An ontology-based mining approach for user search intent discovery
Yan Shen, Yuefeng Li, Yue Xu, Renato Iannella, Abdulmohsen Algarni, Xiaohui Tao · QUT ePrints (Queensland University of Technology) · 2011
Abstract Discovering proper search intents is a vi-tal process to return desired results. It is constantly a hot research topic regarding information retrieval in recent years. Existing methods are mainly limited by utilizing context-based mining, query expansion, and user profiling techniques, which are still suffering from the issue of ambiguity in search queries. In this pa-per, we introduce a novel ontology-based approach in terms of a world knowledge base in order to construct personalized ontologies for identifying adequate con-cept levels for matching user search intents. An iter-ative mining algorithm is designed for evaluating po-tential intents level by level until meeting the best re-sult. The propose-to-attempt approach is evaluated in a large volume RCV1 data set, and experimental results indicate a distinct improvement on top precision after compared with baseline models.