1. Semi-Automatic Generation of Mini-Ontologies from Canonicalized Relational Tables Chris Hathaway Supported by NSF

2. Introduction Ontologies are an important tool for realizing the vision of the semantic web Major setback - creation and upkeep Created by experts Experts are biased in knowledge, agreement needed Ontologies continually change Some automation is needed

3. Introduction (cont’d) Current attempts at automatic generation of ontologies not successful, because extracted from free-form, unstructured text. A more effective alternative is to extract ontologies from structured data on the web (tables, charts, etc.) TANGO project Part 1: Extract tables from the web Part 2: Define mini-ontologies from tables Part 3: Merge into growing domain ontology

4. Process Overview Start with canonicalized table Generate candidates for: Object Sets Relationship Sets Functional Constraints Inclusion Constraints/Hierarchical Structure Get help from user Choose best candidate for the ontology

5. Example 1: Generate Concepts Create list of candidate concepts (usually column names)

6. Example 1: Generate Concepts Current ontology

7. Example 1: Generate Relationships Decide relationship sets Exponential number of combinations Basic assumption: one main concept relates to all others (attributes) Goal: find central column of interest

8. Example 1: Generate Relationships Look for mapping between one column and title of table

9. Example 1: Generate Relationships Current ontology

10. Example 1: Generate Constraints FDs and Participation Constraints FD definition: X → Y iff (X[i] = X[j]) → (Y[i] = Y[j]) for all row indexes i and j. Unless solid case (two or more same values), only consider FDs from central object to attributes Use heuristics for setting exact participation (0:1,1:*, etc)

11. Example 1: Generate Concepts Numerical values are usually functionally determined by column of interest and have 0:* participation constraint.

12. Example 1: Generate Constraints Completed mini-ontology

13. Example 2: Generate Concepts SubFamily, Group, and SubGroup are generic types Enumerate column values as object sets because less than 5 divisions (recursively)

14. Example 2: Generate Relationships Found mapping of central column of interest to title (Language) Create ISA hierarchy from table structure

15. Example 2: Generate Relationships Current ontology

16. Example 2: Generate Hierarchical Constraints Assign members to each object set for easy calculation Find inclusion dependencies: Union – All members of parents are members of one or more child Intersection (Less common) – Child members are always in both parents Mutual exclusion – Intersection of any two child members is empty.

17. Example 2: Generate Hierarchical Constraints Completed mini-ontology

18. Getting Help from the User Sometimes human intervention is required Effective use of the user’s input will rely on IDS statements: Issue: explains the problem (Ex. No central object was found in the table) Default: describes default behavior (Ex. A new non-lexical object named Object will be created) Suggestion: suggests an action for the user to follow (Ex. Either choose the column central to describing the table, or name the new object set something appropriate)

19. Conclusion Successful transformation from table to ontology Develop a set of rules, assumptions, heuristics, etc. to automate most accurately Greater ease for the ontology creator