Using WordNet for Text Categorization

Transcription

1 16 The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 Using WorNet for Text Categorization Zakaria Elberrichi 1, Abelattif Rahmoun 2, an Mohame Amine Bentaalah 1 1 EEDIS Laboratory, Department of Computer Science, University Djilali Liabès, Algeria 2 King Faisal University, Saui Arabia Abstract: This paper explores a metho that use WorNet concept to categorize text ocuments. The bag of wors representation use for text representation is unsatisfactory as it ignores possible relations between terms. The propose metho extracts generic concepts from WorNet for all the terms in the text then combines them with the terms in ifferent ways to form a new representative vector. The effects of this metho are examine in several experiments using the multivariate chi-square to reuce the imensionality, the cosine istance an two benchmark corpus the reuters newswire articles an the 20 newsgroups ata for evaluation. The propose metho is especially effective in raising the macro-average F1 value, which increase to for the Reuters from an to for the 20 newsgroups from Keywors: 20Newsgroups, ontology, reuters-21578, text categorization, wornet, an cosine istance. Receive April 5, 2006; Accepte August 1, Introuction Text Categorization (TC) is the classification of ocuments with respect to a set of one or more preexisting categories [14]. TC is a har an very useful operation frequently applie to assign subject categories to ocuments, to route an filter texts, or as a part of natural language processing systems. During the last ecaes, a large number of methos propose for text categorization were typically base on the classical Bag-of-Wors moel where each term or term stem is an inepenent feature. The isavantages of this classical representation are: The ignorance of any relation between wors, thus learning algorithms are restricte to etect patterns in the use terminology only, while conceptual patterns remain ignore. The big imensionality of the representation space. In this article, we propose a new metho for text categorization, which is base on: The use of the WorNet ontology to capture the relations between the wors. The use of the multivariate χ 2 metho to reuce the imensionality an create the categories profiles. The originality of this approach lies in merging terms with their associate concepts extracte from the use ontology to form a hybri moel for text representation. In orer to show the positive contribution of this approach, we have performe a series of experiments on the Reuters an 20Newsgoups test collections. WorNet s large coverage an frequent utilization has le us to use it for our experiments. The remainer of this paper is structure as follows. Section 2 presents a brief presentation of WorNet. The architecture of our approach is provie in section 3 with its ifferent stages. Testing an performance analysis compare to the Bag-Of-Wor representation is provie in section 4. Section 5 cites some relate works. The conclusion an future work are provie in section WorNet There exist many ifficulties to surmount to create an effective texts categorization system: the spee of the inexing an research, the inex size, the robustness, the reliability, the effectiveness, etc. But the principal ifficulties encountere in the fiel are those pose by the natural languages themselves. This is why many experiments using linguistic resources an treatments were realize an presente in the literature. The use of knowlege an avance linguistic treatments in the fiel oes not achieve the unanimity in the community. Inee, many experiments seem to show that sometimes the results obtaine instea of improving o egrae. This was not the case of our approach, where we use two of the semantic relations of WorNet: the synonymy an the hyponymy. WorNet is a thesaurus for the English language base on psycholinguistics stuies an evelope at the University of Princeton [11]. It was conceive as a ata-processing resource which covers lexico-semantic categories calle synsets. The synsets are sets of synonyms which gather lexical items having similar significances, for example the wors a boar an a plank groupe in the synset {boar, plank}. But a boar can also inicate a group of people (e.g., a

2 Using WorNet for Text Categorization 17 boar of irectors) an to isambiguate these homonymic significances a boar will also belong to the synset {boar, committee}. The efinition of the synsets varies from the very specific one to the very general. The most specific synsets gather a restricte number of lexical significances whereas the most general synsets cover a very broa number of significances. The organization of WorNet through lexical significances instea of using lexemes makes it ifferent from the traitional ictionaries an thesaurus [11]. The other ifference which has WorNet compare to the traitional ictionaries is the separation of the ata into four ata bases associate with the categories of verbs, nouns, ajectives an averbs. This choice of organization is justifie by psycholinguistics research on the association of wors to the syntactic categories by humans. Each atabase is ifferently organize than the others. The names are organize in hierarchy, the verbs by relations, the ajectives an the averbs by N-imension hyperspaces [11]. The following list enumerates the semantic relations available in WorNet. These relations relate to concepts, but the examples which we give are base on wors. Synonymy: relation bining two equivalent or close concepts (frail /fragile). It is a symmetrical relation. Antonymy: relation bining two opposite concepts (small /large). This relation is symmetrical. Hyperonymy: relation bining a concept -1 to a more general concept -2 (tulip /flower). Hyponymy: relation bining a concept -1 to a more specific concept -2. It is the reciprocal of hyperonymy. This relation may be useful in information retrieval. Inee, if all the texts treating of vehicles are sought, it can be interesting to fin those which speak about cars or motor bikes. Meronymy: relation bining a concept -1 to a concept -2 which is one of its parts (flower/petal), one of its members (forest /tree) or a substance mae of (pane/glass). Metonymy: relation bining a concept -1 to a concept -2 of which it is one of the parts. It is the opposite of the meronymy relation. Implication: relation bining a concept -1 to a concept -2 which results from it (to walk /take a step). Causality: relation bining a concept -1 to its purpose (to kill /to ie). Value: relation bining a concept -1 (ajective) which is a possible state for a concept -2 (poor /financial conition). Has the value: relation bining a concept -1 to its possible values (ajectives) (size /large). It is the opposite of relation value. See also: relation between concepts having a certain affinity (col /frozen). Similar to: certain ajectival concepts which meaning is close are gathere. A synset is then esignate as being central to the regrouping. The relation 'Similar to' bins a peripheral synset with the central synset (moist /wet). Derive from: inicate a morphological erivation between the target concept (ajective) an the concept origin (colly /col) Synonymy in WorNet A synonym is a wor which we can substitute to another without important change of meaning. Cruse [2] istinguishes three types of synonymy: Absolute synonymes. Cognitive synonymes. Plesionymes. Accoring to the efinition of Cruse [3] of the cognitive synonyms, X an Y are cognitive synonyms if they have the same syntactic function an that all grammatical eclaratory sentences containing X have the same conitions of truth as another ientical sentence where X is replace by Y. Example: Convey /automobile The relation of synonymy is at the base of the structure of WorNet. The lexemes are gathere in sets of synonyms ("synsets"). There are thus in a synset all the terms use to inicate the concept. The efinition of synonymy use in WorNet [11] is as follows: "Two expressions are synonymous in a linguistic context C if the substitution of for the other out of C oes not moify the value of truth of the sentence in which substitution is mae". Example of synset: [Person, iniviual, someone, someboy, mortal, human, runk person] Hyponyms /Hyperonyms in Wor Net X is a hyponym of Y (an Y is a hyperonym of X) if: F(X) is the minimal inefinite expression compatible with sentence A is F(X) an A is F(X) implies A is F(Y). In other wors, the hyponymy is the relation between a narrower term an a generic term expresse by the expression "is-a". Example: It is a og It is an animal [2]. A og is a hyponym of animal an animal is a hyperonym of og.

3 18 The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 Categories The ocument to be classifie Generation of the bag of wors Train corpus ( pre-classifie ocuments) The ocument profile Generation of the bag of wors Mapping terms in concepts: The choice of the mapping strategy. The choice of the isambiguation strategy Extraction of hypernyms. WorNet Calculate cosine istances between profiles The Chi-square reuction... The categories profiles Learning Phase Classification Phase Figure1. The suggeste approach. In WorNet, the hyponymy is a lexical relation between meanings of wors an more precisely between synsets (Synonym Sets). This relation is efine by: X is a hyponym of Y if X is a kin of Y is true. It is a transitive an asymmetrical relation, which generates a ownwar hierarchy of heritage for the organization of the nouns an the verbs. The hyponymy is represente in WorNet by the symbol '@', which is interprete by "is-a" or "is a kin of". Example: It is a tree It is a plant. 3. WorNet-Base Texts Categorization The approach suggeste is compose of two stages, as inicate in Figure 1. The first stage relates to the learning phase. It consists of: Generating a new text representation base on merging terms with their associate concept. Selecting the characteristic features for creating the categories profiles. The secon stage relates to the classification phase. It consists on: Weighting the features in the categories profiles. Calculating the istance between the categories profiles an the profile of the ocument to be classifie The Learning Phase The first issue that nees to be aresse in text categorization is how to represent texts so as to facilitate machine manipulation but also to retain as much information as neee. The commonly use text representation is the Bag-Of-Wors, which simply uses a set of wors an the number of occurrences of the wors to represent ocuments an categories [12]. Many efforts have been mae to improve this simple an limite text representation. For example, [6] uses phrases or wor sequences to replace single wors. In our approach, we use a metho that merges terms with their associate concepts to represent texts. To generate a text representation using this metho, four steps are require: Mapping terms into concepts an choosing a merging strategy.

4 Using WorNet for Text Categorization 19 Applying a strategy for wor senses isambiguation. Applying a strategy for consiering hypernyms. Applying a strategy for features selection Mapping Terms into Concepts The process of mapping terms into concepts is illustrate with an example shown in Figure 2. For simplicity, suppose there is a text consisting in only 10 wors: government (2), politics (1), economy (1), natural philosophy (2), life science (1), math (1), political economy (1), an science (1), where the number inicate is the number of occurrences. Key Wors government (2) politics (1) economy (1) naturalphilosophy (2) life science (1) math (1) political economy (1) science (1) Figure 2. Example of mapping terms into concepts. The wors are then mappe into their corresponing concepts in the ontology. In the example, the two wors government (2) an politics (1) are mappe in the concept government an the term frequencies of these two wors are ae to the concept frequency. From this point, three strategies for aing or replacing terms by concepts can be istinguishe as propose by [1]: A. A Concept This strategy extens each term vector t r by new entries for WorNet concepts C appearing in the texts set. Thus, the vector t r will be replace by the concatenation of t r an c r where r c = ( cf (, c 1 ),..., cf (, c l )) Concept: physics (2) Concept: government (3) Concept: economics (2) Concept: bioscience (1) Concept: mathematics (1) Concept: science (1). The concept vector with l = C an cf (, c ) enotes the frequency that a concept c C appears in a text. The terms, which appear in WorNet as a concept, will be accounte for at least twice in the new vector representation; once in the ol term vector t r an at least once in the concept vector c r. B. Replace Terms by Concepts This strategy is similar to the first strategy; the only ifference lies in the fact that it avois the uplication of the terms in the new representation; i.e., the terms which appear in WorNet will be taken into account only in the concept vector. The vector of the terms will thus contain only the terms, which o not appear in WorNet. C. Concept Vector Only This strategy iffers from the secon strategy by the fact that it exclues all the terms from the new representation incluing the terms, which o not appear in WorNet; category Strategies for Disambiguation c r is use to represent the The assignment of terms to concepts is ambiguous. Therefore, one wor may have several meanings an thus one wor may be mappe into several concepts. In this case, we nee to etermine which meaning is being use, which is the problem of sense isambiguation [8]. Since a sophisticate solution for sense isambiguation is often impractical [1], we have consiere the two simple isambiguation strategies use in [7]. A. All Concepts This strategy consiers all propose concepts as the most appropriate one for augmenting the text representation. This strategy is base on the assumption that texts contain central themes that in our cases will be inicate by certain concepts having height weights. In this case, the concept frequencies are calculate as follows: {,{ t T c ref ( t ) } cf (,c ) = tf (1) B. First Concept This strategy consiers only the most often use sense of the wor as the most appropriate concept. This strategy is base on the assumption that the use ontology returns an orere list of concepts in which more common meanings are liste before less common ones [10]. {,{ t T first ( ref ( t )) c } cf (,c ) = tf c = (2) Aing Hypernyms If concepts are use to represent texts, the relations between concepts play a key role in capturing the ieas in these texts. Recent research shows that simply changing the terms to concepts without consiering the relations oes not have a significant improvement an some time even perform worse than terms [1]. For this purpose, we have consiere the hypernym relation between concepts by aing to the concept frequency of each concept in a text the frequencies that their hyponyms appears. Then the frequencies of the concept vector part are upate in the following way: (,c) = cf (,b) cf ' (3) b H ( c ) c

5 20 The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 where H(c) gives for a given concept c its hyponyms Features Selection Selection techniques for imensionality reuction take as input a set of features an output a subset of these features, which are relevant for iscriminating among categories [3]. Controlling the imensionality of the vector space is essential for two reasons. The complexity of many learning algorithms epens crucially not only on the number of training examples but also on the number of features. Thus, reucing the number of inex terms may be necessary to make these algorithms tractable. Also, although more features can be assume to carry more information an shoul, thus, lea to more accurate classifiers, a larger number of features with possibly many of them being irrelevant may actually hiner a learning algorithm constructing a classifier. For our approach, a feature selection technique is necessary in orer to reuce the big imensionality cause by consiering concepts in the new text representation. For this purpose we use the Chi- Square Statistic for feature selection. The χ 2 statistic measures the egree of association between a term an the category. Its application is base on the assumption that a term whose frequency strongly epens on the category in which it occurs will be useful for iscriminating among the categories. For the purpose of imensionality reuction, terms with small χ 2 values are iscare. The χ 2 multivariate, note χ 2 multvariate is a supervise metho allowing the selection of terms by taking into account not only their frequencies in each category but also the interaction of the terms between them an the interactions between the terms an the categories. The principle consists in extracting K better features characterizing best the category compare to the others, this for each category. With this intention, the matrix (term-categories) representing the total number of occurrences of the p features in the m categories is calculate (see Figure 3). The total sum of the occurrences is note N. The values N represent the frequency of the feature X J in the category e k.. Then, the contributions of these features in iscriminating categories are calculate as inicate in Equation 4, then sorte by escening orer for each category. The evaluation of the sign in the Equation 4 makes it possible to etermine the irection of the contribution of the feature in iscriminating the category. A positive value inicates that it is the presence of the feature which contribute in the iscrimination while a negative value reveals that it is its absence which contribute in it. The principal characteristics of this metho are: It is supervise because it is base on the information brought by the categories. It is a multivariate methoe because it evaluates the role of the feature with consiering the other features. It consiers interactions between features an categories. In spite of its sophistication, it remains of linear complexity in terms number. C 2 x ( f = N f f j. f j..k f.k ) 2 sign( f f f j.. k ) (4) N where f = representing the relative N frequencies of the occurrences Classification Phase The classification phase consists in generating a weighte vector for all categories, then using a similarity measure to fin the closest category Vector Generation Given the features frequencies in all categories, the task of the vector generation step is to create a weighte vector = ( w(, t1),..., w(, t m )) for any category base on its feature frequency = tf t,..., tf t, which commonly vector ( ( ) ( )) tf 1 results from the feature selection step. Each weight w(, t) expresses the importance of feature t in category with respect to its frequency in all training ocuments. The objective of using a feature weight rather than plain frequencies is to enhance classification effectiveness. In our experiments, we use the stanar tfif function, efine as: where: Figure 3. Matrix of features frequencies in categories. C tfif ( t ) k, c i = tf ( t k, c i ) Log f ( t ) (5) k m

6 Using WorNet for Text Categorization 21 tf ( t, c k i ) enotes the number of times feature t k occurs in category c i. f ( t k ) enotes the number of categories in which feature t k occurs. C enotes the number of categories Distance Calculation The similarity measure is use to etermine the egree of resemblance between two vectors. To achieve reasonable classification results, a similarity measure shoul generally respon with larger values to ocuments that belong to the same class an with smaller values otherwise. The ominant similarity measure in information retrieval an text classification is the cosine similarity between two vectors. Geometrically, the cosine similarity evaluates the cosine of the angle between two vectors 1 an 2 an is, thus, base on angular istance. This allows us to abstract from varying vector length. The cosine similarity can be calculate as the normalize prouct: S i, j = w i w i j TFIDF TFIDF 2 w,i w, j TFIDF w j w, j TFIDF 2 w, j (6) where: w is a feature, I an J are the two vectors (profiles) to be compare. TFIDF w,i the weight of the term w in I an TFIDF w,j is the weight of the term w in J. This can be translate in the following way: "More there are common features an more these features have strong weightings, more the similarity will be close to 1, an vice versa ". In our approach, this similarity measure is use to calculate the istance between the vector of the ocument to be categorize an all categories vector. As a result, the ocument will be assigne to the category whose vector is the closest with the ocument vector. 4. Experiments an Evaluation We have conucte our experiments on two commonly use corpora in text categorization research: 20 Newsgroups, an MoApte version of the Reuters collection of the news stories. All ocuments for training an testing involve a pre-processing step, which inclues the task of stopwors removal. Experimental results reporte in this section are base on the so-calle "F 1 measure", which is the harmonic mean of precision an recall. F 1 ( recall, precision ) recall precision = 2 recall + precision (7) In the above formula, precision an recall are two stanar measures wiely use in text categorization literature to evaluate the algorithm s effectiveness on a given category where true positive precision = 100 (8) ( true positive) + ( false positive) true positive recall = 100 (9) ( true positive ) + ( false negative ) We also use the macroaverage F 1 to evaluate the overall performance of our approach on given atasets. The macroaverage F 1 compute the F 1 values for each category an then takes the average over the percategory F 1 scores. Given a training ataset with m categories, assuming the F 1 value for the i-th category is F 1 (i), the macroaverage F 1 is efine as : macroavera ge 4.1. Datasets for Evaluation Reuters F m F = i = m ( i) (10) The Reuters ataset has been use in many text categorization experiments; the ata was collecte by the Carnegie group from the Reuters newswires in There are now at least five versions of the Reuters atasets wiely use in TC community. We choose the Moapte version of the Reuters collection of new stories ownloae from /reuters In our experiments, we use the ten most frequent categories from this corpus as our ataset for training an testing as inicate in Table 1. Table 1. Detailes of the reuters use categories. Category # Training # Test Total Earn Acquisition Money-fx Grain Crue Trae Interest Wheat Ship Corn Newsgroups The 20Newsgroups contains approximately 20,000 newsgroups ocuments being partitione (nearly) evenly across 20 ifferent newsgroups, we use the 20newsgroups version ownloae from Table 2 specifies the 20Newsgroups categories an their sizes.

7 22 The International Arab Journal of Information Technology, Vol. 5, No. 1, January Results Table 2. Detailes of 20Newsgroups categories. Category # Train # Test Total # Docs Docs Docs alt.atheism comp.graphics comp.os.ms-winows.misc comp.sys.ibm.pc.harware comp.sys.mac.harware comp.winows.x misc.forsale rec.autos rec.motorcycles rec.sport.baseball rec.sport.hockey sci.crypt sci.electronics sci.me sci.space soc.religion.christian talk.politics.guns talk.politics.mieast talk.politics.misc talk.religion.misc Total Tables 3 an 4 summarize the results of our approach compare with the Bag-Of-Wor representation over Reuters (10 largest categories) an 20Newsgroups categories. The results obtaine in the experiments suggest that the integration of conceptual features improve text classification results. On the Reuters categories (see Table 3); the best overall value is achieve by the following combination of strategies: "a concept" strategy using "First concept" strategy for isambiguation with the profile size k=200. Macro-average values then reache 71.7%, thus yieling a relative improvement of 6.8% compare to the Bag-Of-Wor representation. The same remarks can be one on the 20Newsgroups categories (see Table 4). The best performance is obtaine with the profile size k=500. The relative improvement is about 5.2% compare to the Bag-Of-Wor representation. 5. Relate Work The importance of WorNet as a source of conceptual information for all kins of linguistic processing has been recognize with many ifferent experiences an specialize workshops. There are a number of interesting uses of WorNet in information retrieval an supervise learning. Green [4, 5] uses WorNet to construct chains of relate synsets (that he calls lexical chains ) from the occurrence of terms in a ocument. It prouces a WorNet base ocument representation using a wor sense isambiguation strategy an term weighting. Dave [13] has explore WorNet using synsets as features for ocument representation an subsequent clustering. He i not perform wor sense isambiguation an only foun that WorNet synsets ecrease clustering performance in all his experiments. Voorhees [15] as well as Molovan an Mihalcea have explore the possibility to use WorNet for retrieving ocuments by keywor search. It has alreay become clear by their work that particular care must be taken in orer to improve precision an recall. Term/Concept A Concept Replace Terms By Concepts Concept Vector Only Bag-Of- Wor Disambiguation First All First All First All K= K= K= K= K= K= K= The Size of Categories Profiles Table 3.The comparison of performance (F 1 ) on Reuters K= The Size of Categories Profiles Table4. The comparison of performance (F 1 ) on 20Newsgroups. Term/Concept A Concept Concept Vector Only Replace Terms By Concepts Disambiguation First All First All First All Bag-Of- Wor K= K= K= K= K= K= K= K=

8 Using WorNet for Text Categorization Conclusion an Future Work In this paper, we have propose a new approach for text categorization base on incorporating backgroun knowlege (WorNet) into text representation with using the χ 2 multivariate, which consists on extracting the K better features characterizing best the category compare to the others. The experimental results with both Reuters21578 an 20Newsgroups atasets show that incorporating backgroun knowlege in orer to capture relationships between wors is especially effective in raising the macro-average F 1 value. The main ifficulty is that a wor usually has multiple synonyms with somewhat ifferent meanings an it is not easy to automatically fin the correct synonyms to use. Our wor sense isambiguation technique is not capable of etermining the correct senses. Our future works inclue a better isambiguation strategy for a more precise ientification of the proper synonym an hyponym synsets. Some work has been one on creating WorNets for specialize omains an integrating them into MultiWorNet. We plan to make use of it to achieve further improvement. References [1] Bloehorn S. an Hotho A., Text Classification by Boosting Weak Learners Base on Terms an Concepts, in Proceeings of the Fourth IEEE International Conference on Data Mining, IEEE Computer Society Press, [2] Cruse D., Lexical Semantics, Cambrige, Lonon, New York, Cambrige University Press, [3] Dash M. an Liu H., Feature Selection for Classification, Journal Intelligent Data Analysis, Elsevier, vol. 1, no. 3, [4] Green S., Builing Hypertext Links in Newspaper Articles Using Semantic Similarity, in Proceeings of Thir Workshop on Applications of Natural Language to Information Systems (NLDB 97), pp , [5] Green S., Builing Hypertext Links by Computing Semantic Similarity, IEEE Transactions on Knowlege an Data Engineering (TKDE), vol. 11, no. 5, pp , [6] Hofmann T., Probmap: A Probabilistic Approach for Mapping Large Document Collections, Journal for Intelligent Data Analysis, vol. 4, pp , [7] Hotho A., Staab S., an Stumme G., Ontologies Improve Text Document Clustering, in Proceeings of the 2003 IEEE International Conference on Data Mining (ICDM'03), pp , [8] Ie N. an Véronis J., Introuction to the Special Issue on Wor Sense Disambiguation: The State of the Art, Computational Linguistics, vol. 24, no. 1, pp. 1-40, [9] Kehagias A., Petriis V., Kaburlasos V., an Fragkou P., A Comparison of Wor an Sense- Base Text Categorization Using Several Classification Algorithms, Journal of Intelligent Information Systems, vol. 21, no. 3, pp , [10] McCarthy D., Koeling R., Wees J., an Carroll J., Fining Pre-Dominant Senses in Untagge Text, in Proceeings of the 42n Annual Meeting of the Association for Computational Linguistics, pp Barcelona, Spain, [11] Miller G., Nouns in WorNet: A Lexical Inheritance System, International Journal of Lexicography, vol. 3, no. 4, [12] Peng X. an Choi B., Document Classifications Base on Wor Semantic Hierarchies, in Proceeings of the International Conference on Artificial Intelligence an Applications (IASTED), pp , [13] Pennock D., Dave K., an Lawrence S., Mining the Peanut Gallery: Opinion Extraction an Semantic Classification of Prouct Reviews, in Proceeings of the Twelfth International Worl Wie Web Conference (WWW 2003), ACM, [14] Sebastiani F., Machine Learning in Automate Text Categorization, ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, [15] Voorhees E., Query Expansion Using Lexical- Semantic Relations, in Proceeings of ACM- SIGIR, Dublin, Irelan, pp , ACM/Springer, Zakaria Elberrichi is lecturer in computer science an a researcher at Evolutionary Engineering an Distribute Information Systems Laboratory, EEDIS at the University Djillali Liabes, Sii-belabbes, Algeria. He hols a master egree in computer science from the California State University in aition to PGCert in higher eucation. He has more than 17 years of experience in teaching both BSc an MSc levels in computer science an planning an leaing ata mining relate projects. The last one calle New Methoologies for Knowlege Acquisition. He supervises five master stuents in e- larning, text mining, web services, an workflow.

9 24 The International Arab Journal of Information Technology, Vol. 5, No. 1, January 2008 Abellatif Rahmoun receive his BSc egree in electrical engineering, University of Science an Engineering of Oran, Algeria, his Master egree in electrical engineering an computer science from Oregon State University, USA, an his PhD egree in computer engineering, Algeria. Currently, he is a lecturer in Computer Science Department, Faculty of Planning an Management, King Faisal University, Kingom of Saui Arabia. His areas of interest inclue fuzzy logic, genetic algorithms an genetic programming, neural networks an applications, esigning ga-base neuro fuzzy systems, ecision support systems, AI applications, e-learning, electronic commerce an electronic business an fractal image compression using genetic tools.