TY - JOUR
T1 - User-centric Query Refinement and Processing Using Granularity Based Strategies
AU - Zeng, Y.
AU - Zhong, N.
AU - Wang, Y.
AU - Qin, Y.
AU - Huang, Z.
AU - Zhou, H
AU - Yao, Y
AU - van Harmelen, F.A.H.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
U2 - 10.1007/s10115-010-0298-8
DO - 10.1007/s10115-010-0298-8
M3 - Article
SN - 0219-1377
VL - 27
SP - 419
EP - 450
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
IS - 3
ER -
