Semantic HMC: Ontology-Described Hierarchy Maintenance in Big Data Context

Abstract : One of the biggest challenges in Big Data is the exploitation of Value from large volumes of data that are constantly changing. To exploit value, one must focus on extracting knowledge from these Big Data sources. To extract knowledge and value from unstructured text we propose using a Hierarchical Multi-Label Classification process called Semantic HMC that uses ontologies to describe the predictive model including the label hierarchy and the classification rules. To not overload the user, this process automatically learns the ontology-described label hierarchy from a very large set of text documents. This paper aims to present a maintenance process of the ontology-described label hierarchy relations with regards to a stream of unstructured text documents in the context of Big Data that incrementally updates the label hierarchy.
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Communication dans un congrès
On the Move to Meaningful Internet Systems: OTM 2015 Workshops, Oct 2015, Rhodes, Greece. Springer International Publishing, Lecture Notes in Computer Science 9416, pp 492-501, 2015, Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, EI2N, FBM, INBAST, ISDE, META4eS, and MSC 2015, Rhodes, Greece, October 26-30, 2015. Proceedings. 〈10.1007/978-3-319-26138-6_53〉
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https://hal.archives-ouvertes.fr/hal-01273809
Contributeur : Christophe Cruz <>
Soumis le : dimanche 14 février 2016 - 00:27:23
Dernière modification le : mercredi 12 septembre 2018 - 01:26:48

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Rafael Peixoto, Christophe Cruz, Nuno Silva. Semantic HMC: Ontology-Described Hierarchy Maintenance in Big Data Context. On the Move to Meaningful Internet Systems: OTM 2015 Workshops, Oct 2015, Rhodes, Greece. Springer International Publishing, Lecture Notes in Computer Science 9416, pp 492-501, 2015, Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, EI2N, FBM, INBAST, ISDE, META4eS, and MSC 2015, Rhodes, Greece, October 26-30, 2015. Proceedings. 〈10.1007/978-3-319-26138-6_53〉. 〈hal-01273809〉

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