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\title{Probability of Semantic Similarity and N-Grams Pattern Learning for Data Classification}
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             \author[1]{V Vineeth  Kumar}

             \author[2]{Dr. N  Satyanarayana.}

             \affil[1]{  JNTU Hyderabad}

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\date{\small \em Received: 6 December 2016 Accepted: 5 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


Semantic learning is an important mechanism for the document classification, but most classification approaches are only considered the content and words distribution. Traditional classification algorithms cannot accurately represent the meaning of a document because it does not take into account semantic relations between words. In this paper, we present an approach for classification of documents by incorporating two similarity computing score method. First, a semantic similarity method which computes the probable similarity based on the Bayes' method and second, n-grams pairs based on the frequent terms probability similarity score. Since, both semantic and N-grams pairs can play important roles in a separated views for the classification of the document, we design a semantic similarity learning (SSL) algorithm to improves the performance of document classification for a huge quantity of unclassified documents. The experiment evaluation shows an improvisation in accuracy and effectiveness of the proposal for the unclassified documents.

\end{abstract}


\keywords{semantic similarity, classification, naive bayes, n-grams pattern.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
eb mining is facing an important problem in measuring the semantic similarity among the words in the process of information retrieval and language processing. The most semantic based application requires the accurate measuring of semantic similarity among the document concepts and words. In information search, one of the most important problems is to semantically to get a number of documents correlated to a user's request. Semantic similarity between the words such as "word sense disambiguation" (WSD) can be an efficient assessment for the text entailment and automatic document classification, it is also important for the variety of natural language processing tasks. Automatic classification of documents is an important part of the research in the vision, and an enormous prospective for numerous applications around the text, such as search and analysis. Its purpose is to allocate a document given to the group of default to which it is in the right places. So far, applications have different types of algorithms based on the study or automatic calculation in this process and showed how much work \hyperref[b1]{[2]}, \hyperref[b2]{[3]}, \hyperref[b4]{[5]}. However, mainly of the work functional to this task used aneffortless wordcollection representation where each attribute communicates to a particular word. That is, assume that words are independent and utilize only the distribution of content words.\par
Over the past few years, we've seen the Web evolve into a semantic Web. The amount of information posted with linked data has consistently increased. With this increase, annotation and classification systems have created new opportunities to reuse this data as a semantic knowledge base and can be interconnected and structured to increase the accuracy and recovery of annotation and classification mechanisms. The Semantic web aims to explain the meaning of the information posted on the Web in order to make it possible to search by understanding the meaning of the information accurately. In this regard, document text learning and classification is most common, by assigning text to one or more existing class. This development determines the class membership of a text document that has a separate set of classes with profiles and different features. Criteria for deciding appropriate features for classification are important and are determined by the priority of the classifier. Semantic classification occurs when the target document element or term of the classification represents the meaning of the document.\par
Measuring the semantic similarity among texts is a basic task and can be capable of being utilized for a variety of applications, together with "text clustering" \hyperref[b0]{[1]} and "text classification" \hyperref[b1]{[2]}. The challenge in evaluation similarities among texts is infrequent, that is, there will be no coincidence of terms between the two texts. For example, two texts "Apple's New Product" and "iPhone-6" refer to related topics, even though they do not use similar terms.\par
To overcome scarcity, we need to use external data or knowledge to enrich the semantic representation of text. The semantically associated words of a particular word are listed in amanually created universal dictionary vocabulary ontology such as "Word Net". In this, a synset includes a set of synonyms for a specific word sense. However, semantic similarities among individual transform more than time and across domains. For example, apples are often associated with computers on the web. However, this apple sensation is not listed in most universal thesauri or dictionaries. Users searching for apples on the web may be concerned in the meaning of "apple" and "not apple" as a fruit. Innovative words are stably generated and new senses are dispensed to existing words. Preserving ontology manually to confined these innovative words and senses is costly, if not impracticable.\par
In this paper, we contribute an automated semantic similarity learning (SSL) move towards to compute the probability of semantic similarity among terms or entities of documents with the class knowledge set entities. Here, we define two probabilistic scores, a semantic similarity (SS) score and N-grams pair similarity (GS) score enhancing Naive Bayes probabilistic method to aggregate the relation between document and class entities. Semantic Similarity method relates the trained class entities terms with the extracted document key terms to compute the document probable SS score against each class entities, and Ngrams pair similarity method relate a document with each trained class entity with the constructed N-grams pairs, which is constructed using most frequent terms extracted from the document and the probable GS score is the summation of all individual N-grams pairs, i.e., sum(GS 1 , GS 2 , .. , GS n ).We perform an experiment evaluation on Reuters-21578 Datasets to demonstrate the effectiveness of the proposal.\par
This papers organized in 6 sections. Section-1 above describes the introduction, section-2 discuss the background works, section-3 presents the proposed works outline, probabilistic semantic and N-gram pairs pattern learning, section-4 discuss the semantic similarity classification approach, section-5 present experiment methodology and results and finally section-6 presents the conclusion of the work. 
\section[{II. Background Study}]{II. Background Study}\par
Semantic similarity plays an significant responsibility in "natural language processing", "information retrieval", "text summarization", "text classification", and "text clustering". Particularly, "Explicit Semantic Analysis" (ESA) \hyperref[b6]{[6]} is extensively utilized because of its accessibility and diversity. ESA was build up to calculate word relationship as well as text comparison in natural language. ESA creates a "weighted index" that maps each phrase to the listing of articles that appears and calculates the similarity among the two words or a vector of text.\par
Naive Bayes \hyperref[b0]{[1]} classification performance using semantic similarity has made various efforts. An approach that is often used to mitigate naive independent assumptions is to express attribute addiction in a graph-based model called a "Bayesian network", where nodes correspond to attributes. Oriented arch is weighted by the circumstances probability for each node specified a close relation. Because "Bayesian network learning" is NP-hard \hyperref[b6]{[6]}, numerous approaches recommend imposing model constraints to formulate it easier to deal with learning problems. Subsequent approaches in \hyperref[b8]{[8]}, \hyperref[b9]{[9]}, \hyperref[b17]{[17]}, \hyperref[b18]{[18]} have brought considerable improvements. For example, in \hyperref[b21]{[21]}, an ensemble of Tree Augmented Naive-Bayes (TANs) was be trained, each rooted in a dissimilar attribute. It then compiles the classifications of all eligible TANs to predict class labels. In \hyperref[b8]{[8]}, we assume that the entire Bayesian network structure is learned first and all attributes are dependent. Unlike \hyperref[b18]{[18]}, the "Markov network model" is utilized to express characteristic dependencies that are estimated similar to [39] by taking advantage of the conditional log probability intention purpose. However, performing andtake advantage operation can be computationally demanding. Many methods are used to inherit the structural simplicity of Naive Bayes classifiers to keep away from the complication of the construction learning process \hyperref[b9]{[9]}, \hyperref[b10]{[10]}, \hyperref[b12]{[12]}, \hyperref[b13]{[13]}. While the "Naive Bayes classification" is functional at the "decision tree leaves level" and is act upon on a subset of the training data, the data set properties are divided into two collections as in  {\ref [11]},where one group is assigned a class probability based on "Naive Bayes", and the other is supported on a "decision table".\par
Despite its effortlessness, the previously point out the classifier still shows a few constraints in handling very much related data. In \hyperref[b12]{[12]}, \hyperref[b13]{[13]}, the features are weighted dissimilarly depending on the involvement to the classification. A comparable approach was applied to the most effective "Bayesian Network classifier" and " Hidden Naive Bayes" \hyperref[b9]{[9]}. In \hyperref[b9]{[9]}, the authors recommended generating a hidden close qualified that correspond to the effect of everything else on each property. The effect is computed as a linear arrangement of circumstance common information among attribute pairs, similar to \hyperref[b8]{[8]}. Therefore, the parent correlation is ignored.\par
Dissimilarity like \hyperref[b10]{[10]}, [11], \hyperref[b19]{[19]}, "En Bay" \hyperref[b1]{[2]} implements a new, uncomplicated, and useful approach that unites the generation of conditionally independent decision models and the reliable probability approximation by class. En Bay is a pattern-based Bayesian classifier that frequently uses a set of items frequently to estimate Bayesian probabilities. En Bay uses new and effective probabilistic approximation estimates that adhere to the conditional independence model. The set of extended, normal and separate items to be comprised in a class-based approximation is chosen by entropy-based heuristics, and the set of properties is conditionally mutually independent, depending on the class being evaluated. We extend En Bay probability computation methods to computes the semantic similarity probability score based on the terms dependency over the trained class terms entities, as discussed in section 3.2 below.\par
The "Large Bayes classifier" [11] performed the primary challenge to mitigate well-built independent 3 Year 2017 ( ) H to estimate the probability through product form approximation \hyperref[b12]{[12]}. However, all preceding patternbased Bayesian advance create inimitable product approximations for all test cases. Thus, estimations are only tied to the considered grade. Moreover, since it is necessary to extract animmense number of long and redundant repeated item sets, the superiority of the approximation is sensitive to changes in the "support threshold", and the classification algorithm cannot cope with a large data set. We extend this constructing Ngrams pairs using frequent items to estimates the N-grams similarity probability score, as discussed in section 3.3 below. 
\section[{III. Proposed Approach a) Outline}]{III. Proposed Approach a) Outline}\par
The Semantic Similarity Learning (SSL) method, which uses probabilistic performances to describe probabilistic scores and put together scores supported on Bayes' method for accurate document classification to measure the robust discovery and semantic similarity of related entities to document.  \hyperref[fig_0]{1}. outlines our the proposed approach method. Our method obtains the main points of the probabilistic analysis of associations and related documents on the basis of trained document entities. The approach performs two probabilistic score computation method. First, Semantic Similarity Method which measures the similarity of their associated entity of a document with the list of trained class entity terms by SS k = P(d k (t) | C m ), i.e., probability of a document d k (t)terms associated with a set of class C m terms by means of cosine similarity.\par
The second method extracts the most frequent terms F from the extracted document terms using term frequency (tf) and using F we construct N-grams pairs. In general, an N-gram method slice a longer text into ncharacters, but we customized this to slice a pattern into number words pairs (V-Pair) based on n which we term as N-gram pattern, an illustration is shown in Fig. \hyperref[fig_1]{2}.Using the constructed pairs we compute,???? ?? = ? ?? ?? ?? ??=1\par
, where n is the number of pairs and W i = P( V-Pair n | C m ) i.e., probability of N-gram pair terms related to the set of class C m terms using cosine similarity. Now, we compute the final probability of semantic similarity P sem = sum (SS k, GS k ) for each document against each trained class. To classify the document we find the max P sem among the computed probability of semantic similarity of each class. The class which has the max P sem will be considered as the document class. We describe each method mechanism in the aspect in the following sections. [1], "Decision Trees" \hyperref[b1]{[2]}, "SVMs" \hyperref[b2]{[3]}, "Rule-based" \hyperref[b3]{[4]}, and "Associative classifiers" \hyperref[b4]{[5]}. 
\section[{Unlabeled Document}]{Unlabeled Document}
\begin{quote}
Keyword\end{quote}
\par
Bayesian classification methods recognized a classification supported on the of "Bayes theorem" \hyperref[b0]{[1]}.It predicted that a class based on test documents previously un seen T = \{ a 1 , a 2 , . . , a n \}by opting the class c i that make the most of the subsequent formula: Despite the simplicity, the Bayesian approach is calculation intractable without compelling a powerful model simplification \hyperref[b0]{[1]}, \hyperref[b6]{[6]}, \hyperref[b7]{[7]}. The most important instance of simplification is the "Naive Bayes classifier" \hyperref[b0]{[1]}, which solves the problem by assuming that all attributes are conditionally self-determined and given as the class c i . Therefore, the join probability of (1), is based on the generated Naive Bayes model, which can be approximated as, \hyperref[b1]{(2)} Based on the approximation we combine the probabilistic semantic similarity (SS) scores extracted from the training data to find the appropriate entities for the document. Let's assume that multiple key terms are entered as input. That is, we compute P (c|T ')for the set of core key terms T' =\{t 1 , t 2 , ... , t k \}, where T' is a key term, which are derived using traditional Naive Bayes for any related class c i .\par
One possible approach to this task is a twostep method of determining the key terms first and then applying the existing Naive Bayes. However, this approach raises the question of how key terms are established. We have developed a probabilistic similarity method for finding related entities. It can be functional to a set with probability determined members. F = \{n = 3 n = 2 n = 1 Year 2017 ( ) 
\section[{H}]{H}\par
For a particular, a set of key terms T, P (c|T' ) is calculated for all probable states T'.Fig.  {\ref 3},summarizes an illustration of the probabilistic semantic similarity method for a set of key terms of a document d k as t 1 , . . , t k . SS method is utilized to calculate P(t K |C m ), which is the probability score SS k of the set of key terms, T for the class C m . 
\section[{Figure 3: Probabilistic Semantic Similarity Method}]{Figure 3: Probabilistic Semantic Similarity Method}\par
Unfortunately, the circumstances of selfdetermination supposition prepared by Naive Bayes may not true always, to obelieves high-order associations for the period of the probability estimate, a parallel proposal is made based frequent pattern learning in T, and constructing a N-grams pairs to support accurate classification. 
\section[{c) N-grams Pattern Learning}]{c) N-grams Pattern Learning}\par
The N-gram is defined as a sequence of terms, the length is n, and the words taken are called terms. In the literature, we can see the definition of an N-gram as a concurrent set of terms, but only consecutive term sequences were used in this study. One word in the document is represented by a set of overlapping Ngrams as shown in Fig. \hyperref[fig_1]{2}. The N-gram model can be fictional by introduction a small window over a sentence or text, where only n words can be seen at the same time. So the effort less N-gram model is the so-called "unigram model". This is a one-word model at a time. For example, the "Latest application and iPhone released." sentence contains five unigrams as, "Latest", "application", "and", "iPhone" and "released" Of course, this is not very beneficial information. It is just a word that makes up the sentence. In fact, N-grams are interesting when n is greater than 2 (bigram) or more.\par
Each word happens in a document with a dissimilar frequency. The main thought of categorization utilized by Trenkle and Cavnar \hyperref[b4]{[5]} is that they should have similar N-gram frequency distributions when comparing documents of the same category. We performanN-gram pattern learning through creating n pairs using frequent document terms.\par
For a given document d having a T terms. Let's assume the frequent terms represent as F. Using the F terms we construct N-grams pairs as V-Pair. To learn the probability of V-pair pattern associationW n of a document with a class c m we calculate P(V-Pair n |c m )as shown in Fig .  {\ref 3}. Here, the class must contain all the pair terms to match the association. To compute the Ngrams probable similarity GS, we done the summation of all W n as, In order to efficiently search for class-specific patterns, in the SSL training phase, an FP-growth data pattern representation \hyperref[b18]{[18]} is separately created to store training data belonging to each class in a compressed form. The FP-growth pattern is a single-tree data structure for the minimum support (min\textunderscore sup) frequent item set used in the class pattern learning context. Algorithm-1 shows the pseudo-code in the SSL training phase. Minimum supported thresholds were applied to remove infrequently used items. In this case, items that do not meet the necessitated minimum support threshold are not consisted of in the FP-growth pattern.d k C 1 C 2 C m . . . .\par
The obtained FP-G of each individual class c i , will be used as a trained knowledge for the SSL classifier. 
\section[{b) SSL Classification}]{b) SSL Classification}\par
The SSL classification approach is one of the accomplished algorithms managing unlabeled documents. It applies two probability computation as semantic similarity SS and N-gram Similarity G Son the dataset to perform the classification using the trained FP-growth pattern knowledge as shown in Fig. \hyperref[fig_0]{1}. The SSL classifier initialized with anonly some trained class item sets. At each iteration, it chose an unlabeled document and perform the computation to compute the SS and GS score. It learns separate similarity score over each class pattern learning, and support a set of class labels for the unlabeled documents. For each class c i belonging to the training data set is corresponding to FP-growth is visited to construct the class-centric product estimation and calculated the probability P(T,c i ).\par
Its ultimate prediction is through by coming together both SS and GS score, P sem = sum( SS k , GS k ) which decline classification error spredictions. The massive is the P sem of the class will be predicated as thed k C 1 C 2 C m . . . . . . W 2 = P( V-Pair 2 |C 1 ) W 1 = P( V-Pair 1 |C 1 ) W n = P( V-Pair n |C 1 ) . . . . . . . . W 2 = P( V-Pair 2 |C 2 ) W 1 = P( V-Pair 1 |C 2 ) W n = P( V-Pair n |C 2 ) . . . . . . . . W 2 = P( V-Pair 2 |C m ) W 1 = P( V-Pair 1 |C m ) W n = P( V-Pair n |C m ) . . . . . . . . ???? 1 = ? ?? ?? ?? ??=1 ???? 2 = ? ?? ?? ?? ??=1 ???? ?? = ? ?? ?? ?? ??=1 Year 2017 ( ) 
\section[{H}]{H}\par
closer association. This predictionim provises the performance of the algorithm classification accuracy. Since SSL classification uses two probability computation score with the FP-growth pattern, its presentation is better than any particular classifier.Algorithm-2briefly summarizes the SSL classification algorithm.  The "Reuters-21578 corpus" is the mainly common utilized benchmark corpus in text classification. It consists of over 20,000 Reuters news stories from 1987 to 1991, and 135 subject classes are used in the experiment. This version contains "9603 training documents", "3299 test documents", and "27,863 inimitable words" after stopping stemming and word removal. We consider only 10 topics as classes of Reuters-21578 data for experimental evaluation measurements.d k = \{T k \} ?D k ; //--For all class in FP-G vector -- for all c i in FP-Gdo c i = \{FT i \} ?FP-G i ; SS i = P (d k ? c i ); V\textunderscore SS[i] = SS i ; end for VD s [k] = V\textunderscore SS; // -- 
\section[{b) Performance Measure}]{b) Performance Measure}\par
To estimate the classification performance of the proposed method, we utilize the precision, recall, and accuracy. Let considered P is all relevant documents and N is all negative document. PC + as a positively classified, NC + as negatively classified documents. PCas a positively classified for an incorrect document, NCas negatively classified for correct documents. By constructing a confusion matrix for the above evaluation measure we compute the classifier performance.\par
To measure the classifier precision rate CP, the classifier recall rate CR and the classifier accuracy rate CA the following equation are used. 
\section[{c) Evaluation Results}]{c) Evaluation Results}\par
In the Reuters-21578 datasets we do consider both labeled and unlabeled documents, the effect of using two probabilistic semantic similarity learning is given in Table  {\ref 1}. We initially evaluate with Semantic Similarity Score (SS), then with N-grams patterns pairs Score (GS) and finally with both. The classification performance using both the Semantic Similarity and the N-gram pattern pairs learning outperforms over the one using any single learning for most classes.???? = ???? + ???? + + ???? +\textbf{(4)}???? = ???? + ???? + + ???? ?\textbf{(5)}???? = ???? + + ???? + ?? + ??\textbf{(6)} 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVII Issue II Version I
\begin{quote}
8 Year 2017 ( ) H Table 1:\end{quote}
\par
The accuracy enhancement by using semantic similarity learning on "Reuters-21578 corpus".\par
We found that the greater the number of related documents in the training set, the higher the accuracy of using N-Naive Bayes has a low error rate and high accuracy when there are many documents in the class. The classification comparison result is shown in Fig. 4. At first, we performed comparisons with stateof-the-art Bayesian classifiers. And because our approach is pattern-based, we compare it with the wellknown associative classifiers SVM and the new improved Bayesian approach known as En Bays \hyperref[b1]{[2]}.\par
Finally, we performed a comparative assessment of precision, recall, and accuracy rates as a classifier for classifiers.   The rate of precision and recall in Fig.  {\ref 5} and 6 shows an improvisation in compared to SVM and En Bays method. The effects of both SS and GS score in probability similarity measure shows SSL precision improvisation. Fig.  {\ref 7} shows the classifier accuracy measures comparison. It also shows an improvisation of SSL approach in compare to others. The falling of accuracy with increasing of the document due to the limitation of trained class knowledge. As both the method has a dependency on the trained data knowledge for performing probability similarity computation cause the falling of the rate. 
\section[{VI. Conclusion}]{VI. Conclusion}\par
In this paper, we propose a semantic similarity and N-gram pattern learning method based on the Bayesian classifier, which approximates Bayesian probability using frequent itemsets. It utilized new and more efficient probability approximations that adhere to the conditional independence model. A long, frequent, and separate set of items to be included in a classbased approximation is selected. It is based on the Baye's theorem and semantic similarity computation approach. Our method is a sort of probabilistic semantic similarity learning (SSL) that uses vectors to generate vectors of related entities as semantic representations of specific text and to measure semantic similarities. SSL combines vectors using expanded Naive Bayes, while SSL simply adds up the vectors for each term occurring in the text based on the majority of rules. This method uses both Semantic Similarity Learning for SSL algorithms and N-gram pattern learning and applies algorithms to unstructured document classification.\par
Experiments on the Reuters-21578 document show that the SSL approach improves classification performance, and unlabeled documents are a good resource to overcome documents with a limited number of labels.\par
Future developments in this work will address the integration of generalized item aggregation mining algorithms to further improve classification and accuracy in noise-prone areas of data where there liability of probability estimation is particularly important.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-4.png}
\caption{\label{fig_2}( 1 )}\end{figure}
       \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.091375\textwidth}P{0.391\textwidth}P{0.367625\textwidth}}
\tabcellsep if n = 3;\tabcellsep \\
1-grams\tabcellsep 2-grams\tabcellsep 3-grams\\
application\tabcellsep (application,\tabcellsep (application, software, screen)\\
software\tabcellsep software)\tabcellsep (application, software, phone)\\
screen\tabcellsep (application, screen)\tabcellsep (application, software, iphone)\\
phone\tabcellsep (application, phone)\tabcellsep (software, screen, phone)\\
iphone\tabcellsep (application, iphone)\tabcellsep (software, screen, iphone)\\
\tabcellsep (software, screen)\tabcellsep (screen, phone, iphone)\\
\tabcellsep (software, phone)\tabcellsep \\
\tabcellsep (software, iphone)\tabcellsep \\
\tabcellsep (screen, phone)\tabcellsep \\
\tabcellsep (screen, iphone)\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 			\footnote{© 20 7 Global Journa ls Inc. (US)} 			\footnote{© 2017 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[ Intelligence Research Soc. Conf ()]{b5}\label{b5} 	 		\textit{},  	 	 		\textit{Intelligence Research Soc. Conf}  		2008. p. .  	 
\bibitem[Yuan et al. ()]{b17}\label{b17} 	 		‘A Cluster-based Resource Correlative Query Expansion in Distributed Information Retrieval’.  		 			Lin Yuan 		,  		 			Lin Hongfei 		,  		 			Li He 		.  	 	 		\textit{Journal of Computational Information Systems}  		2012. 8  (1)  p. .  	 
\bibitem[Hall (ed.) ()]{b7}\label{b7} 	 		\textit{A Decision Tree-Based Attribute Weighting Filter for Naive Bayes},  		 			M Hall 		.  		XXIII, M. Bramer, F. Coenen, and A. Tuson (ed.)  		2007. Springer. p. .  	 	 (Research and Development in Intelligent Systems) 
\bibitem[Johnson and Shore (1980)]{b9}\label{b9} 	 		‘Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross Entropy’.  		 			R Johnson 		,  		 			J Shore 		.  	 	 		\textit{IEEE Trans. Information Theory}  		Jan. 1980. 26  (1)  p. .  	 
\bibitem[Preethi (2012)]{b13}\label{b13} 	 		‘Case and Relation (CARE) based Page Rank Algorithm for Semantic Web Search Engines’.  		 			Ms N Preethi 		,  		 			Devi 		,  		 			DrT 		.  	 	 		\textit{IJCSI International Journal of Computer Science Issues}  		May 2012. 9  (1) .  	 
\bibitem[Banerjee et al. (2007)]{b11}\label{b11} 	 		‘Clustering short texts using Wikipedia’.  		 			S Banerjee 		,  		 			K Ramanathan 		,  		 			A Gupta 		.  	 	 		\textit{Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr. (SIGIR)},  				 (Int. ACM SIGIR Conf. Res. Develop. Inf. Retr. (SIGIR))  		Jul. 2007. p. .  	 
\bibitem[Hall and Frank]{b4}\label{b4} 	 		‘Combining Naive Bayes and Decision Tables’.  		 			M Hall 		,  		 			E Frank 		.  	 	 		\textit{Proc. 21st Int'l Florida Artificial},  				 (21st Int'l Florida Artificial)  		 	 
\bibitem[Huan et al. (2011)]{b15}\label{b15} 	 		‘EIIS: an educational information intelligent search engine supported by semantic services’.  		 			Ch.-Qin Huan 		,  		 			Ru-Lin Duan 		,  		 			Y Tang 		,  		 			Zhi-Ting Zhu 		,  		 			Y.-Jian Yan 		,  		 			Yu-Qing Guo 		.  	 	 		\textit{international Journal of Distance Education Technologies}  		January 1, 2011.  	 
\bibitem[Baralis et al. (2013)]{b1}\label{b1} 	 		‘En Bay: A Novel Pattern-Based Bayesian Classifier’.  		 			Elena Baralis 		,  		 			Luca Cagliero 		,  		 			Paolo Garza 		.  	 	 		\textit{IEEE Transactions On Knowledge And Data Engineering}  		December 2013. 25  (12) .  	 
\bibitem[Grossman and Domingos ()]{b2}\label{b2} 	 		‘Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood’.  		 			D Grossman 		,  		 			P Domingos 		.  		 \xref{http://dx.doi.org/10.1145/1015330.1015339}{10.1145/1015330.1015339}.  		 \url{http://doi.acm.org/10.1145/1015330.1015339}  	 	 		\textit{Proc. 21st Int'l Conf. Machine Learning (ICML '04)},  				 (21st Int'l Conf. Machine Learning (ICML '04))  		2004. p. 46.  	 
\bibitem[Agrawal et al. ()]{b8}\label{b8} 	 		‘Mining Association Rules between Sets of Items in Large Databases’.  		 			R Agrawal 		,  		 			T Imielinski 		,  		 			A Swami 		.  	 	 		\textit{ACM SIGMOD Record}  		1993. 22 p. .  	 
\bibitem[Vishal Jain and Singh ()]{b22}\label{b22} 	 		‘Ontology Based Information Retrieval in Semantic Web: A Survey’.  		 			Dr Mayank Vishal Jain 		,  		 			Singh 		.  	 	 		\textit{I.J. Information Technology and Computer Science}  		2013. 10 p. .  	 
\bibitem[Duhan et al. ()]{b21}\label{b21} 	 		‘Page Ranking Algorithms: A Survey’.  		 			N Duhan 		,  		 			A K Sharma 		,  		 			K K Bhatia 		.  	 	 		\textit{proceedings of the IEEE International Advanced Computing Conference (IACC)},  				 (the IEEE International Advanced Computing Conference (IACC))  		2009.  	 
\bibitem[Kohavi ()]{b3}\label{b3} 	 		‘Scaling up the Accuracy of Naive Bayes Classifiers: A Decision-Tree Hybrid’.  		 			R Kohavi 		.  	 	 		\textit{Proc. Second Int'l Conf. Knowledge Discovery Data Mining (KDD '96)},  				 (Second Int'l Conf. Knowledge Discovery Data Mining (KDD '96))  		1996. p. .  	 
\bibitem[Baralis et al. ()]{b10}\label{b10} 	 		‘Support Driven Opportunistic Aggregation for Generalized Itemset Extraction’.  		 			E Baralis 		,  		 			L Cagliero 		,  		 			T Cerquitelli 		,  		 			V D' Elia 		,  		 			P Garza 		.  	 	 		\textit{Proc. Fifth Int'l Conf. Intelligent Systems},  				 (Fifth Int'l Conf. Intelligent Systems)  		2010.  	 
\bibitem[Chu et al. ()]{b18}\label{b18} 	 		‘Textual document indexing and retrieval via knowledge sources and data mining’.  		 			W W Chu 		,  		 			Z Liu 		,  		 			W Mao 		.  	 	 		\textit{Commun. Inst. Inf. Comput. Mach. (CIICM)}  		2002.  (5)  p. .  	 
\bibitem[Lee et al. (2001)]{b14}\label{b14} 	 		‘The semantic web’.  		 			T B Lee 		,  		 			J Hendler 		,  		 			O Lassila 		.  	 	 		\textit{Scientific American}  		May 2001. 284  (5) .  	 
\bibitem[Vizcaíno et al. ()]{b19}\label{b19} 	 		‘Towards an on tology for global software development’.  		 			A Vizcaíno 		,  		 			F García 		,  		 			I Caballero 		,  		 			J C Villar 		,  		 			M Piattini 		.  	 	 		\textit{IET Softw}  		2012. 6  (3)  p. .  	 
\bibitem[Sun et al. (2011)]{b12}\label{b12} 	 		‘Towards effective short text deep classification’.  		 			X Sun 		,  		 			H Wang 		,  		 			Y Yu 		.  	 	 		\textit{Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr. (SIGIR)},  				 (Int. ACM SIGIR Conf. Res. Develop. Inf. Retr. (SIGIR))  		Jul. 2011. p. .  	 
\bibitem[Sharma et al. ()]{b16}\label{b16} 	 		‘Web Page Indexing through Page Ranking for Effective Semantic Search’.  		 			Robin Sharma 		,  		 			Ankita Kandpa 		,  		 			Priyanka Bhakuni 		,  		 			Rashmi Chauhan 		,  		 			R H Goudar 		,  		 			Asit Tyagi 		.  	 	 		\textit{Proceedings of7'h International Conference on Intelligent Systems and Control},  				 (7'h International Conference on Intelligent Systems and Control)  		2013.  	 
\bibitem[Ferreira et al. ()]{b6}\label{b6} 	 		\textit{Weighted Naive Bayes Modelling for Data Mining},  		 			J T A S Ferreira 		,  		 			D G T Denison 		,  		 			D J Hand 		.  		2001.  	 
\bibitem[Tyagi and Sharma (2012)]{b20}\label{b20} 	 		‘Weighted Page Rank Algorithm Based on Number of Visits of Links of Web Page’.  		 			N Tyagi 		,  		 			S Sharma 		.  	 	 		\textit{In International Journal of Soft Computing and Engineering (IJSCE)}  		 2231- 2307.  		July 2012.  (2) .  	 
\bibitem[Shirakawa et al. (2015)]{b0}\label{b0} 	 		‘Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes’.  		 			Masumi Shirakawa 		,  		 			Kotaro Nakayama 		,  		 			Takahiro Hara 		,  		 			Shojiro Nishio 		.  	 	 		\textit{IEEE Transactions On Emerging Topics In Computing}  		June 2015. 3  (2) .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
