Enterprise information integration

The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN). Index Terms: World Wide Web, Web page classification, textual categorization, HTML, Artificial Neural Networks, ANN.

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.

Googling for Health Information

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

Automatic web page classification can be used in domain-specific search engines to help users get the specific information more conveniently and precisely on Internet. The semantic similarity and noisy data in domain-specific web pages make traditional classifier perform poorly on them. In this paper, a dictionary-based multilingual web page classification method is proposed to try to improve the classification performance. A domain-specific dictionary is constructed in the method to intensify the domain-specific knowledge in the pages. An automatic encoding detection and integration method is also introduced in the classifier to extract Chinese and English information precisely from the multilingual pages. After verified in the experiments, the method is integrated into a real domain-specific search engine where it shows good effectiveness. Keywords-Web page classification, Search engine, Domainspecific knowledge, Dictionary

Automatic processing of websites is of great importance for applications such as search engine that extract information from web pages. Search engines use meta tag values when classifying pages of websites. Meta tag names can change for different languages. For example, for login page, entries such as login, login page or giris, giris sayfasi may change from language to language. When the websites are examined, it can be seen that each of the pages created for the same purpose has similar designs. In this study, a deep learning based model was proposed for functional classification of web pages, regardless of language. Transfer learning was used to reduce the cost during the feature extraction process from recorded web page images. Finally, the results of two different experiments are presented for show the effectiveness of the proposed method in the classification of web pages according to their functions.

The lack of sufficient labeled Web pages in many languages, especially for those uncommonly used ones, presents a great challenge to traditional supervised classification methods to achieve satisfactory Web page classification performance. To address this, we propose a novel Nonnegative Matrix Tri-factorization (NMTF) based Dual Knowledge Transfer (DKT) approach for cross-language Web page classification, which is based on the following two important observations. First, we observe that Web pages for a same topic from different languages usually share some common semantic patterns, though in different representation forms. Second, we also observe that the associations between word clusters and Web page classes are a more reliable carrier than raw words to transfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which we want classify Web pages, through two different paths: word cluster approximations and the associations between word clusters and Web page classes. Due to the reinforcement between these two different knowledge transfer paths, our approach can achieve better classification accuracy. We evaluate the proposed approach in extensive experiments using a real world cross-language Web page data set. Promising results demonstrate the effectiveness of our approach that is consistent with our theoretical analyses.

Web page classification is considered one of the most challenging research areas. Where the Web has a huge volume of unstructured and distributed documents that are related to a variety of domains; so, considering one base for the classification tasks will be extremely difficult. In addition, the Web is full of noise that will certainly harm the classifier performance especially if it is found in the classifier training data. Generally, it will be more valued to build domain-oriented classifiers (vertical classifi- ers) to classify pages related to a specific domain and compensate those classifiers with novel learning techniques to achieve better performance. The contribution of this paper is three edged; firstly, a novel learning technique called .Continuous Learning. is introduced. Secondly, the paper presents a new trend for Web page classification by presenting the domain-oriented classifiers (vertical classifiers). A new way of applying Bayes and K-Nearest Neighbor algorithms is introduced in order to build Domain Oriented Naïve Bayes (DONB) and Domain Oriented K-Nearest Neighbor (DOKNN) classifiers. The third contribution is combining both disciplines by introducing a novel classification strategy. Such strategy adds the continuous learning ability to Bayes theorem to build a Continuous learning domain oriented Naïve Bayes (CLNB) classifier. Where the overfitting problem has a great impact on most Web page classification techniques, continuous learning can be considered as a proposed solution. It allows the classifier to adapt itself continuously for achieving better performance. The proposed classifiers are tested; experimental results have shown that CLNB demonstrates significant performance improvement over both DONB and DOKNN where its accuracy goes beyond 94.1% after testing 1000 pages.

Web page classification is considered one of the most challenging research areas. Where the Web has a huge volume of unstructured and distributed documents that are related to a variety of domains; so, considering one base for the classification tasks will be extremely difficult. In addition, the Web is full of noise that will certainly harm the classifier performance especially if it is found in the classifier training data. Generally, it will be more valued to build domain-oriented classifiers (vertical classifi- ers) to classify pages related to a specific domain and compensate those classifiers with novel learning techniques to achieve better performance. The contribution of this paper is three edged; firstly, a novel learning technique called .Continuous Learning. is introduced. Secondly, the paper presents a new trend for Web page classification by presenting the domain-oriented classifiers (vertical classifiers). A new way of applying Bayes and K-Nearest Neighbor algorithms is introduced in order to build Domain Oriented Naïve Bayes (DONB) and Domain Oriented K-Nearest Neighbor (DOKNN) classifiers. The third contribution is combining both disciplines by introducing a novel classification strategy. Such strategy adds the continuous learning ability to Bayes theorem to build a Continuous learning domain oriented Naïve Bayes (CLNB) classifier. Where the overfitting problem has a great impact on most Web page classification techniques, continuous learning can be considered as a proposed solution. It allows the classifier to adapt itself continuously for achieving better performance. The proposed classifiers are tested; experimental results have shown that CLNB demonstrates significant performance improvement over both DONB and DOKNN where its accuracy goes beyond 94.1% after testing 1000 pages.

We propose a generic multiple classifier system based solely on pairwise classifiers to classify web pages. Web page classification is getting huge attention now because of its use in enhancing the accuracy of search engines and in summarizing web content for small-screen handheld devices. We have used a Support Vector Machine (SVM) as our core pair-wise classifier. The proposed system has produced very encouraging results on the problem web page classification. The proposed solution is totally generic and should be applicable in solving a wide range of multiple class pattern recognition problems.KeywordsSupport Vector MachineHandwritten CharacterSupport Vector ClassifierMultiple Classifier SystemStructure Support Vector MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In 2020, Most Filipinos are using the internet due to COVID-19 pandemic lockdowns. The internet is not limited to adults and children might be exposed to online adult content and abuse. The Philippine Internet Service providers fail to capture pornographic web pages that are not for child viewing. A Web Page classifier would help in detecting and classifying web pages. In this study, a total of 12000 web pages with adult content and academic web pages were collected using scrapy and existing datasets from DMOZ were used to create a Support Vector Machine (SVM) multi-class classifier. To improve the accuracy of the SVM model, data preprocessing was performed to remove noisy and irrelevant data from the dataset. The text in the web pages was used to train the SVM classifier by using Term Frequency and Inverse Document Frequency, Count vectorizer, and Word2vec Skip-gram embedding with TF-IDF as a multiplier. A series of experiments were conducted using multiple word embedding techniques. The SVM model built using word2vec with TF-IDF multiplier outperforms the SVM model built using TF-IDF and Count Vectorizer. The word embedding generated using word2vec was generated with a window size of 9 and a vector dimension of 900. The SVM model built using word2vec shows an S6% accuracy. The SMV model is deployed in the Django framework and a chrome plugin was created to use the SVM model using REST API.

These days, the Internet is developing at an exponential rate and can cover just about any data required. Nonetheless, the immense measure of web pages makes it more difficult to effectively discover the target data by a user. Therefore, an efficient method, for classifying this huge amount of data is essential if the web pages are to be exploited to its full potential. In the domain of automatic web page classifier many approaches have been tried to solve this problem using different Machine learning-based algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree, K-Nearest Neighbor (K-NN) and Neural Networks. However, there is a lack of comparison between these algorithms to find a better framework for the classification and analysis of health related web pages. In this research study, we compare two commonly used supervised Machine Learning algorithms; Support Vector Machines (SVM) and Naive Bayes to classify web pages which provide drugs related information of patients for example side effects, patient action and follow-up information for patients. We use Unified Medical Language System (UMLS) to annotate the health related concepts in Web pages and train SVM and Naive Bayes classifiers in General Architecture for Text Engineering to classify health related and non-health related Web pages. The evaluation was performed using K-fold cross validation using four runs on a data set of fifty Web pages. Results found that SVM performed better to classify health and non-health related pages in terms of precision, recall and F-measure.

Chinese Web page classification (WPC) has been considered as a hot research area in data mining. In order to effectively classify Web pages, we present a Web page categorization based on a least square support vector machine (LS-SVM) with latent semantic analysis (LSA). LSA uses singular value decomposition (SVD) to obtain latent semantic structure of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of Web page expression, and make use of summarization algorithm to reduce the noise of Web pages. A preliminary experimental comparison is made showing encouraging results.

Web page classification is the process of categorizing a web page into one or more classes which have been predetermined. If we remove all HTML tags from a web page, then this process can be considered as a text classification problem. However, this approach does not achieve high precision due to noisy contents, which always exist in regular HTML documents. To address this problem, we propose using a content extraction method to extract the main contents of the web pages and use them for the classification task. Experimental results show that the proposed method significantly improves the precision of the Vietnamese web page classification from 71% to 80%. It also indicates that context features such as the anchor texts of reference links and the contents of tags TITLE can use as a good summarization for web page contents.

With the phenomenal growth of World Wide Web and the huge number of web sites with so many web pages, finding relevant information becomes quite difficult for the web users. Web site promoters also need to ensure that their web site is on the top of the search results for better market share. Finding the right combination of keywords and placing them at the appropriate places may help in bringing the web site on the top of search results when a search engine searches the web sites and creates indexes. Over the years web site optimization research has reported encouraging results. Despite promising results, data mining techniques for web sites classification have hardly been applied. An appropriate implementation of classification of web pages may help designing the web pages accordingly. Here in this work data mining using R’s web scraping technique has been used for performing extraction of web pages and also pair of java tools called “website” and “webscrap” were developed to automatically get pages and for further analysis of the downloaded pages. This is the first attempt to analyze medical related nomenclature using.