Predictive and Evolutive Cross-Referencing for Web Textual Sources

Abstract : 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.
Type de document :
Communication dans un congrès
Computing Conference, Jul 2017, Londres, United Kingdom. IEEE, 2017 COMPUTING CONFERENCE, pp.1114-1122, 2017, 〈https://ieeexplore.ieee.org/document/8252230/〉
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01858785
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : mardi 21 août 2018 - 12:52:13
Dernière modification le : vendredi 7 décembre 2018 - 16:48:04

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  • HAL Id : hal-01858785, version 1

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Hassan Thomas, Christophe Cruz, Aurélie Bertaux. Predictive and Evolutive Cross-Referencing for Web Textual Sources. Computing Conference, Jul 2017, Londres, United Kingdom. IEEE, 2017 COMPUTING CONFERENCE, pp.1114-1122, 2017, 〈https://ieeexplore.ieee.org/document/8252230/〉. 〈hal-01858785〉

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