Semantic Technologies and Programmatic Access to Semantic Data
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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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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Outline
Introduction
Literature reviews
Ontology application
Ontology construction
Experimental results
Conclusions and current research
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
IntroductionIntroduction
Background
Motivation
Objective
Literature reviews
Ontology construction
Experimental results
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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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Literature reviewsIntroductionLiterature reviews
Semantic webOntology Information classification modelSingle value decompositionAdaptive resonance theory network
Ontology constructionExperimental resultsConclusions and future works
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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Domain Ontology Applications
Grigoris Antoniou
Frank van Harmelen
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Networking and Intelligent Computing Lab
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Ontology construction
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Networking and Intelligent Computing Lab
Information classification model
There are three traditional information classification models:
Vector space model
Probabilistic model
Boolean model
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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Adaptive resonance theory network (2/3)
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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Networking and Intelligent Computing Lab
Adaptive resonance theory network (3/3)
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
Experimental results
Conclusions and future works
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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Recursive ART
1
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
Recursive ART
1
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
Generating ontology through the Jena package (2/3)
Describe Web resource dataResource : anything that have URI
Description : describe property of resource
Three main elementsSubject
Predicate
objectSubject Object
Predicate
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Networking and Intelligent Computing Lab
Generating ontology through the Jena package (3/3)
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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Networking and Intelligent Computing Lab
Experiments
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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
Experimental resultsExperiment environment
Pentium-4 2.4G
512MB RAM
JAVA program language
RDF ontology language
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Networking and Intelligent Computing Lab
Experimental resultsIntroduction
Literature reviews
Ontology construction
Experimental resultsFirst stage experiment
Second stage experiment
Conclusions and future works
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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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Department of Information Management Chaoyang University of Technology
Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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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Networking and Intelligent Computing Lab
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
65
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Networking and Intelligent Computing Lab
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
70
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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
75
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Networking and Intelligent Computing Lab
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