1. TARTAR Information Extraction Transforming Arbitrary Tables into F-Logic Frames with TARTAR Aleksander Pivk, York Sure, Philipp Cimiano, Matjaz Gams, Vladislav Rajkovic, Rudi Studer Presented By Stephen Lynn

2. TARTAR Information Extraction  Free-form Text  Linguistic/NLP approaches  Tabular Structures  Table comprehension task  html, excel, pdf, text, etc.  Semantic interpretation task  More effort???

3. TARTAR Information Extraction TARTAR Architecture

4. TARTAR Information Extraction Semantic Representation  Frame Logic (F-Logic)  Model-theoretic semantics  Complete resolution-based proof theory  Expressive power of logic  Availability of efficient reasoning tools

5. TARTAR Information Extraction F-Logic Frame

6. TARTAR Information Extraction

7. TARTAR Information Extraction Table Comprehension  Dimensions – a grouping of cells representing similar entities

8. TARTAR Information Extraction Table Comprehension  Stub – dimension with headers used to index elements in body

9. TARTAR Information Extraction Table Comprehension  Box head – column headers (often nested)

10. TARTAR Information Extraction Table Comprehension  Body – data values

11. TARTAR Information Extraction Table Classes  1D, 2D, Complex

12. TARTAR Information Extraction Methodology

13. TARTAR Information Extraction Cleaning & Canonicalization  Clean DOM tree  CyberNeko HTML Parser  Rowspan/Colspan expansion

14. TARTAR Information Extraction Structure Detection  Token Type Hierarchy  Assign Functional Types and Probabilities

15. TARTAR Information Extraction Structure Detection  Detect Logical Table Orientation

16. TARTAR Information Extraction Structure Detection  Discover and Level Regions  Logical Units

17. TARTAR Information Extraction FTM Building  Functional Table Model (FTM)  Arrange regions into a tree  Leaf nodes are data

18. TARTAR Information Extraction Semantic Enriching of FTM  Labeling  WordNet and GoogleSets  Map FTM to a frame

19. TARTAR Information Extraction Evaluation  Crawl, extract, filter web tables  135 tables  85.4% success rate  Mostly problems with complex tables  Compare auto-generated frames with human generated frames  14 people transformed 3 tables each  21 total tables (each done twice)  Syntactic/Semantic correctness (Strict and Soft)

20. TARTAR Information Extraction Results Inter-annotator agreement System-annotator agreement

21. TARTAR Information Extraction Benefits  Fully automated knowledge formalization  Arbitrary tables  Independent of domain knowledge  Independent of document type  Explicit semantics of generated frames  Query answering over heterogeneous tables