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

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

A Data Transformation System for Biological Data Sources

Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before