6533b7d0fe1ef96bd125b7a1

RESEARCH PRODUCT

Predictive and Evolutive Cross-Referencing for Web Textual Sources

Christophe CruzAurélie BertauxThomas Hassan

subject

Competitive intelligenceComputer science[SPI] Engineering Sciences [physics]Big data02 engineering and technologyReasonningFocused crawlerDiscovery[INFO] Computer Science [cs]World Wide WebKnowledge-based systems[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI][SPI]Engineering Sciences [physics]020204 information systems0202 electrical engineering electronic engineering information engineeringLeverage (statistics)[INFO]Computer Science [cs]Semantic Web[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]business.industryOntologyFocused CrawlerWork in processClassificationAdaptive[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsCross-ReferencingClasssification020201 artificial intelligence & image processingbusinessClassifier (UML)Model

description

International audience; One of the main challenges in the domain of competitive intelligence is to harness important volumes of information from the web, and extract the most valuable pieces of information. As the amount of information available on the web grows rapidly and is very heterogeneous, this process becomes overwhelming for experts. To leverage this challenge, this paper presents a vision for a novel process that performs cross-referencing at web scale. This process uses a focused crawler and a semantic-based classifier to cross-reference textual items without expert intervention, based on Big Data and Semantic Web technologies. The system is described thoroughly, and interests of this work in progress are discussed.

yearjournalcountryeditionlanguage
2017-07-18
https://u-bourgogne.hal.science/hal-01858785