Lifting user generated comments to SIOC

International audienceHTML boilerplate code is acting on webpages as presentation directives for a browser to display data to a human end user. For the machine, our community made tremenduous e orts to provide querying endpoints using consensual schemas, protocols, and principles since the avent of the Linked Data paradigm. These data lifting e orts have been the primary materials for bootstraping the Web of data. Data lifting usually involves an original data structure from which the semantic architect has to produce a mapper to RDF vocabularies. Less e orts are made in order to lift data produced by a Web mining process, due to the di culty to provide an e cient and scalable solution. Nonetheless, the Web of documents is mainly composed of natural language twisted in HTML boilerplate code, and few data schemas can be mapped into RDF. In this paper, we present CommentsLifter, a system that is able to lift SIOC data from user-generated comments in the Web 2.0

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

Web Data Extraction from Template Pages

No Abstrac

The Syllabus Based Web Content Extractor (SBWCE)

Syllabus Based Web Content Extractor (SBWCE) introduces a new technique of Syllabus Based Web Content Mining. It makes the Syllabus Based Web Content Extraction easy and creates an instant online book view based on the links relevant to the given Syllabus. Three important contributions are made by the current work. First, as multiple format educational information is needed for Syllabus based content; the technique used makes the finding of such content easier. Second, a new approach for capturing and recording the heuristics involved during searching by experts is used. Third, the grouping of Syllabus Words for precise extraction is exploited. This paper introduces SBWCE and presents the related details

Mining user-generated comments

International audience—Social-media websites, such as newspapers, blogs, and forums, are the main places of generation and exchange of user-generated comments. These comments are viable sources for opinion mining, descriptive annotations and information extraction. User-generated comments are formatted using a HTML template, they are therefore entwined with the other information in the HTML document. Their unsupervised extraction is thus a taxing issue – even greater when considering the extraction of nested answers by different users. This paper presents a novel technique (CommentsMiner) for unsupervised users comments extraction. Our approach uses both the theoretical framework of frequent subtree mining and data extraction techniques. We demonstrate that the comment mining task can be modelled as a constrained closed induced subtree mining problem followed by a learning-to-rank problem. Our experimental evaluations show that CommentsMiner solves the plain comments and nested comments extraction problems for 84% of a representative and accessible dataset, while outperforming existing baselines techniques