Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Intelligent Text Categorization and Clustering
Intelligent Text Categorization and Clustering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Journal of Theoretical and Applied Electronic Commerce Research
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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.