crawler A crawler is a program which traverses the Internet following these links from one page to the next.

10. 10 Focused crawler Not all the Internet knowledge is required for every query. This assumption seems reasonable because most people work on a restricted domain and do not need the knowledge of the whole Internet Searching the whole Internet in this case is very inefficient and expensive. Free texts in the Internet contain various information in diverse domains.

11. 11 Focused crawler (continue) The focus can be achieved by examining keywords Problems: –“Understanding“ the semantic of document –Extremely focusing on one topic Another way to focus is the Internet connectivity structure

12. 12 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

13. 13 Annotation models Mark in web page Example: –SUT is one of the largest engineering schools in the Islamic Republic of Iran – SUT is one of the largest universities in the Islamic Republic of Iran

14. 14 Annotation models (continue) Generate RDF Example: –SUT is one of the largest engineering schools in the Islamic Republic of Iran – university Country

15. 15 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

16. 16 Annotation methods Manually Semi-automatically Automatically

17. 17 Automatic Annotation The fully automatic creation of semantic annotations is an unsolved problem. Automatic semantic annotation for the natural language sentences in these pages is a daunting task and we are often forced to do it manually or semi- automatically using handwritten rules

18. 18 Manual Annotation Manual annotation is more easily accomplished today, using authoring tools, which provide an integrated environment for simultaneously authoring and annotating text. However, the use of human annotators is often fraught with errors due to factors such as annotator familiarity with the domain, amount of training, personal motivation and complex schemas Manual annotation is also an expensive process

19. 19 Semi-automatic Annotation To overcome the annotation acquisition bottleneck, semiautomatic annotation of documents has been proposed.

20. 20 Semi-automatic annotation assumptions: –vocabulary set is limited –word usage has patterns –semantic ambiguities are rare –terms and jargon of the domain appear frequently

21. 21 Semantic Annotation Platform (SAP)

22. 22 Multistrategy SAPs Multistrategy SAPs are able to combine methods from both pattern-based and machine learning-based systems. No SAP currently implements the multistrategy approach for semantic annotation, although it has been implemented in systems for ontology extraction (such as On-To-Knowledge)

23. 23 Semi-automatic annotation (continue) Example –I go to Shanghai Link structure is more like a RDF graph

24. 24 The accuracy of concepts and relations about different algorithm

25. 25 Automatic annotation

26. 26 Source preprocessing Document Object Model (DOM) Text Model Layout Model NLP Model

27. 27 Information Identification Operators –perform extraction actions on document access models –Retrieval, Check, Execute Strategies –build operator sequences according to user time and quality requirements Source Description –build operator sequences according to user time and quality requirements

28. 28 Ontology population The final stage of the overall process is to decide which hypothesis represents the extracted information to insert into the ontology The module simulates insertions and calculates the cost according to the number of new instance creations, instance modifications or inconsistencies found

29. 29 Outline What is annotation? Why use annotation? Crawler Annotation model Annotation methods Our Implementation

30. 30 Our implementation Crawler: –Crawl all link that contains: sharif.ir sharif.edu sharif.ac.ir

31. 31 Our implementation Source pre-processing –Html to text text = text.replaceAll("\n", "*_newline_*"); text = text.replaceAll("\\ ", ""); text = text.replaceAll(" ", ""); text = text.replaceAll("\\ ", ""); text = text.replaceAll(" ", " "); text = text.replaceAll("<", "<"); … text = text.replaceAll("\\*_newline_\\*", "\n"); –Additional text = text.replaceAll("\n(\n|| )*\n","."); text = text.replaceAll(",", " and ");

32. 32 Our implementation Information extraction: –JMontyLingua SUT is one of the largest engineering schools in the Islamic Republic of Iran ("be" "SUT" "one" "of largest engineering school" "in Islamic Republic" "of Iran")

33. 33 Our implementation JMontyLingua problem: –SUT has computer, mechanic and electric engineering departments –("have" "SUT" "computer mechanic and electric engineering departments") –("have" "SUT" "computer and mechanic and electric engineering departments")

34. 34 Our inplementation ("be" "SUT" “university" "in Islamic Republic" "of Iran") => ("be" "SUT" “university" "in Islamic Republic of Iran") =>SUT,be,university & SUT,be_in,Islamic Republic of Iran university

35. 35 Any question?