Towards Comparative Web Content Mining using Object Oriented Model

Web content data are heterogeneous in nature; usually composed of different types of contents and data structure. Thus, extraction and mining of web content data is a challenging branch of data mining. Traditional web content extraction and mining techniques are classified into three categories: programming language based wrappers, wrapper (data extraction program) induction techniques, and automatic wrapper generation techniques. First category constructs data extraction system by providing some specialized pattern specification languages, second category is a supervised learning, which learns data extraction rules and third category is automatic extraction process. All these data extraction techniques rely on web document presentation structures, which need complicated matching and tree alignment algorithms, routine maintenance, hard to unify for vast variety of websites and fail to catch heterogeneous data together. To catch more diversity of web documents, a feasible implementation of an automatic data extraction technique based on object oriented data model technique, 00Web, had been proposed in Annoni and Ezeife (2009). This thesis implements, materializes and extends the structured automatic data extraction technique. We developed a system (called WebOMiner) for extraction and mining of structured web contents based on object-oriented data model. Thesis extends the extraction algorithms proposed by Annoni and Ezeife (2009) and develops an automata based automatic wrapper generation algorithm for extraction and mining of structured web content data. Our algorithm identifies data blocks from flat array data structure and generates Non-Deterministic Finite Automata (NFA) pattern for different types of content data for extraction. Objective of this thesis is to extract and mine heterogeneous web content and relieve the hard effort of matching, tree alignment and routine maintenance. Experimental results show that our system is highly effective and it performs the mining task with 100% precision and 96.22% recall value

Portable extraction of partially structured facts from the web

A novel fact extraction task is defined to fill a gap between current information retrieval and information extraction technologies. It is shown that it is possible to extract useful partially structured facts about different kinds of entities in a broad domain, i.e. all kinds of places depicted in tourist images. Importantly the approach does not rely on existing linguistic resources (gazetteers, taggers, parsers, etc.) and it ported easily and cheaply between two very different languages (English and Latvian). Previous fact extraction from the web has focused on the extraction of structured data, e.g. (Building-LocatedIn-Town). In contrast we extract richer and more interesting facts, such as a fact explaining why a building was built. Enough structure is maintained to facilitate subsequent processing of the information. For example, this partial structure enables straightforward template-based text generation. We report positive results for the correctness and interest of English and Latvian facts and for the utility of the extracted facts in enhancing image captions

A Novel Approach for Clustering of Heterogeneous Xml and HTML Data Using K-means

Data mining is a phenomenon of extraction of knowledgeable information from large sets of data. Now a day�s data will not found to be structured. However, there are different formats to store data either online or offline. So it added two other categories for types of data excluding structured which is semi structured and unstructured. Semi structured data includes XML etc. and unstructured data includes HTML and email, audio, video and web pages etc. In this paper data mining of heterogeneous data over Xml and HTML, implementation is based on extraction of data from text file and web pages by using the popular data mining techniques and final result will be after sentimental analysis of text, semi-structured documents that is XML files and unstructured data extraction of web page with HTML code, there will be an extraction of structure/semantic of code alone and also both structure and content.. Implementation of this paper is done using R is a programming language on Rstudio environment which commonly used in statistical computing, data analytics and scientific research. It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize, and present data

A Novel Approach for Clustering of Heterogeneous Xml and HTML Data Using K-means

Data mining is a phenomenon of extraction of knowledgeable information from large sets of data. Now a day’s data will not found to be structured. However, there are different formats to store data either online or offline. So it added two other categories for types of data excluding structured which is semi structured and unstructured. Semi structured data includes XML etc. and unstructured data includes HTML and email, audio, video and web pages etc. In this paper data mining of heterogeneous data over Xml and HTML, implementation is based on extraction of data from text file and web pages by using the popular data mining techniques and final result will be after sentimental analysis of text, semi-structured documents that is XML files and unstructured data extraction of web page with HTML code, there will be an extraction of structure/semantic of code alone and also both structure and content.. Implementation of this paper is done using R is a programming language on Rstudio environment which commonly used in statistical computing, data analytics and scientific research. It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize, and present data

Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data

This paper gives a short overview of specific logical approaches to data extraction, data management, and reasoning about data. In particular, we survey theoretical results and formalisms that have been obtained and used in the context of the Lixto Project at TU Wien, the DIADEM project at the University of Oxford, and the VADA project, which is currently being carried out jointly by the universities of Edinburgh, Manchester, and Oxford. We start with a formal approach to web data extraction rooted in monadic second order logic and monadic Datalog, which gave rise to the Lixto data extraction system. We then present some complexity results for monadic Datalog over trees and for XPath query evaluation. We further argue that for value creation and for ontological reasoning over data, we need existential quantifiers (or Skolem terms) in rule heads, and introduce the Datalog± family. We give an overview of important members of this family and discuss related complexity issues

Information Aggregation using the Cameleon# Web Wrapper

Cameleon# is a web data extraction and management tool that provides information aggregation with advanced capabilities that are useful for developing value-added applications and services for electronic business and electronic commerce. To illustrate its features, we use an airfare aggregation example that collects data from eight online sites, including Travelocity, Orbitz, and Expedia. This paper covers the integration of Cameleon# with commercial database management systems, such as MS SQL Server, and XML query languages, such as XQuery

The method of data search and analysis from the Internet resources for the formation of actual requirements for candidates

У статті розглянуті питання екстракції даних з Web-ресурсів на прикладі збору інформації щодо вакансій. Виділено три основні взаємодіючі сторони цього процесу: джерело даних, база даних та експерт. Розглянуто основні проблематичні сторони процесу видобування даних, а саме: наявність декількох джерел даних; представлення даних різними мовами; видобування даних з різних форматів файлів; багаторазові повторювані операції і безперервні оновлення. Проаналізовано та визначено переваги та недоліки таких методів WebMiningяк:аналіз DOM дерева, парсинг рядків, використання регулярних виразів, XML парсинг та візуальний підхід. У статті застосовано метод аналізу DOM дерева з використання XPath. Запропоновано використання методу компараторной ідентифікації для моделювання процесу видобування даних. Представлено приклад застосування наведеного підходу для ідентифікації певної вакансії на сайті пошуку роботи. Розроблено тезаурус вимог роботодавців та налаштовано роботу парсера.The article deals with the issues of data extraction from Web-resources on the example of gathering information on vacancies. There are three main interacting parts of this process: data source, database, and an expert. The main problematic aspects of the data mining process are the availability of several data sources; data representation in different languages; extraction data from different file formats; multiple updating of repetitive operations and data. The advantages and disadvantages of Web Mining methods were analyzed and defined. They are DOM tree analysis, line parsing, usage of regular expressions, XML parsing and visual approach. Method of DOM tree using XPath was applied in the paper. The method of comparator identification for modeling the data extraction process was proposed. The component, which receives the search topic and the search start page, carries out a thematically directed extraction. The comparator compares the extracted word from the page with the words of the search model. The application of the above-mentioned approach is presented for identifying a vacancy on the job search site. The thesaurus of employers' requirements is developed. Words-indicators of the required vacancies are presented in three languages. The parser work was set up. The parser processes the documents and retrieves the data used to fill a particular data model. The developed module works as follows. It begins to work with obtaining an array of necessary pages from the selected Web site. The next step is the analysis of Web page‘s structure. Then it is necessary to get the content of a specific HTML page, which contains the necessary information for its further retrieval and processing. As a result ―vacancy model‖ is developed. The model should include the following elements: vacancy title; date of adding a job to the site; the city where the applicant needs to work; requirements for the candidate; applicant duties; working conditions. Extraction of requirements, liabilities, and conditions was defined as the most problematic area, whereas the same information can be presented in a different way. In order to unify requirement experts were engaged

When humans and machines collaborate: Cross-lingual Label Editing in Wikidata

The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data