1. Unifying Data and Domain Knowledge Using Virtual Views IBM T.J. Watson Research Center Lipyeow Lim, Haixun Wang, Min Wang, VLDB2007 2008. 1. 4 Summarized by Gong GI Hyun, IDS Lab., Seoul National University

2. Copyright  2008 by CEBT Background  DBMS originally designed for transaction data.  Current DBMSs are not ready to manipulate data in connection with knowledge.  An unending quest Database or Knowledge-base? New applications: the Semantic web, etc.  New extensions are required to bridge the gap between data representation and knowledge representation/inferencing IDS Lab. Seminar - 2Center for E-Business Technology

3. Copyright  2008 by CEBT A Motivating Example  RDBMS allows us to query wines through attributes ID, Type, Origin, Maker, Price.  Human intelligence operates in a quite different way. Humans have ability to combine data with the domain Knowledge. IDS Lab. Seminar - 3Center for E-Business Technology

4. Copyright  2008 by CEBT A Motivating Example  Query 1 To find wines that originate from the US. RDB Case : – Select W.ID From Wine as W Where W.Origin = ‘US’; – Result : No result Human’s answer : Zinfandel – Zinfandel’s Origin EdnaValley is located in California. IDS Lab. Seminar - 4Center for E-Business Technology

5. Copyright  2008 by CEBT A Motivating Example  Query 2 Which wine is a red wine? RDB Case : – Select W.ID From Wine as W Where W.hasColor = ‘red’; – Result : ERROR! – HasColor is not in the schema of the wine table. Human’s Answer : Zinfandel – Zinfandel is red.  Both the user and the DBMS must know what HasColor stands for when it appears in a query, and how to derive the value for HasColor for any given wine. IDS Lab. Seminar - 5Center for E-Business Technology

6. Copyright  2008 by CEBT Domain Knowledge from OWL Ontology  Wine Ontology from the web ontology language OWL (W3C)  Extract class hierarchies, (transitive) properties, implications from OWL IDS Lab. Seminar - 6Center for E-Business Technology

7. Copyright  2008 by CEBT Challenges  How to incorporate domain knowledge (ontology) into a RDBMS? Relational Data model remains ill-suited for semi-structured data.  How to integrate relational data with domain knowledge?  How to query relational data with meaning ?  How to process such queries ? IDS Lab. Seminar - 7Center for E-Business Technology

8. Copyright  2008 by CEBT Overview of our solution  Create a relational virtual view on top of the data and the domain knowledge. Data and knowledge can be queried together. New knowledge can be derived  The virtual view is an interface through which users can query data, domain knowledge, and derived knowledge in a seamlessly unified manner.  Rewrite query on virtual view. IDS Lab. Seminar - 8Center for E-Business Technology

9. Copyright  2008 by CEBT The Virtuality of the View  Users create virtual views over the relational data and the ontology.  Virtual columns/attributes not in original data.  Virtual columns not materialized -- inferred from the ontology. IDS Lab. Seminar - 9Center for E-Business Technology

10. Copyright  2008 by CEBT Creating the Vitual View CREATE VIEW WineView(Id, Type, Origin, Maker, Price, LocatedIn) AS SELECT W.*, R.Regions FROM Wine AS W, RegionKnowledge AS R WHERE W.Origin = R.region IDS Lab. Seminar - 10Center for E-Business Technology

11. Copyright  2008 by CEBT Queries  Query 1 : To find wines that originate from the US Select ID from WineView Where ‘US’ in LocatedIn  Query 2 : Which wine is a red wine?  Select ID from WineView Where hasColor = ‘red’; IDS Lab. Seminar - 11Center for E-Business Technology

12. Copyright  2008 by CEBT Physical Storage Layer  The ontology is modeled as semi-structured data. Traditional RDMSs cannot handle directly.  Hybrid Relational-XML DBMS IBM DB2 9 PureXML supports XML IDS Lab. Seminar - 12Center for E-Business Technology

13. Copyright  2008 by CEBT Ontology Repository  Ontology repository extracts several types of information from the ontology files including class hierarchies, implication rules, transitive properties.  Class Hierarchies subClassOf isA IDS Lab. Seminar - 13Center for E-Business Technology  Transitive Property TransitiveProperty  Implication rules Implication graph

14. Copyright  2008 by CEBT Query Expanding  SELECT V.Id FROM WineView AS V WHERE.hasColor=White; (Type=WhiteWine) → (hasColor=white) (Type=Riesling) → (hasColor=white)  SELECT V.Id FROM Wine AS W WHERE W.type=WhiteWine OR W.type=Riesling; IDS Lab. Seminar - 14Center for E-Business Technology

15. Copyright  2008 by CEBT Experiment  Investigate time to rewrite the queries on virtual views.  Measurement: rewriting time averaged over 5 randomly generated data sets.  Some tweaks : Remove dead nodes Memoization techniques Pre-computation of predicate re-writing IDS Lab. Seminar - 15Center for E-Business Technology

16. Copyright  2008 by CEBT Experiment  Implication Graph Density  Size of transitive property trees IDS Lab. Seminar - 16Center for E-Business Technology

17. Copyright  2008 by CEBT Related Works  Ontology Tools OntoEdit : Use a file system to store ontology. RStar, KAON : Allow the ontology data to be stored in a RDB  Two Limitations of this loosely coupled approach DBMS users cannot reference ontology data directly. Ontology related query processing cannot leverage the query processing and optimization power of a DBMS.  A recent advance in ontology management in DBMSs was introduced by Oracle. IDS Lab. Seminar - 17Center for E-Business Technology

18. Copyright  2008 by CEBT Conclusion  Framework for putting a little semantics into relational SQL systems.  Users register ontologies in DBMS and links them with relational data by creating virtual views.  Virtual columns in the virtual views are not materialized.  Queries on the virtual columns are rewritten to predicates on base table columns.  Future work: performance issues IDS Lab. Seminar - 18Center for E-Business Technology