a semi-automatic ontology acquisition method for the semantic web man li, xiaoyong du, shan wang...

A Semi-automatic Ontology Acquisition Method for the Semantic WebMan Li, Xiaoyong Du, Shan WangRenmin University of China, BeijingWAIM 2005

4 May 2012SNU IDB Lab.

Hye Chan, Bae

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Outline Introduction SOAM Case Study Conclusion Discussion

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Introduction The Semantic Web aims to add

– Semantics– Better structure to the information

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Introduction Success of Semantic Web depends on

– The proliferation of ontologies– Pay more attention to the construction of ontologies

How do I constructthe ontology?

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Introduction Manual development of ontologies still remains a tedious and

cumbersome task

How to acquire ontologyautomatically or semi-automatically

from existing resources?

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Introduction A large amount of data about various domains are organized and

stored in relational database

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Introduction SOAM

– Semi-automatic Ontology Acquisition Method– Based on data in relational database– Balance the cooperation between user contributions and machine learning

Acquire ontology directly by using a group of rules Refine ontology according to lexical knowledge repositories

(semi-automatically)

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SOAM overview

Step4:Acquire ontological instances based on refined ontological structure

Step3:Refine the obtained ontological structure

Step2:Acquire ontological structure according to the database schema information

Step1:Capture the information about relational database schema

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SOAM overview

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Acquiring Ontological Structure Prior assumption

– Relational schema is at least in 3NF

We have 11 rules for acquiring ontological structure!!

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Acquiring Ontological Structure

Rule 1

R1

A1

A2

A3

R2

A1

A4

R3

A1

A5

A6

Ri

A1

A2

A3

A4

A5

A6

Class Ci

Equivalence


Rule 2

Ri

A1

A2

A3

12

Ri

A1

A2

A3

A4Rj

A3

A5

A6

Class Ci


Rule 2

13

Ri

A1

A2

R2

A2

A5

Class Ci

R1

A1

A3

A4

14


Rule 3

Ri

A1

A2

A3 Rj

A3

A4

A5

Class Ci

Class Cj

A3

Inclusion dependency

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Rule 4

Ri

A1

A2

A3

A4

Rj

A2

A3

A5

Class Ci

Class Cj

is-p

art-o

fhas-p

art

-of

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Rule 5

Rk

A1

A2

Rj

A2

A5

Ri

A1

A3

A4

Class Ci

Class Cj

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Rule 6

Rl

A1

A2

A3Rj

A2

A6

Ri

A1

A4

A5

Class Ci

Class CjRk

A3

A7

Class Ck

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Rule 7

Ri

A1

A2

A3

Class Ci

String String Number

Datatype property

A1 A

2

A3

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Rule 8

Ri

A1

A2

A3

Rj

A1

A4

A5

Inclusion dependency

Class Ci

Class Ci

su

bcla

ss-o

f

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Rule 1 (ref.)

Ri

A1

A2

A3

Rj

A1

A4

A5

Equivalence

Class Cj

Ri

A1

A2

A3

A4

A5

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Rule 9, 10, 11

Ri

A1

A2

A3

Class Ci

A1

minCardinality=1maxCardinality=1

NOT NULL : minCardinality = 1UNIQUE : maxCardinality = 1

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Refining Ontological Structures The obtained ontological structure is coarse Refining obtained ontology according to machine-readable

– dictionaries– thesauri

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Refinement algorithm The basic idea

1. A user wants to refine a concept in the ontology2. The algorithm can help him find some similar lexical entries3. The user can refine the concept according to the information

to REFINE

Con-cepts

k most similarlexical entries

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Similarity measures Lexical similarity

– Edit distance method is used (LSim) Similarity in conceptual level

– Considers the similarity about Super-concepts (SupSim) Sub-concepts (SubSim)

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Case Study

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Conclusion Gives a semi-automatic ontology acquisition method

– Based on data in relational database

Future work– Apply our approach in other domains– Do some researched on acquiring ontology from other resources

Natural language text XML And so on

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Discussion Strong point

– More practical rules for real data in relational database?– Refinement using lexical repositories

Weak point– No example

Hard to understand the rules fully– Need to understand more about ontology languages

OWL

Thank you!!!

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a semi-automatic ontology acquisition method for the semantic web man li, xiaoyong du, shan wang...

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