Web Page Annotation Using Web Usage Mining and Domain Knowledge Ontology

Today’s world the growth of the WWW has increased tremendously, the user is totally relying on web for information. Search engine provides the result pages to the user but all are not relevant so the challenging task is extracting the pages from web and provide to the user. WUM is an approach to extract knowledge and use it to the different purposes. In this paper new semantic approach is proposed based on WUM and Domain Knowledge Ontology. Ontology database preparation, it is also challenging task in this project

ENTITY EXTRACTION USING STATISTICAL METHODS USING INTERACTIVE KNOWLEDGE MINING FRAMEWORK

There are various kinds of valuable semantic information about real-world entities embedded in web pages and databases. Extracting and integrating these entity information from the Web is of great significance. Comparing to traditional information extraction problems, web entity extraction needs to solve several new challenges to fully take advantage of the unique characteristic of the Web. In this paper, we introduce our recent work on statistical extraction of structured entities, named entities, entity facts and relations from Web. We also briefly introduce iKnoweb, an interactive knowledge mining framework for entity information integration. We will use two novel web applications, Microsoft Academic Search (aka Libra) and EntityCube, as working examples

Glimpses of Semantic Web Technologies and Related Case Studies

Semantic web is a platform of new evolution in rapidly developing World Wide Web. Semantic web refers to extracting knowledge from large amount of data. The purpose of this paper is to give a first-hand information and description for the semantic web technology. Although several research works have been carried out in the high semantic web technology, the semantic web is yet vastly unexplored. A semantic web technological innovation is rapidly changing traditional methods of searching data and how search engines work. Few prominent semantic web case studies are presented. One of the popular applications of XML RDF is Really Simple Syndication (RSS) feed which is discussed in detail

Pattern extraction from the world wide web

The World Wide Web is a source of huge amount of unlabeled information spread across different sources in varied formats. This presents us with both opportunities and challenges in leveraging such large amount of unstructured data to build knowledge bases and to extract relevant information. As part of this thesis, a semi-supervised logistic regression model called “Dual Iterative Pattern Relation Extraction” proposed by Sergey Brin is selected for further investigation. DIPRE presents a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. This project built in JAVA using Google AJAX Search API includes designing, implementing and testing DIPRE approach in extracting various relationships from the web

Cluster Optimization for Improved Web Usage Mining

Now days, World Wide Web (WWW) has become rich and most powerful source of information. Conversely, it has become tricky and critical task to retrieve actual information due to its continuous expansion in dimensions. Web Usage Mining is a step-wise technique of extracting useful access patterns of the user from web. Web personalization makes use of web usage mining techniques, for knowledge acquisition process done by analyzing the user navigational patterns. The web page personalization involves clustering of different web pages having similar navigation patterns for an individual. Since cluster size expands due to the frequent access, optimization or shrinking the size of clusters becomes a chief consideration. This paper proposes a tactic of cluster optimization based on concept of swarm intelligence techniques. Later on based on the recognition of user access patterns, clustering is implemented using neural fuzzy approach i.e. NEF Class algorithm and cluster optimization is implemented using Ant Nest Mate Approach

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis

What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors

"© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL 13, ISS 2, (APR 2019)} http://doi.acm.org/10.1145/3316810"[EN] A Web template is a resource that implements the structure and format of a website, making it ready for plugging content into already formatted and prepared pages. For this reason, templates are one of the main development resources for website engineers, because they increase productivity. Templates are also useful for the final user, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information, such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. There exist many techniques and tools for template extraction, but, unfortunately, it is not clear at all which template extractor should a user/system use, because they have never been compared, and because they present different (complementary) features such as precision, recall, and efficiency. In this work, we compare the most advanced template extractors. We implemented and evaluated five of the most advanced template extractors in the literature. To compare all of them, we implemented a workbench, where they have been integrated and evaluated. Thanks to this workbench, we can provide a fair empirical comparison of all methods using the same benchmarks, technology, implementation language, and evaluation criteria.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Ciencia, Innovacion y Universidades/AEI under grant TIN2016-76843-C4-1-R and by the Generalitat Valenciana under grants PROMETEO-II/2015/013 (SmartLogic) and Prometeo/2019/098 (DeepTrust).Alarte, J.; Silva, J.; Tamarit Muñoz, S. (2019). What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors. ACM Transactions on the Web. 13(2):9:1-9:19. https://doi.org/10.1145/3316810S9:19:19132Alarte, J., Insa, D., Silva, J., & Tamarit, S. (2015). TeMex. Proceedings of the 24th International Conference on World Wide Web - WWW ’15 Companion. doi:10.1145/2740908.2742835Julián Alarte David Insa Josep Silva and Salvador Tamarit. 2016. Site-Level Web Template Extraction Based on DOM Analysis. 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An XJML-based wrapper generator for Web information extraction. Proceedings of the 1999 ACM SIGMOD international conference on Management of data - SIGMOD ’99. doi:10.1145/304182.304570Ma, L., Goharian, N., Chowdhury, A., & Chung, M. (2003). Extracting unstructured data from template generated web documents. Proceedings of the twelfth international conference on Information and knowledge management - CIKM ’03. doi:10.1145/956863.956961Manjula, R., & Chilambuchelvan, A. (2013). Extracting templates from Web pages. 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE). doi:10.1109/icgce.2013.6823541Christopher D. Manning Prabhakar Raghavan and Hinrich SchÃijtze. 2008. Introduction to Information Retrieval. Cambridge University Press New York NY. Christopher D. Manning Prabhakar Raghavan and Hinrich SchÃijtze. 2008. Introduction to Information Retrieval. Cambridge University Press New York NY.Meng, X., Hu, D., & Li, C. (2003). Schema-guided wrapper maintenance for web-data extraction. Proceedings of the fifth ACM international workshop on Web information and data management - WIDM ’03. doi:10.1145/956699.956701Nguyen, D. Q., Nguyen, D. Q., Pham, S. B., & Bui, T. D. (2009). A Fast Template-Based Approach to Automatically Identify Primary Text Content of a Web Page. 2009 International Conference on Knowledge and Systems Engineering. doi:10.1109/kse.2009.39Schäfer, R. (2016). Accurate and efficient general-purpose boilerplate detection for crawled web corpora. Language Resources and Evaluation, 51(3), 873-889. doi:10.1007/s10579-016-9359-2Sivakumar, P. (2015). Effectual Web Content Mining using Noise Removal from Web Pages. Wireless Personal Communications, 84(1), 99-121. doi:10.1007/s11277-015-2596-7Song, D., Sun, F., & Liao, L. (2013). A hybrid approach for content extraction with text density and visual importance of DOM nodes. Knowledge and Information Systems, 42(1), 75-96. doi:10.1007/s10115-013-0687-xR. 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Forecast Pupil Performance using SVM & Logistic Regression

The primary reason why machine learning has gained so much prominence nowadays is that it enables accurate and reliable decision making by extracting hidden relationships between various features present in the data. For this purpose the technique such as supervised methodologies and unsupervised methodologies are used. For this reason, machine learning can be used in almost any area of work to help in proper decision making and predictions. In our current project, we are trying to predict this student performance that utilizes supervised machine learning methodologies like support vector machines , logistic regression, random forests etc. We have also tried to publish this model to a web application so that it can be used by the academic community. The information extracted and the knowledge gained by extracting information from the educational data set would be helpful for predicting the student grades and their future performance. The main intention of the project used to predict student performance beforehand and help them get good grades in future. This would help in increasing the motivation levels of the students, improving their grades, decreasing their dropout ratio, and preparing better students for a better world

TechMiner: Extracting Technologies from Academic Publications

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision