unifying data and domain knowledge using virtual views ibm t.j. watson research center lipyeow lim,...

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

Copyright 2008 by CEBTCenter for E-Business Technology

Background

DBMS originally designed for transaction data.

Current DBMSs are not ready to manipulate data in con-nection 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/infer-encing

IDS Lab. Seminar - 2


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.




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.




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 Has-Color for any given wine.



Domain Knowledge from OWL Ontol-ogy

Wine Ontology from the web ontology language OWL (W3C)

Extract class hierarchies, (transitive) properties, implications from OWL



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 ?



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.



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 on-tology.



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



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’;



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



Ontology Repository

Ontology repository extracts several types of information from the ontology files including class hierarchies, implication rules, transitive properties.

Class Hierarchies

subClassOf

isA


Transitive Property

TransitiveProperty

Implication rules

Implication graph


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;



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



Experiment

Implication Graph Density

Size of transitive property trees



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.



Conclusion

Framework for putting a little semantics into relational SQL systems.

Users register ontologies in DBMS and links them with re-lational data by creating virtual views.

Virtual columns in the virtual views are not materialized.

Queries on the virtual columns are rewritten to predi-cates on base table columns.

Future work: performance issues


unifying data and domain knowledge using virtual views ibm t.j. watson research center lipyeow lim,...

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