1. Ontology Maintenance with an Algebraic Methodology: a Case Study Jan Jannink, Gio Wiederhold Presented by: Lei Lei

2. Challenges  Obstacle: Autonomy of diverse knowledge sources  Data volatility and amount increases cost  Major challeges: Establish and maintain application specific portion of knowledge sources

3. An Algebraic Approach  Construct virtual knowledge bases geared to a specific application  Use composable operators to transform contexts into contexts  Operators express relevant parts of a source and the conditions using rules  Rules define a valid context transformation

4. On-line Dictionary:Webster  Autonomously maintained to develop a novel thesaurus application  120,000 entries, two million words  Semi-annual updates  Errors and inconsistencies help robustness

5. Target Application  Construct a graph of the definitions to determine related terms, and automatically generate thesaurus entries

6. Related Work  Ontology composition (Wiederhold 1994)  Rule-based approach to semantic integration (Bergamaschi et al. 1999)  Semantic reconciliation (Siegel 1991)  Uschold et al. 1998  Specification morphisms, (Smith 1993)  WordNet system (Miller & al. 1990)  WHIRL (Cohen 1998)  PageRank (Page&Brin 1998)  Latent semantic indexing (Deerwester 1990)  Hypertext authority (Kleinberg 1998)

7. Outline  Algebra Usage Scenario  Background  Context Creation  Ontology Maintainance  Future Work  Conclusion  My Evaluation

8. Typical Algebra Usage Scenario A minimal sufficient set of Linkage between items in different resources

9. Background  Algebraic Operators Canonical unary to establish and refine a context within which the source knowledge meets the application requirement

10. Background(Cont.)  Semantic Context * No global notion of consistency * Defined as objects that encapsulate other objects * Congruity: relevance of source info. to application * Similarity: equivalent and mergeable objects between different sources

11. Rule Language(Cont.)  Allow uninterpreted components of an object to become attributes of the object  Constructors: create new objects  Constructors: generate proxy objects  Editors & convertors: modify the objects

12. Object Model(Cont.)  Subsume existing models  Only objects have an identity to which others can refer  Correspond to XML supplemented with obj. identity  Rich to model complex relationship

13. Context Creation  Summarize Operator (S operator) Transforms source data based on a predicate Create object: Encapsulates & populates Data classification: Groups source into equivalent classes Syntax: (given contexts c1,c2, a matching rule e)

14. Context Creation(Cont.)  1.Predicate e partitions the objects of c1 into n equivalent parts  2. c2 consists of n+1 values: e,s1,s2,…,sn  3.One is an exception class, not match e

15. Example with Webster’s Dictionary  Automatic Thesaurus Extraction from Dictionary

16. Example(Cont.)  Construct a directed graph from definition: 1.Each head word and definition grouping is a node 2.Each word in a definition node is an arc to the node having that head word  Definition from the dictionary data for Egoism

17. Context Creation(Cont.) *Syllable and accent markers in head words *Misspelled head words *Mis-tagged fields *Stemming and irregular verbs(Hopelessness) *Common abbreviations in definitions(etc.) *Undefined words with common prefixes(un-) *Multi-word head words(Water Buffalo) *Underfined hyphenated and compound words Set 99% accuracy in the conversion from data to graph stru.

18. Constructing the Congruity Expression  An object that represents the entire source  Subdivided into chunks One head word One definition  Express congruity relationship between the dictionary and thesaurus application

19. Ontology Maintenance  Context Refinement  Return the ten longest head words of the dictionary

20. Maintaining the Ontology  Changes in source help correct and extend dict.  Maintain statistics with the S operator when extracting the relevant parts of the dictionary Find no longer needed rules  Note which rules no longer needed  A comparison of the terms reveals new errors

21. Future Work  A web based interface to display ArcRank algorithm based on PageRank (http://www-db.stanford.edu/SKC)

22. Conclusion  An on-line dictionary is good test-bed  An algebraic approach improving maintainability  Congruity simplified identification and handling of changes  Use Summarize to define and refine a context that prepare the dictionary data for thesaurus service use

23. My Assessment  Strength * Decouple the selection of congruent parts of the source data *Congruity and similarity measure use algebra rather than single language *Mirror classes using operators of the algebra instead of low level abstract primitives that are difficult to compose  Weakness * Details of ci’=S(ci) are needed *Difficult to grasp the capability of S operator *Accuracy and error accumulation problem *Ambiguous Rules Generation

24. Questions?