department of information management chaoyang university of technology networking and intelligent...

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Department of Information Management Chaoyang University of Technology

Networking and Intelligent Computing Lab

Semantic web role and its method: Domain

ontology

Rung Ching Chen ( 陳榮靜 )

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黃鶴樓　崔灝　　昔人已乘黃鶴去，此地空餘黃鶴樓。黃鶴一去不復返，白雲千載空悠悠。晴川歷歷漢陽樹，芳草萋萋鸚鵡洲。日暮鄉關何處是，煙波江上使人愁。

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黃鶴樓送孟浩然之廣陵李白

眼前有景道不得，崔顥題詩在上頭

故人西辭黃鶴樓，煙花三月下揚州。孤帆遠影碧空盡，惟見長江天際流。

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Outline

Introduction

Literature reviews

Ontology application

Ontology construction

Experimental results

Conclusions and current research

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IntroductionIntroduction

Background

Motivation

Objective

Literature reviews



Conclusions and future works

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Background (1/2)The content of web sites changes rapidly and grows very fast

How to understand querist’s needs and how to find related web pages from the Internet are very important.

Yahoo vs. Google

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Background (2/2)The main drawback of current search engines is that they can’t read the real semantic of the web page content. They don’t use the domain specific knowledge for web page analyses.

The concept of Semantic Web has been proposed recently.

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MotivationSemantic web and ontology

The construction of successful semantic web depends on whether the ontology can be constructed rapidly and easily.

Most of the research on ontology construction is determined by domain experts. It is difficult to modify the concepts of an existed domain ontology for a semantic web.

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Objective

A large number of ontology representation methods have been proposed.

we use the hierarchical tree structure to represent the domain ontology because it is the most general one .

Methods of construct ontologyManual construction

Semi-automatic construction

full-automatic construction

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Literature reviewsIntroductionLiterature reviews

Semantic webOntology Information classification modelSingle value decompositionAdaptive resonance theory network

Ontology constructionExperimental resultsConclusions and future works

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Semantic web (1/2)Drawbacks of existing network

The information is presented in documents.

It is unable to process or extract the information that people actually need.

Semantic web is an extension of the existing network structure

Provide a new foundations of data description.

Promotional development network service automatically.

Make the information understandable to machines.

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Semantic web (2/2)Builds the high-level languages on low-level languages progressively.

Offers the information that the computer can read without revising the existing webpage content.

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Ontology (1/4) The W3C has defined ontology as knowledge for describing and expressing various domains using concepts, definitions, and relations.

Ontology usually appears in the form of semantic web.

A node represents a concept or an individual entity on the semantic web.

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Ontology (2/4)Gruber definition “An ontology is a formal, explicit specification of a shared conceptualization”

Conceptualization: a certain existing phenomenon or the relevant abstract model of concept of the definite phenomenon in the field.

Share: ontology is shared by a group, not an individual.

Formal: ontology can be read and understood by computers.

Explicit: the concept form and restriction of ontology can be expressed in clear way.

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Ontology (3/4)Gruber thought the elements of ontology include:

Concept: Concept can be used to represent any thing in the real world. It is usually organized as a tree structure in ontology.

Relation: Relation means the connection between concepts of the certain types.

Function: Function is a special case for Relation.

Axiom: The axiom is used to model the fact.

Instance: The instance is the appearance of concretized concept.

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Ontology (4/4)Ontology language is extended from the XML (Extensible Markup Language) syntax.

It is responsible for W3C to formulate and renew.

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Domain Ontology Applications

Grigoris Antoniou

Frank van Harmelen

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1. Horizontal Information Products at Elsevier

2. Data Integration at Audi

3. Skill Finding at Swiss Life

4. Think Tank Portal at EnerSearch

5. E-Learning

6. Web Services

7. Other Scenarios

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Elsevier – The SettingElsevier is a leading scientific publisher.

Its products are organized mainly along traditional lines:

Subscriptions to journals

Online availability of these journals has until now not really changed the organisation of the productline

Customers of Elsevier can take subscriptions to online content

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Elsevier – The ProblemTraditional journals are vertical products Division into separate sciences covered by distinct journals is no longer satisfactory Customers of Elsevier are interested in covering certain topic areas that spread across the traditional disciplines/journalsThe demand is rather for horizontal products

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Elsevier – The Problem (2)

Currently, it is difficult for large publishers to offer such horizontal products

Barriers of physical and syntactic heterogeneity can be solved (with XML)

The semantic problem remains unsolved

We need a way to search the journals on a coherent set of concepts against which all of these journals are indexed

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Elsevier – The Contribution of Semantic Web

Technology

Ontologies and thesauri (very lightweight ontologies) have proved to be a key technology for effective information access

They help to overcome some of the problems of free-text search They relate and group relevant terms in a specific domain They provide a controlled vocabulary for indexing information

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Elsevier – The Contribution of Semantic Web Technology (2)

A number of thesauri have been developed in different domains of expertise

Medical information: MeSH and Elsevier’s life science thesaurus EMTREE

RDF is used as an interoperability format between heterogeneous data sources

EMTREE is itself represented in RDF

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Elsevier – The Contribution of Semantic Web Technology (3)

Each of the separate data sources is mapped onto this unifying ontology

The ontology is then used as the single point of entry for all of these data sources

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Information classification model

There are three traditional information classification models:

Vector space model

Probabilistic model

Boolean model

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Vector space and probabilistic model

Vector space model:The element represents the number of keywords that appear in a document. The cosine similarity method is used to find the related web pages.

Probabilistic model:This model uses a probabilistic approach to evaluate the relationships among web pages and to judge whether they are related.

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Boolean modelIt is the simplest categorized method, which is based on set theory and Boolean algebra. Boolean model can be divided into three relations: inheritance, intersection and independence

intersection

inheritance

Concept A

Concept B

Concept A

Concept B

Concept A

Concept B

independence

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Single Value Decomposition (1/2)

Row represents documents and column indicates keywords.

Whether a keywords appears in a document is represented as an element.

documents

keywords

M × N

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Single Value Decomposition (2/2)

Latent Semantic Analysis, LSA project document and keywords to a low dimension.

Using Singular Value Decomposition, SVD to remove unnecessary information.

kS

k

kk

= **

t

td kSkV

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Adaptive resonance theory network (1/3)

ART network is an unsupervised learning network

Principle:The theory of ART grew from the theory of cognition.

It is similar to a human neural system. Not only does it learn new examples, but also preserves old memories.

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Characteristic:It has the features of both stability and plasticity. In order to resolve the antinomy of stability and plasticity, the ART network adjusts the vigilance value.

Advantage:The learning speed is quick.The consumption memory space is small.Does not have beforehand to establish the group number.

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The structure of the ART network:Input layer: The input data is training samples.

Output layer: This presents the results of the trained network.

Weight connections: This connects the input layer and the

output layer Output vector

1X 2X nX

1Y 2Y mY

Input vector

Output layer

Input layer

Connection layer

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Ontology constructionIntroduction

Literature reviews

Ontology construction Analyzing web pages

Finding the TF-IDF values of terms

Reducing the matrix and transfer elements to duality data

Using a recursive ART network to cluster the web pages

Applying a Boolean model to construct an ontology

Representing the ontology using a Jena package



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Ontology constructionWWW

Document

Web pages analysis

Finding TF-IDF

SVD operation

ART networkfor cluster

Use TF-IDF to find the

concept of each group

Whether satisfied low document

Boolean method

Create ontology

Produce RDF

ontology

Stop-word

Construct

relation

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Analyzing web pages (1/2)

After collect web page, the system removes stop words.

Stop words can avoid wrong judgment when there are some non-important words but appear the frequency to be high.

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Analyzing web pages (2/2)

Most web pages are written in HTML. HTML uses open/closed tags to indicate web page commands.

Tij = nij × Wm

Tij: expressed concept Cj appears in web page di weight.

nij: expressed concept Cj the frequency which appears under the different tag.

Wm: expressed the weight of tag.

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TF-IDFOur research uses the product of TF and IDF to represent the importance of a keyword in the document.

TFi,j’:it is the term relative to the frequency of keyword i in a document j after weight operation.

IDFi: it is the inverse document frequency of term i, that is the reciprocal of appear frequency of term i in all document.

N: is the number of all documents

ni: is the number of appearances of term i in the number of documents N.

)log()(

'_,

,,

iji

jiijiij n

N

tfMAX

tfIDFTFIDFTF

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Reducing the matrix and transfer elements to

duality data We list out the keyword and webpage documents to make a duality matrix.

If the keywords appear in the documents, the keyword is set to 1; if not, it is set to 0. The SVD operation is used to reduce the large matrix to a small one

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Using the recursive ART network to cluster the

web pages We propose a recursive ART network algorithm to produce a tree structure

ART

50 Doc.

30 Doc.

20 Doc.

ART ART ART

25 Doc. 25

Doc.

20 Doc. 10

Doc.

5 Doc. 15

Doc.

100 Doc.

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Recursive ART

1

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Recursive ART

1

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Applying Boolean operation

The Boolean model is used to modulate and construct the relation between different concepts.

For example, imagine ten documents involving four types of concepts: Transports, flying, boats, and airplanes.

Documents containing “transports”: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.

Documents containing “fly”: 2, 3, 6, 7, 9, 10.

Documents containing “boat”: 1, 4, 5, 8.

Documents containing “airplane”: 6, 7, 10.

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Generating ontology through the Jena package (1/3)

A Resource description framework (RDF) is a framework developed by W3C and metadata groups.

It is able to carry several metadata while roaming on the Internet.

RDF provides interoperability between applications that exchange machine-understandable information on the web

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Describe Web resource dataResource ： anything that have URI

Description ： describe property of resource

Three main elementsSubject

Predicate

objectSubject Object

Predicate

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A given problem may be represented by a meaning graph of the RDF

where the URI is a web resource and author is a property with the value “John

http://www.cyut.edu.tw/~s9214639/ Johnauthor

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Experiments

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Experimental resultsExperiment environment

Pentium-4 2.4G

512MB RAM

JAVA program language

RDF ontology language

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Experimental resultsIntroduction

Literature reviews


Experimental resultsFirst stage experiment

Second stage experiment


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First stage experimentWe select a musical instrument ontology constructed by an expert for semi-automatic experiment.

We use the keywords of the existing domain ontology to produce a new ontology provided by our method.

After the new ontology has been created, we compare the new ontology with the expert ontology to demonstrate the precision of our method.

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Data (1/2)Ontology

http://www.db-net.aueb.gr/thesus/onto/instrum.rdf

52 concepts

“has” and “sub-class” relations

DataCollected Web pages on “Music/Instruments/” domain.

There are 36 catalogs in that domain.

518 Web pages.

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Data (2/2)

Category Number Category Number Category Number Category Number

Instrument 15 Lute 5 Gong 2 Woodwind 2

Synthesizer

5 Bass 32 Accordion 44 Bassoon 8

Stringed 3 Cello 9 Brass 17 Clarinet 12

Percussion 9 Viola 5 Horn 14 Flute 13

Wind 6 Violin 20 Saxophone 25 Oboe 12

Banjo 26 Mandolin 24 Trombone 11 Panpipes 3

Guitar 24 Piano 19 Trumpet 29 Piccole 5

Harp 20 Bell 3 Tuba 6 Recorder 26

Harpichord 14 Drums 33 Harmonium 6 Harmonica 14

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Mark matrixAfter analyses web pages, the column denotes keywords, the row represents web documents. If the keyword can be found in the web document, it will be set to ‘1’, otherwise it will be set to ‘0’.

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Recursive ART (1/2)The recursive ART network will check whether the output values are greater than the vigilance. We test the vigilance step-by-step from 0.1 to 0.9 with an increment of 0.1.

group

0

10

20

30

40

50

60

70

80

90

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

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Recursive ART (2/2)The clustering of the ART network results in 78 groups.

we calculated the keywords TF/IDF values for each group, using the highest value to represent the keyword of the group.

Each group generates a representative keyword, deleting identical representative keywords among different groups, and then leaving only 40 keywords.

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group Key-term group Key-term

1 Drum 21 Trumpet

2 Pinched 22 Viola

3 Bass 23 Tuba

4 Harp 24 Clarinet

5 Mandolin 25 String

6 Piccolo 26 Wind

7 Harmonica 27 Trombone

8 Piano 28 Flute

9 Harpsichord 29 Woodwind

10 Violin 30 Bell

11 Guitar 31 Brass

12 Cymbal 32 recorder

13 Accordion 33 Gong

14 Oboe 34 Panpipes

15 Cello 35 Battery

16 Lyre 36 Tambourine

17 Instrument 37 Triangle

18 Percussion 38 Harmonium

19 Synthesizer 39 Bassoon

20 Saxophone 40 banjo

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Output ontologywe obtain a 5-level ontology from the 40 candidate nodes by Boolean logic level operations.

I nstrument

Stri ngsynti esi zer percussi on

wi nd

pinched

viola

bass

mandolin

violin

cello

piano

tamboari ne cymbal battery drum bel l gong tri angl e

guitar lyreHarpsi -chord

harp

banj o

harwoni ca harmoni umwoodwi nd accordi on brass

oboe

cl ari net fl ute pi ccol opanpi pes recorder

bassoon

trumpet

Trom-bone

Saxo-phone

tuba

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Evaluation (1/5)After producing the ontology, we will compared this new ontology with the expert-defined ontology.

Precision and recall rate are then used to evaluate our ontology.

In order to estimate the precision of the system, we defined two kind of precision evaluation methods.

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Evaluation (2/5)Concept precision demonstrates the precision of the keywords the system selects.

Concept_location precision not only demonstrates the precision of the selected keywords but also shows the precision of the location i

n the hierarchy relations.

Precision (C_P)= Precision (C_L_P) =

Recall (R) =

)()(

)(

BNAN

AN

)()(

)(

DNCN

CN

)()(

)(

ENAN

AN

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Evaluation (3/5)

Expert-Definedconcepts

ConceptsNotdefined byexpert

Expert-defined, right location

Expert-Definedlocation inerror

Keywordsgenerated bysystem

A B C D

Keywordsnot generatedby system

E

Expert

concepts

System keywords

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Experts defined ontology

The ontology of the musical instrument domain generated by the experts.

I nstrument

Stri ng synti esi zerpercussi on wi nd

rubed stri ked

pi nched

vi ol a bass mandol i n vi ol i n cel l o

pi ano

tamboari ne bongo cymbal battery tomtom drum bel l tymmpan Xyl o-phone gong tri angl ecastanets

gui tar l ute l yre Harpsi c-hord harp banj o

El ectri cal -gui tar

Accousti c-gui tar

harwoni ca harmoni um barel woodwi nd accordi onbrass

oboe cl ari net fl ute pi col o panpi pes

recorderbassoon

trumpetTrom-bone horn Saxo-

phone

tuba

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Evaluation (4/5)

Expert-Definedconcepts

ConceptsNotdefined byexpert

Expert-defined, right location

Expert-Definedlocation inerror

Keywordsgenerated bysystem

40 0 29 11

Keywordsnot generatedby system

12

Expert

concepts

System keywords

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Evaluation (5/5)When compared with the ontology defined by an expert, the experimental results indicate our proposed method

Precision (C_P) 100% concept precision.

Precision (C_L_P) 73% concept hierarchy precision.

recall rate of 77%,

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Second stage experiment (1/2)

We selected the beer domain and collected web pages from the Internet. There are 18 catalogues, 212 web pages.

CatalogueNumberOf web pages

CatalogueNumberOf web pages

ale 26 pilsner 7

beer 36 microbrewery 4

bitter 6 hop 23

brewery 26 festival 10

larger 14 bock 5

liquid 2 bitter 6

yeast 6 ingredient 11

stout 11 organization 5

porter 7 award 7

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Second stage experiment (2/2)

The system selected 1,688 noun terms from the 6,914 input terms. The system then calculated higher TF-IDF to obtain useful keywords from the 1,688 terms.

We also constructed a matrix in which the column denotes ontology keywords while the row represents web documents.

If the keyword can be found in the web document, it will be set to ‘1’; otherwise, it will be set ‘0’.

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keyword TF-IDF value keyword TF-IDF value

ale 0.91 fermentation 0.617

association 0.89 grist 0.61

award 0.88 kraeusen 0.61

beer 0.872 mash 0.61

bitter 0.81 maltose 0.6

bock 0.81 pasteurization 0.6

brewery 0.81 wort 0.6

festival 0.80 cask 0.6

hop 0.77 firkin 0.59

ingredient 0.72 exchanger 0.58

lager 0.71 adjunct 0.58

liquid 0.70 dme 0.57

malt 0.70 hops 0.57

microbrewery 0.698 malt 0.57

organization 0.698 yeast 0.56

pilsner 0.69 alcoholic 0.56

porter 0.69 aroma 0.56

shout 0.68 astringent 0.56

yeast 0.66 bitter 0.55

dope 0.66 diacetyl 0.55

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dunker 0.66 esters 0.55

farmhouse 0.66 grainy 0.52

hefeweizen 0.66 happyhours 0.51

helles 0.658 skunked 0.5

kolsch 0.65 oxidation 0.5

lager 0.65 phenolic 0.5

lambic 0.64 yeasty 0.49

maibock 0.64 brewpub 0.483

marzen 0.63 camre 0.47

mead 0.62 breweriana 0.47

mild 0.62 rauchbier 0.46

munchener 0.62 saison 0.4

pilsener 0.51 steinbier 0.4

pilsner 0.51 stout 0.4

pils 0.51 vienna 0.4

porter 0.51

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Recursive ART (1/2)The recursive ART network will check whether the outputvalues are greater than the vigilance. We test the vigilancestep-by-step from 0.1 to 0.9 with an increment of 0.1.

group

0

5

10

15

20

25

30

35

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

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Recursive ART (2/2)The clustering performed by recursive ART network yields 29 groups.

group documents

group Documents

1 26 16 8

2 17 17 9

3 22 18 8

4 23 19 2

5 23 20 6

6 9 21 6

7 2 22 7

8 17 23 8

9 8 24 6

10 6 25 8

11 7 26 9

12 12 27 8

13 6 28 4

14 4 29 4

15 8

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Output ontologyIn this manner, Each group generates a representative keyword, deleting identical representative keywords among different groups, and then leaving only 13 keywords. Boolean logic is used to

calculate relationships between levels of concepts.

Yeast

festi val brewery association award

fermentation

hop

maltmead

beer

ale

stoutporter

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Evaluate (1/2)After producing the ontology, its precision must be evaluated. However, there was no another ontology to compare with. So we invited domain experts to evaluate its precision.

Identifies the term

Does Not identify the term

Identifies the term and location is right

Identifies the term but location is in error

The system generates the concepts

A B C D

User view

of the terms

System terms

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Evaluate (2/2)Precision (C_P)=

Precision (C_L_P) =

The average Precision (C_P) of domain experts evaluate is 0.794 (almost 79%), and the average Precision (C_L_P) of domain experts evaluate is 0.742 (almost 74%).

)()(

)(

BNAN

AN

)()(

)(

DNCN

CN

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RDF formatFinally, we used the W3C standard for ontology web languages to record the ontology, and outputted the results in a Jena package using an RDF format.

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Conclusions (1/2)Ontology can help user to learn and search related information effectively. Constructing an ontology fast and correctly has become an important topic for content based search on the Internet.

Our proposed method does require less time to select keywords and to define the relations automatically with human intervention.

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Conclusions (2/2)The proposed method facilitates users understanding of the content of data and its relevancy, and is able to suggest content that is highly relevant.

In the future, we will focus on investigations a better method for finding multi-relations among terms, and extend the system’s abilities to cover a multi-field ontology as the foundation for robust and accurate ontology constructing.

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Current ReasearchSensors Network Intrusion Detection.

Ontology application on Medical Knowledge

Ontology merging and alignment

Using applied soft computing to solve problems

Web pages analysis

Image processing

RFID Application

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Thanks for listening!