Under the context of large-scale scientific literatures, this paper provides a user-centric approach for refining and processing incomplete or vague query based on cognitive- and granularity-based strategies. From the viewpoints of user interests retention and granular information processing, we examine various strategies for user-centric unification of search and reasoning. Inspired by the basic level for human problem-solving in cognitive science, we refine a query based on retained user interests. We bring the multi-level, multi-perspective strategies from human problem-solving to large-scale search and reasoning. The power/exponential law-based interests retention modeling, network statistics-based data selection, and ontology-supervised hierarchical reasoning are developed to implement these strategies. As an illustration, we investigate some case studies based on a large-scale scientific literature dataset, DBLP. The experimental results show that the proposed strategies are potentially effective. © 2010 Springer-Verlag London Limited.
Zeng, Y., Zhong, N., Wang, Y., Qin, Y., Huang, Z., Zhou, H., ... van Harmelen, F. A. H. (2011). User-centric Query Refinement and Processing Using Granularity Based Strategies. Knowledge and Information Systems, 27(3), 419-450. https://doi.org/10.1007/s10115-010-0298-8