A HYBRID METHOD FOR DOMAIN ONTOLOGY CONSTRUCTION

FROM THE WEB

B. Frikh

, A. S. Djaanfar

and B. Ouhbi

B.P. 1796 Atlas F`es, Ecole Sup´erieure de Technologie, Route d’imouzer, F`es, Morocco

Laboratoire LISQ, Facult´e des Sciences, Dhar El Mahraz, F`es, Morocco

Ecole Nationale Sup´erieure d’Arts et M´etiers, Marjane II, B´eni M’Hamed, B.P. 4024, Mekn`es, Morocco

Keywords:

Domain ontology, CHIR-statistic, Mutual information, Hybrid approach, Web mining.

Abstract:

This paper describes a hybrid statistical and semantic relationships among model concepts for ontology con-

struction. The implementation of the model, called HCHIRSIM (Hybrid Chir-Statistic and Similarity), can

be adapted to any domain ontology learning from the Web. It can be viewed as a combination of information

from inference view of concepts by using the CHIR-statistic method and the semantic relationships among

concepts from the Web by the mutual information measure. The experiments show that our hybrid approach

outperforms both purely statistical and purely semantic relationships among concepts approaches. The suc-

cessful evaluation of our method with different values of the weighting parameter shows that the proposed

approach can effectively construct a cancer domain ontology from unstructured text documents.

1 INTRODUCTION

The goal of a domain ontology is to reduce(or elim-

inate) the conceptual and terminological confusion

among the members of a virtual community of users

who need to share electronic documents and infor-

mation of various kinds. This is achieved by iden-

tifying and properly deﬁning a set of relevant con-

cepts that characterize a given application domain.

The vision of the World Wide Web as a huge reposi-

tory of machine-processable information may be re-

alized in different ways. The ﬁrst one is to relay

on the large-scale use of semantic annotations that

refer to entities deﬁned in a formal ontologies lan-

guage such as RDF (RDF, 2004). The other one is

to try to automatically ”re-construct” the knowledge

presented in (unstructured) web documents. Several

Web mining and information extraction techniques

have been proposed to automate this task (Craven

et al., 2000), (Etzioni et al., 2005), (Petasis et al.,

2003), (Sanchez and Moreno, 2004), (Frikh et al.,

2009). The techniques employed by different systems

in ontology construction or ontology learning, may

vary depending on the tasks to be accomplished. The

techniques can generally be classiﬁed into statistics-

based, linguistics-based, logic-based, or hybrid. The

various statistics-based techniques for accomplishing

the tasks in ontology learning are mostly derived from

information retrieval, machine learning and data min-

ing. Some of the common techniques include clus-

tering (Wong et al., 2006), latent semantic analysis

(Turney, 2001), co-occurrence analysis (Budanitsky,

1999), term subsumption (Fotzo and Gallinari, 2004),

contrastive analysis (Velardi et al., 2005) and associ-

ation rule mining (Strehl, 2002). The main idea be-

hind these techniques is that the extent of occurrence

of terms and their contexts in documents often pro-

vide reliable estimates about the semantic identity of

terms.

Co-occurrence analysis is usually coupled with

some measures to determine the association strength

between terms or the constituents of terms. Some of

the popular measures include dependency measures

(e.g. mutual information (Church and Hanks, 1990)),

log-likelihood ratios (Resnik, 1999) (e.g. chi-square

test), rank correlations (e.g. Pearsons and Spearmans

coefﬁcient (Strehl, 2002)), distance measures (e.g.

Kullback-Leiber divergence (Maedche et al., 2002)),

and similarity measures (e.g. cosine measures (Senel-

lart and Blondel, 2003)).

Some of the common relevance measures from

information retrieval include the Term Frequency-

Inverse Document Frequency (TF-IDF) (Salton and

Buckley, 1988) and its variants, and others based on

language modeling (Croft and Ponte, 1998) and prob-

ability (Fuhr, 1992).

285

Frikh B., S. Djaanfar A. and Ouhbi B..

A HYBRID METHOD FOR DOMAIN ONTOLOGY CONSTRUCTION FROM THE WEB.

DOI: 10.5220/0003667502850292

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 285-292

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

In these approaches, the assumption normally made

is that words that co-occur with sufﬁcient frequency

in the documents of a collection are in fact related

to each other. Our approach is different in that our

technique considered an hybrid approach. This new

statistical data can describe the term category depen-

dency more accurately than the statistics used in the

paper of Sanchez and Moreno (Sanchez and Moreno,

2003), since CHIR keeps only terms relevant to the

categories. The main assumption in their work is

that words that are near to the speciﬁed keyword are

closely related. To relax this hypothesis, we introduce

a hybrid statistical and semantic relationships among

model concepts for ontology construction. The imple-

mentation of the algorithm, called HCHIRSIM (Hy-

brid chir-statistic similarity), can be adapted to any

domain ontology learning from the Web. It extended

an earlier work of the authors (Djaanfar et al., 2010)

in the sense that it can be viewed as a combination of

information from inference view of concepts by us-

ing the CHIR-statistic method and the semantic rela-

tionships among concepts from the Web by the mu-

tual information measure. The experiments show that

our hybrid approach outperforms both purely statis-

tical and purely semantic relationships among con-

cepts approaches. The successful evaluation of our

method with different values of the weighting param-

eter shows that the proposed approach can effectively

construct a cancer domain ontology from unstruc-

tured text documents. The paper is organized as fol-

lows. Based on the CHIR-statistic and the similarity

measure, we ﬁrst propose our hybrid model, give then

technical details of the HCHIRSIM-algorithm, and ﬁ-

nally present evaluation results for different values of

the weighting parameter.

2 DEFINITION OF THE MODEL

Motivated by the performance of text clustering by

selecting the words that help to distinguish the docu-

ments into different clusters (Li et al., 2008) and the

fact that two terms are considered similar if their mu-

tual information with all terms in the vocabulary are

nearly the same (cf. (Brun et al., 2002)), we propose

a hybrid model that is able to identify and properly

deﬁne a set of relevant concepts that characterize a

given application domain then captures the semantic

relationships among concepts from the Web.

2.1 Term Selection based on the CHIR

Statistic

To measure the degree of dependency between a term

and a speciﬁc category, the χ

statistic tests the hy-

pothesis that the term and the category are statistically

independent of each other. The χ

statistic is deﬁned

as:

w,c

∑

(O(i, j) − E(i, j))

E(i, j)

, (1)

where O(i, j) is the observed frequency of the docu-

ments that belong to category j and contain w. E(i, j)

is the expected frequency of category j and term i.

In text mining and information retrieval studies, the

statistic has been used for feature selection (Salton

and Buckley, 1988). As we have said in the intro-

duction the CHIR statistic is an extended variant of

the χ

statistic for term-category independence test to

measure the degree of dependency between a term w

and a category C of documents. This method can im-

prove the performance of text clustering by selecting

the words that help to distinguish the documents into

different clusters (Li et al., 2008). Despite to the χ

statistic, the CHIR statistic select only relevant terms

that have strong positive dependency on certain cat-

egories in the corpus and remove the irrelevant and

redundant terms. The new term-category dependency

measure R

w,c

is deﬁned by:

w,c

O(w, c)

E(w, c)

, (2)

where O(w, c) is the number of documents that are in

the category c and contain the term w and E(w, c) is

the expected frequency of the category c to contain

the term w. If there is positive dependency then the

observed frequency should be larger than 1. If there

is negative dependency, R

w,c

should be smaller than

1. In the case of the no-dependency between the term

w and the category c, the term-category dependency

measure R

w,c

should be close to 1. When R

w,c

is larger

than 1, the dependency between w and c is positive,

otherwise, the dependency is negative. To get better

informationabout the dependency between a term and

a category, (Li et al., 2008) use a combining formula

of χ

w,c

and R

w,c

and deﬁne the term goodness of a

term w in a corpus with m classes as:

(w) =

∑

j=1

p(R

w,c

)χ

w,c

with R

w,c

> 1,

(3)

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286

where

p(R

w,c

) =

w,c

∑

j=1

w,c

with R

w,c

> 1,

(4)

is the weight of χ

w,c

in the corpus in terms of R

w,c

A bigger r

(w) value indicates that the term is more

relevant.

When there is positive dependency between the

term w and the category c

, this new term-goodness

measure is the weighted sum of χ

w,c

2.2 Term Similarity Metric

The most widely measure used, in statistics and in-

formation theory, is the mutual information. In mu-

tual information, the co-occurrence frequencies of the

constituents of complex terms are utilized to measure

their dependency. Two terms are considered similar if

their mutual information with all terms in the vocabu-

lary are nearly the same cf. (Brun et al., 2002). In this

paper, the metric we use for measuring the similar-

ity of two terms is that given by (Dagan et al., 1999).

This similarity measure is deﬁned by:

Sim(w, w

′

) =

2|V|

|V|

∑

i=1

(

min(I(z

, w), I(z

, w

′

)

max(I(z

, w), I(z

, w

′

)

min(I(w, z

), I(w

′

, z

)

max(I(w, z

), I(w

′

, z

)

), (5)

where V is the vocabulary and I(z

, w) is the mutual

information of terms z

and w. We use it to identify

terms which are semantically relevant in a domain on-

tology construction. This measure is based on the mu-

tual information calculated for a window of d terms.

It’s nature is essentially semantic than syntactic. The

mutual information is deﬁned as follows:

I(z

, w) = P

, w)log

, w)

P(z

)P(w)

, (6)

where d is the withdrawal, P(z

) and p(w) are the

a priori probability of term z

and w respectively.

P(z

, w) is the probability of succession of terms z

and w in the window observation. This probability

can be estimated by ratio of the number of times that

is followed by w within the window and by the car-

dinal of the vocabulary.

P(z

, w) =

, w)

|V|

, (7)

where f

, w) is the number of times that z

followed by w.

2.3 The Hybrid Proposed Model

The main objective behind the proposed model is to

propose a hybrid model that is able to identify and

properly deﬁne a set of relevant concepts that char-

acterize a given application domain then captures the

semantic relationships among concepts from the Web.

The basic idea is to reorder the retrieved concepts

based on the hybrid model. It comes from the follow-

ing observations. For a domain application start with

a keyword, w

rep

that has to be representative enough

for the domain. The similarity between the initial

keyword w

rep

and a candidate concept in the Web is

measured based on the weighting model combining a

component estimated from the CHIR-statistic and one

from the similarity measure by linear interpolation:

S(w) = λ ∗ r

(w) + (1− λ)sim(w,w

rep

), (8)

where λ is a weighting parameter between 0 and 1.

This can be viewed as a combination of information

from inference view of concepts by using the CHIR-

statistic method and the semantic relationships among

concepts from the Web by the mutual information

measure. Concepts with the highest similarities are

returned as the retrieval results. Since relevant con-

cepts convey semantically similar information with

respect to the initial keyword, it is likely that previous

Web pages have already judged them as co-relevant

through relevance feedback. Therefore, it is reason-

able to assume that concepts having strong score are

likely to be relevant concepts to the initial keyword.

With each retrieved concepts a new keyword is con-

structed joining the new concept and the initial one.

This process (cf. Figure1) is repeated recursively un-

til a selected depth level is achievedor no more results

are found.

3 HCHIRSIM ALGORITHM

In this section, the proposed algorithm used to dis-

cover and select representative concepts and websites

for a domain and construct the ﬁnal ontology is

described. This algorithm is based on analyzing a

large number of web sites in order to ﬁnd important

concepts for a domain by introducing an initial

keyword. The candidate concepts are processed in

order to select the most adequate ones by performing

HCHIRSIM analysis. The selected classes are ﬁnally

incorporated into the ontology. For each one, the

main websites from where it was extracted are stored,

and the process is repeated recursively in order to

ﬁnd new terms and build a hierarchy of concepts.

This algorithm is described by the following steps:

A HYBRID METHOD FOR DOMAIN ONTOLOGY CONSTRUCTION FROM THE WEB

287

• Perform a k-means clustering algorithm on the set

of all documents and get initial clusters.

• Start with a keyword that has to be representative

enough for the domain and a set of parameters

that constrain the search and the concept selection

(cf.(Djaanfar et al., 2010)).

• Extract all the candidate concepts by analyzing

the neighborhood of the initial keyword; select the

anterior words and posterior words as candidate

concepts.

• For each candidate concept, calculate its score

S(w) measure by using (8).

• Sort the terms in descending order of their S(w)

measure.

• Select the top l terms from the list.

• The l concepts extracted are incorporated as

classes or instances in the taxonomy and the

URLs, from where they are extracted, are stored.

• For each concept incorporated in the taxonomy, a

new keyword is constructed joining the new con-

cept and the initial one. This process is repeated

recursively until a selected depth level is achieved

or no more results are found.

• Finally, a reﬁnement process is performed in or-

der to obtain a more compact taxonomy and avoid

redundancy.

Figure 1: Taxonomy building methodology.

4 ONTOLOGY BUILDING AND

REPRESENTATION

Ontologies consist of a set of classes representing the

categories of the entities of interest in a domain and

the relationships between those entities. In doing so,

an ontology can be used to capture what it means to be

one of those entities; that is, the semantics of the do-

main. In our study, we use a number of tools for ontol-

ogy development. These include: the Web Ontology

Language(OWL), a commonly used language for ex-

pressing ontologies, and Prot´eg´e-OWL, a leading on-

tology engineering environment,Lucene 3.0.1(a high-

performance, full-featured text search engine library

written entirely in Java. It is a technology suitable for

nearly any application that requires full-text search,

especially cross-platform), HTML Parser 2.0, Stem-

mers 1.0( suitable for the morphologicroot in English

language), Jena 2.6.3(Framework Java).

4.1 Ontology Representation

The ﬁnal ontology is edited by ”Prot´eg´e 4.1” in or-

der to evaluate its precision then it is saved in OWL

format. Its content is entirely written in the standard

OWL language representation (OWL, 2004). The ﬁ-

nal domain ontology ( the vocabulary of a domain and

a set of constraints on how terms can be combined to

model the domain), is presented to the user in a re-

ﬁned way and can be interrogated by the user.

4.2 Application

As an illustration application, we choose to use ”can-

cer” as the initial keyword. The program was exe-

cuted on a collection of 52758 documents, indexed

from 26 web sites of this domain. The maximum

depth level has been ﬁxed to 5. For each query, we

select the top 11 terms from the list. The 11 concepts

extracted are incorporated as classes or instances in

the taxonomy and the URLs from where they are ex-

tracted are stored. The length of the window is ﬁxed

to 4. The resulting taxonomy is formally correct. We

present in Figure 2, a part of the obtained ontology.

The numbers represent the number of instances in the

classes.

5 MEASUREMENT METHODS

AND RESULTS

Due to the complex nature of ontologies, evaluation

approaches can also be distinguished by the layers of

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

288

Figure 2: Cancer taxonomy visualized on Prot´eg´e 4.1: num-

bers are class identiﬁers.

an ontology (e.g. term, concept, relation) they eval-

uate (Porzel and Malaka, 2004). More speciﬁcally,

evaluations can be performed to assess the (1) cor-

rectness at the terminology layer, (2) coverage at the

conceptual layer, (3) wellness at taxonomy layer, and

(4) adequacy of the non-taxonomic relations. The fo-

cus of evaluation at the terminology layer is to de-

termine if the terms used to identify domain-relevant

concepts are included and correct. Some form of lex-

ical reference or benchmark is typically required for

evaluation in this layer. Typical precision and recall

measures from information retrieval are used together

with exact matching or edit distance (Maedche and

Staab, 2002) to determine performance at the termi-

nology layer. The lexical precision and recall reﬂect

how good the extracted terms cover the target domain.

Lexical Recall (LR) measures the number of relevant

terms extracted e

relevant

divided by the total number of

relevant terms in the benchmark b

relevant

, while Lex-

ical Precision (LP) measures the number of relevant

terms extracted e

relevant

divided by the total number of

terms extracted e

all

. LR and LP are deﬁned as (Sabou

et al., 2005):

LP =

relevant

all

, (9)

LR =

relevant

, (10)

The precision and recall measure can be also com-

bined to compute the corresponding F

-score. The

general formula for a non-negative real β is:

(1+ β

)precision× recall

× precision+ recall

, (11)

Evaluation measures at the conceptual level are con-

cerned with whether the desired domain-relevant con-

cepts are discovered or otherwise. Lexical Overlap

(LO) measures the intersection between the discov-

ered concepts (Cd) and the recommended concepts

(Cm). LO is deﬁned as:

LO =

| Cd

Cm |

| Cm |

, (12)

Ontological Improvement (OI) and Ontological Loss

(OL) are two additional measures to account for

newly discovered concepts that are absent from the

benchmark, and for concepts which exist in the

benchmark but were not discovered, respectively.

They are deﬁned as (Sabou et al., 2005):

OI =

| Cd − Cm |

| Cm |

, (13)

OL =

| Cm−Cd |

| Cm |

, (14)

Evaluations at the taxonomy layer is more com-

plicated. Performance measures for the taxonomy

layer are typically divided into local and global. The

similarity of the concepts positions in the learned

taxonomy and in the benchmark is used to compute

the local measure. The global measure is then

derived by averaging the local scores for all concept

pairs. One of the few measures for the taxonomy

layer is the Taxonomic Overlap (TO) (Maedche

and Staab, 2002). The computation of the global

similarity between two taxonomies begins with the

local overlap of their individual terms. The semantic

cotopy, the set of all super- and sub-concepts, of a

term varies depending on the taxonomy. The local

similarity between two taxonomies given a particular

term is determined based on the overlap of the terms

semantic cotopy. The global taxonomic overlap is

then deﬁned as the average of the local overlaps of all

the terms in the two taxonomies.

In order to evaluate our methodology, we have

used the MeSH Browser. ”MeSH is the National

Library of Medicine’s controlled vocabulary the-

saurus. It consists of sets of terms naming descriptors

in a hierarchical structure that permits searching at

various levels of speciﬁcity. The MeSH Browser is an

online vocabulary look-up aid available for use with

MeSH (Medical Subject Headings). It is designed

to help quickly locate descriptors of possible interest

and to show the hierarchy in which descriptors of

interest appear. Virtually complete MeSH records

A HYBRID METHOD FOR DOMAIN ONTOLOGY CONSTRUCTION FROM THE WEB

289

are available, including the scope notes, annotations,

entry vocabulary, history notes, allowable qualiﬁers,

etc. The browser does not link directly to any

MIDLINE or other database retrieval system and

thus is not a substitute for the PUBMED system.

The MeSH Browser points to the newest version of

MeSH and so it will also ﬁnd new Supplementary

Concepts as these are added and updated weekly.

The MeSH Browser may be used to ﬁnd descriptors,

qualiﬁers, or Supplementary Concepts of interest

and see these in relationship to other concepts. The

browser is part of the MeSH Web pages. It ﬁnds

descriptors of interest without assuming knowledge

of the often complex vocabulary structure and rules”

(Mesh, 2010).

We have submitted the query ”cancer” to the

MeSH Browser. For each candidate concept, we

have extracted all related entries then stocked it in a

text ﬁle. To exploit the resulted concepts, we have

decomposed them in simple terms. All terms under

three characters were rejected. Then we extracted the

morphological root of the rest terms. This different

extracted morphological roots are then considered

as relevant terms for the benchmark lexical. The

total, 881 different morphological roots were used to

recover the domain cancer. We have then conducted

the evaluation. For the number of terms retained

in the ontology construction, we use concepts and

instances. To evaluate F

-score, we take β equal to 1.

Results are presented in tables Table.1- Table.5.

Table 1: Performance of HCHIRSIM algorithm in terms of

precision, recall, lexical overlap, ontological improvement

and ontological loss for λ = 0.5.

λ = 0.5 MeSH Documents collection

Selected 881 827

Rejected 463 102

Total 1344 929

LR 93,87%

LP 89,02%

91,38%

LO 86,83%

OI 7,04%

OL 13,17%

Table 1 summarizes the performance of the

HCHIRSIM algorithm for λ = 0.5 Our new measure

achieved a 93,87%, 89,02% for recall, precision, re-

spectively. One will notice that our new algorithm

achieved a lexical overlap of 86,83% and ontologi-

cal improvement of 7,04%. However the ontological

loss is 13,17%. This validate the performance of the

HCHIRSIM algorithm for λ = 0.5.

5.1 Experimental Results

In order to determine the best value for the weighting

parameter of our HCHIRSIM model, we have conduct

experiments, under the same conditions as in the pre-

vious subsection, on the values of the weighting pa-

rameter λ. Concretely, we use λ ∈ {0, 0.2, 0.5, 0.8, 1}.

Results are presented in tables 1− 5:

Table 2: Performance of HCHIRSIM algorithm in terms of

precision, recall, lexical overlap, ontological improvement

and ontological loss for λ = 0.

λ = 0 MeSH Documents collection

Selected 881 614

Rejected 463 213

Total 1344 827

LR 69,69%

LP 74,24%

71,90%

LO 66,97%

OI 2,72%

OL 33,03%

Table 3: Performance of HCHIRSIM algorithm in terms of

precision, recall, lexical overlap, ontological improvement

and ontological loss for λ = 0.2.

λ = 0.2 MeSH Documents collection

Selected 881 809

Rejected 463 113

Total 1344 922

LR 91,83%

LP 87,74%

89,74%

LO 85,36%

OI 6,47%

OL 14,64%

Table 4: Performance of HCHIRSIM algorithm in terms of

precision, recall, lexical overlap, ontological improvement

and ontological loss for λ = 0.8.

λ = 0.8 MeSH Documents collection

Selected 881 796

Rejected 463 117

Total 1344 913

LR 90,35%

LP 87,19%

88,74%

LO 84,34%

OI 6,02%

OL 15,66%

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290

Table 5: Performance of HCHIRSIM algorithm in terms of

precision, recall, lexical overlap, ontological improvement

and ontological loss for λ = 1.

λ = 1 MeSH Documents collection

Selected 881 528

Rejected 463 256

Total 1344 784

LR 59,93%

LP 67,35%

63,42%

LO 57,89%

OI 2,04%

OL 42,11%

5.2 Discussion

Taking λ = 1 corresponds to the Chir-Statistic tech-

nique which is an improved version of the chi-statistic

test (Resnik, 1999). While taking λ = 0 corre-

sponds to the mutual information dependency mea-

sure (Church and Hanks, 1990). Instead of only

exploiting mutual information measure or only us-

ing statistic technique or sequential method given by

(Djaanfar et al., 2010) which is time consuming, we

use a hybrid measure. The performance evaluation

is conducted by setting different weights. The ex-

perimental results are presented in tables 1-5. In

general, the hybrid method yields better performance

than statistics-based or linguistics-based. More-

over, the performance of the hybrid method for the

weight λ = 0.5 (LR = 93, 87%, LP = 89, 02%, F

91, 38%, LO = 86, 83%, OI = 7, 04%, OL = 13, 17%)

is much higher than that of the other weights.

6 CONCLUSIONS

This paper presented a hybrid method combining sta-

tistical and semantic approaches for automating the

ontology construction process by retrieving and ex-

tracting data from Web resources. The obtained algo-

rithm called HCHIRSIM can be adapted to any do-

main ontology learning from the Web. The exper-

iments show that our hybrid approach outperforms

both purely statistical and purely semantic relation-

ships among concepts approaches. The successful

evaluation of our method with different values of

the weighting parameter shows that the proposed ap-

proach can effectively construct a cancer domain on-

tology from unstructured text documents.

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