Ontology Based Data Mining Approach on Web Documents

International Journal of Software Innovation, 2018

The task of extracting the used feature vector in mining tasks (classification, clustering …etc.) is considered the most important task for enhancing the text processing capabilities. This paper proposes a novel approach to be used in building the feature vector used in web text document classification process; adding semantics in the generated feature vector. This approach is based on utilizing the benefit of the hierarchal structure of the WordNet ontology, to eliminate meaningless words from the generated feature vector that has no semantic relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text, also enriching the feature vector by concatenating each word with its corresponding WordNet lexical category. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting technique. The proposed ...

In this article is described a proposal to help users in the arduous task that means recovering information from the Web, specially when queries are about a subject or specific approach. For this, we suggested the use of an ontology whose instances are Web pages links about the domain on which the ontology was constructed, taking advantage of the order and categorization that it offers, to guide the user through the concepts that integrates it and find information related to them. The creation of an ontology about a particular domain and the necessary activities to get an automatic classification of the Web pages like instances in the ontology are described.

Today, the notion of Semantic Web has emerged as a prominent solution to the problem of organizing the immense information provided by World Wide Web, and its focus on supporting a better co-operation between humans and machines is noteworthy. Ontology forms the major component of Semantic Web in its realization. However, manual method of ontology construction is time-consuming, costly, error-prone and inflexible to change and in addition, it requires a complete participation of knowledge engineer or domain expert. To address this issue, researchers hoped that a semi-automatic or automatic process would result in faster and better ontology construction and enrichment. Ontology learning has become recently a major area of research, whose goal is to facilitate construction of ontologies, which reduces the effort in developing ontology for a new domain. However, there are few research studies that attempt to construct ontology from semi-structured Web pages. In this paper, we present a complete framework for ontology learning that facilitates the semi-automation of constructing and enriching web site ontology from semi structured Web pages. The proposed framework employs Web Content Mining and Web Usage mining in extracting conceptual relationship from Web. The main idea behind this concept was to incorporate the web author's ideas as well as web users’ intentions in the ontology development and its evolution.

Proceedings of the Third International Conference on Web Information Systems Engineering, 2002. WISE 2002., 2002

In recent years, we have witnessed the continual growth in the use of ontologies in order to provide a mechanism to enable machine reasoning. This paper describes an automatic classifier, which focuses on the use of ontologies for classifying Web pages with respect to the Dewey Decimal Classification (DDC) and Library of Congress Classification (LCC) schemes. Firstly, we explain how these ontologies can be built in a modular fashion, and mapped into DDC and LCC. Secondly, we propose the formal definition of a DDC-LCC and an ontology-classification-scheme mapping. Thirdly, we explain the way the classifier uses these ontologies to assist classification. Finally, an experiment in which the accuracy of the classifier was evaluated is presented. The experiment shows that our approach results an improved classification in terms of accuracy. This improvement, however, comes at a cost in a low overage ratio due to the incompleteness of the ontologies used. c

2005

In this paper, we deal with the problem of analyzing and classifying web documents to several major categories/classes in a given domain using domain ontology. We present the ontology-based web content mining methodology that contains such main stages as collecting a training set of labeled documents from a given domain, building a classification model above this domain given the domain ontology, and classification of new documents via the induced model. We tested the proposed methodology in a specific domain, namely web pages containing information about production of certain chemicals. Using our methodology, we are interested to identify all relevant web documents while ignoring the documents that do not contain any relevant information. Our system receives as input an OWL file built in Protege tool, which contains the domain-specific ontology, and a set of web documents classified by a human expert as "relevant" or "non-relevant". We use a language-independent key-phrase extractor with integrated ontology parser (defined in a given language) for creating the database from input documents and use it as a training set for the classification algorithm. The system classification accuracy using various levels of ontology is evaluated.The current version of our system supports web content mining in English, Arabic, Russian, and Hebrew languages.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012

This paper presents a new inductive learning method for conceptual classification of multi-label texts in web mining based on ontology through Term Space Reduction (TSR) and through using mutual information measure. Laboratory results show the presented method has high precision in compare to existing methods of SVM, Find Similar, Naïve Bayes Nets, and Decision Trees. It should be noted that break-even point is used in micro-averaging for appropriate classification of data complex entitled "Reuters-21578 Apte Split".

Web-based information available has increased dramatically. How to gather useful information from the web has become a challenging issue for users. Current web information gathering systems attempt to satisfy user requirements by capturing their information needs. For this purpose, user profiles are created for user background knowledge description. User profiles represent the concept models possessed by users when gathering web information. A concept model is implicitly possessed by users and is generated from their background knowledge. While this concept model cannot be proven in laboratories, many web oncologists have observed it in user behavior. Ontology mining discovers interesting and on-topic knowledge from the concepts, semantic relations, and instances in ontology. In this section, a 2D ontology mining method is introduced: Specificity and Exhaustively. Specificity (denoted spe) describes a subject’s focus on a given topic. Exhaustively restricts a subject’s semantic space dealing with the topic. This method aims to investigate the subjects and the strength of their associations in ontology. Our work assumes that all user local instance repositories have content-based descriptors referring to the subjects; however, a large volume of documents existing on the web may not have such content-based descriptors. For this problem, strategies like ontology mapping and text classification/clustering were suggested. The investigation will extend the applicability of the ontology model to the majority of the existing web documents and increase the contribution and significance of the present work. Keywords—Ontology, Web information, Web model, ontology mining, knowledge base.

2014

Finding relevant information on the Internet became a real challenge. This is to some extent due to the volume of data available and the lack of structure in many Web sites. Web usage mining is an important area and fast developing mining on the Internet. The purpose of Web mining is the development of techniques and systems to detect patterns of things and processes on the World Wide Web and the Internet for performance systems that appear to adapt. Ontology is some knowledge that can be used to describe the information on the Web. In this work we propose a framework for generating ontology based on web usage mining . We have implemented all stages of the system which are data acquisition, web mining and ontology creation. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and final review of the ontology .

International Journal of Information and Education Technology, 2012

Journal of Computing and Information Technology, 2005