Concept and example of a semantic solution implemented with SQL views to cooperate with users on queries over structured data with independence from database schema knowledge and technology.

1. Enterprise Database Search Component - EDSC DATAFIELD CONTENT WEB LEXICAL SEMANTICS MEANING RELEVANCE CONTEXT POLYSEMY AMBIGUITY SYNONYM HOMONYM DATABASE FEDERATION HETEROGENEOUS LEGACY E-COMM WORD TERM TEXT SQL QUERY INFERENCE ENGINE COOPERATION SEARCH 1 Mario Flecha - 24 November 2005

5. Handle context to cope with....... AMBIGUITY -> Polysemy, Homonymy, Synonymy POLYSEMY TERM MEANING SAME SOUND ONE MEANING T M 1 M 2 M n . . . ONE TERM VARIOUS MEANINGS M T 1 T 2 T n . . . ONE MEANING VARIOUS TERMS HOMONYMY BY PHONETIC CONVERGENGE T 1 T 2 M 1 M 2 HOMONYMY BY SEMANTIC DIVERGENCE T T 1 T 2 M 1 M 2 SAME SOUND PASSAGE TO POLYSEMY T 1 M 1 T 2 M 2 T M 1 M 2 5 Legend: T = Term M = Meaning

6. Handle context to cope with....... SEMANTIC CONSTELLATION (SEMANTIC FIELD) TEACHING ANALPHABETISM STUDY STUDENT ANALPHABET TEACH KNOWLEDGE EDUCATE EDUCATION ALPHABETIZE ALPHABETIZING LEARN APPRENTICESHIP APPRENTICE 6

11. Contextualizing Terms 11 (a) Example: city*Seattle; state*WS; year*1998. The context database, beyond the prefix, keeps processing information for term treatment, like phonetization, words breaking etc Context Prefix * Term = Contextualized Term Lexical Domain

12. Overall Search Component Architecture 12 User’s Application RIM’s Auxiliary Objects Facts Databases - X,Y,Z... And Instances Facts database instance X Contextualized Term (Semantic Knowledge Base) Contextualized Term (Ontology) Database X Instance 1 . . . . . . . . . . . . . . . . . . . . . . . . . . Database X Instance 1 Term 3 . . . . . . . . . . . . term 1 . . . . . . . . . . . . RIM User * Knowledge Acquisition Consultation * User could be a human or software Mediator Mediator Mediator Database Y Database Z Instance 2 Instance N Instance 1 . Instance 2 Instance N . Instance N Database X Instance 1 Term 2 Database X Instance 2 Term 90 Database X Instance 1000 Term 10 Database Y Instance 5 Term 100 Database Y Instance 3 Term 100 term 2 term 3 term 10 term 30 term 100 term 1000 term K Database L Instance 2 Term 2000 Database Z Instance Z Term K Relations and composite Views of RIM Downward Upward User’s Application Answer (set of tuple Ids) Question (set of questions Knowledge Acquisition Methods

13. EXAMPLES CITY NAME SEARCH 13

14. EXAMPLES: LOCATION SEARCH 14 State City Kind of Street Street’s Name Quarter’s Name Did CepDigital find? Did Medi a tor find?    Aníbal Matos * São Pedro ** N SL   St Aníbal Matos São Pedro N SL   Street Aníbal Matos São Pedro N SL   Street Professor Aníbal Matos São Pedro N Y   Street Professor Aníbal de Matos São Pedro N Y   Avenue Prof.Aníbal Matos São Pedro N Y MG Belo Horizonte Street Professor Aníbal de Matos Santo Antônio N Y MG Belo Horizonte Street Prof Anïbal de Matos Santo Antônio N Y MG Belo Horizonte St Professor Aníbal de Matos Santo Antônio Y Y MG Belo Horizonte Street Professor Aníbal de Matos  or S Antônio N N BL BL MG Belo Horizonte Street Professor  or S Antônio BL BL BL SL MG Belo Horizonte Street Anïbal  or S Antônio N SL

15. CONCLUSION 15