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Ontology Generation for Machine-Generated Web Pages

Explore the generation of semantic annotations and ontology from machine-generated web pages. Learn about automatic annotation techniques such as TISP and FOCIH for extracting knowledge from hidden web content. Discover methods for ontology creation and information harvesting.

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Ontology Generation for Machine-Generated Web Pages

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  1. Ontology Generation, Information Harvesting and Semantic Annotation For Machine-Generated Web Pages Cui Tao PhD Dissertation Defense

  2. Motivation • Birth date of my great grandpa • Price and mileage of red Nissans, 1990 or newer • Protein and amino acids information of gene cdk-4? • US states with property crime rates above 1%

  3. Search by Search Engine

  4. “cdk-4" Search the Hidden Web • The Hidden Web: • Hidden behind forms • Hard to query

  5. Query for Data • The Hidden Web: • Hidden behind forms • Hard to query Find the protein and the animo-acids information for gene “cdk-4"

  6. A Web of Pages  A Web of Knowledge • Web of Knowledge • Machine-“understandable” • Publicly accessible • Queriable by standard query languages • Semantic annotation • Domain ontologies • Populated conceptual model • Problems to resolve • How do we create ontologies? • How do we annotate pages for ontologies?

  7. Contributions of Dissertation Work • Web of Pages  Web of Knowledge • Knowledge & meta-knowledge extraction • Reformulation as machine-“understandable” knowledge • Automatic & semi-automatic solutions via: • Sibling tables (TISP/TISP++) • User-created forms (FOCIH)

  8. Automatic Annotation with TISP(Table Interpretation with Sibling Pages) • Recognize tables (discard non-tables) • Locate table labels • Locate table values • Find label/value associations

  9. Recognize Tables Layout Tables (discard) Data Table Nested Data Tables

  10. Find Label/Value Associations Example: (Identification.Gene model(s).Protein, Identification.Gene model(s).2) = WP:CE28918 1 2

  11. Interpretation Technique:Sibling Page Comparison

  12. Interpretation Technique:Sibling Page Comparison Same

  13. Interpretation Technique:Sibling Page Comparison Almost Same

  14. Interpretation Technique:Sibling Page Comparison Different Same

  15. Technique Details • Unnest tables • Match tables in sibling pages • “Perfect” match (table for layout  discard ) • “Reasonable” match (sibling table) • Determine & use table-structure pattern • Discover pattern • Pattern usage • Dynamic pattern adjustment

  16. Table Unnesting

  17. Table Structure Patterns • Regularity Expectations: • (<tr><(td|th)> {L} <(td|th)> {V})n • <tr>(<(td|th)> {L})n • (<tr>(<(td|th)> {V})n)+ • … Pattern combinations are also possible.

  18. Table Structure Patterns <tr>(<(td|th)> {L})n (<tr>(<(td|th)> {V})n)+

  19. Pattern Usage

  20. Dynamic Pattern Adjustment

  21. TISP++ • Automatic ontology generation • Automatic information annotation

  22. Ontology Generation – OSM • Object set: table labels • Lexical: labels that associate with actual values • Non-lexical: labels that associate with other tables • Relationship set: table nesting • Constraints: updates based on observation

  23. Ontology Generation – OWL • Object set: OWL class • Relationship set: OWL object property • Lexical object set: • OWL data type property • Different annotation properties to keep track of the provenance

  24. Generated Ontology

  25. Generated Ontology

  26. RDF Graph

  27. Query the Data Find the protein and the animo-acids information for gene “cdk-4"

  28. TISP Evaluation • Applications • Commercial: car ads • Scientific: molecular biology • Geopolitical: US states and countries • Data: > 2,000 tables in 35 sites • Evaluation • Initial two sibling pages • Correct separation of data tables from layout tables? • Correct pattern recognition? • Remaining tables in site • Information properly extracted? • Able to detect and adjust for pattern variations?

  29. Experimental Results Table recognition: correctly discarded 157 of 158 layout tables Pattern recognition: correctly found 69 of 72 structure patterns Extraction and adjustments: 5 path adjustments and 34 label adjustments  all correct

  30. TISP++ Performance • Performance depends on TISP • TISP test set • Generates all ontologies correctly • Annotates all information in tables correctly

  31. Form-based Ontology Creation and Information Harvesting (FOCIH) • Personalized ontology creation by form • General familiarity • Reasonable conceptual framework • Appropriate correspondence • Transformable to ontological descriptions • Capable of accepting source data • Automated ontology creation • Automated information harvesting

  32. Form Creation

  33. Created Sample Form

  34. Generated Ontology View

  35. Source-to-Form Mapping

  36. Source-to-Form Mapping

  37. Source-to-Form Mapping

  38. Source-to-Form Mapping

  39. Almost Ready to Harvest • Need reading path: DOM-tree structure • Need to resolve mapping problems • Pattern recognition • Instance recognition

  40. Reading Path

  41. Pattern & Instance Recognition

  42. Pattern & Instance Recognition

  43. regular expression for decimal number left context right context Pattern & Instance Recognition

  44. Pattern & Instance Recognition list pattern, delimiter is “,”

  45. Pattern & Instance Recognition list pattern, delimiter is regular expression for percentage numbers and a comma

  46. Pattern & Instance Recognition list pattern, delimiter is regular expression for percentage numbers and a comma

  47. Can Now Harvest

  48. Can Now Harvest

  49. Can Now Harvest

  50. Semantic Annotation

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