test_moissonnage_selune
type: Agent

Deriving Semantic Objects from the Structured Web
type: Dataset
contact point: Organization
  fn: CCSD
keyword: content-extraction
keyword: relation-discovery
keyword: theses
keyword: infoinfo-wbcomputer-science-csweb
keyword: temporal-analysis
keyword: infoeu-reposemanticsdoctoralthesis
keyword: structural-patterns
keyword: induction
keyword: algorithms
keyword: ontologies
landing page: https://theses.hal.science/tel-00922459
description: This thesis focuses on the extraction and analysis of Web data objects, investigated from different points of view: temporal, structural, semantic. We first survey different strategies and best practices for deriving temporal aspects of Web pages, together with a more in-depth study on Web feeds for this particular purpose. Next, in the context of dynamically-generated Web pages by content management systems, we present two keyword-based techniques that perform article extraction from such pages. Keywords, either automatically acquired through a Tf−Idf analysis, or extracted from Web feeds, guide the process of object identification, either at the level of a single Web page (SIGFEED algorithm), or across different pages sharing the same template (FOREST algorithm). We finally present, in the context of the deep Web, a generic framework which aims at discovering the semantic model of a Web object (here, data record) by, first, using FOREST for the extraction of objects, and second, by representing the implicit rdf:type similarities between the object attributes and the entity of the Web interface as relationships that, together with the instances extracted from the objects, form a labeled graph. This graph is further aligned to a generic ontology like YAGO for the discovery of the graph's unknown types and relations.
issued: 2026-05-07T16:49:49.339233
language: en
modified: 2026-05-07T16:49:49.339238

Deriving Semantic Objects from the Structured Web
type: Distribution
access URL: https://theses.hal.science/tel-00922459
format: HTML
issued: 2026-05-07T16:49:49.346244
modified: 2026-05-07T16:49:49.329263