Intelligent tagging of online texts using fuzzy logic
Robertas Damaševičius, Remigijus Valys, Marcin Woźniak · 2016
We propose four fuzzy-logic based models of tag recommendation, which are based on the interpretation of word frequency as a fuzzy membership function, and provide experimental results of tag recommendation for a variety of text datasets using different fuzzy logic operators. The novelty of the proposed models is the use of fuzzy logic modeling concepts to define a set of tags, the use of an existing set of tags for the selection of tags to strengthen the selection of most relevant (i.e., commonly used) tags, and the possibility to use an ontology to select semantically generalized tags. A system developed using the proposed models is adaptive (adapts the recommended tags to the existing set of tags), has a feedback (after each tagging, the set of tags and the dictionary are updated), is personalized (each user develops its own set of tags), and is semantics-aware (uses an ontology to refine tags). The models are validated using five sets of texts with different topics (technology, cooking, carrier, scientific, nature) and different length.