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Conference Papers Year : 2013

Ontology-based Flexible Topic Classification of Crowdsourcing Textual Resources


The paper presents a solution to the problem of capitalizing in different contexts and by different stakeholders the time-stamped new documents produced by social Web sites (including news, blog entries, and uploaded documents). The solution core includes an ontology-based method to express the interest topics and to automatically classify them. For such textual content obtained in real-time, we propose an unsupervised text classification system based on general YAGO ontology, graph algorithms and a custom scoring method. The system shows good performance using only ontology information and the ontology structure itself. We compare our system against a SVM-based (Support Vector Machine) classic text classification approach. For determining the relevance of a specific document for a specific topic, our approach develops and compares the ontology sub graphs corresponding to the query and to the document. It leads to a high flexibility in terms of capitalizing the already classified documents when refining and changing the interest topic: a graph-based matching of the already obtained ontology-based document representation against the new query representation is enough to assess the document relevance.
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hal-04082680 , version 1 (26-04-2023)



Stefan Daniel Dumitrescu, Stefan Trausan-Matu, Mihaela Brut, Florence Sèdes. Ontology-based Flexible Topic Classification of Crowdsourcing Textual Resources. 5th International Conference on Management of Emergent Digital EcoSystems (MEDES 2013), Oct 2013, Luxembourg, Luxembourg. pp.145-151, ⟨10.1145/2536146.2536172⟩. ⟨hal-04082680⟩
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