Paper Info
Title
Metadata-Based Automatic Query Suggestion In Digital Library Using Pattern Mining
Abstract
This paper presents a Query Auto-Completion (QAC) framework that aims at assisting users in a digital library to specify their search intent with reduced effort. The proposed system suggests metadata-based facets to users as they specify their queries into the system. In this work, we model the facet-based QAC problem as frequent pattern mining problem where the system aims at leveraging association among different facet combinations. Among several frequent pattern mining algorithms, the present work make use of FP-Growth to discover facet patterns at large-scale. These facet patterns represented in form of association rules are used for online query auto-completion or suggestion. A prototype QAC augmented digital library search system is implemented by considering a limited bibliographic dataset (35K resources) of the National Digital Library of India (NDLI: https://ndl.iitkgp.ac.in ) portal. We perform extensive experiments to measure the quality of query suggestions and QAC augmented retrieval performance. Significant improvement over baseline search system is observed in both the aspects mentioned above.
Year
DOI
Venue
2019
10.1007/978-3-030-34058-2_20
DIGITAL LIBRARIES AT THE CROSSROADS OF DIGITAL INFORMATION FOR THE FUTURE, ICADL 2019
Keywords
Field
DocType
Query suggestion, Frequent pattern mining, Query auto completion
Metadata,Information retrieval,Computer science,Association rule learning,Facet (geometry),Digital library,Search intent
Conference
Volume
ISSN
Citations 
11853
0302-9743
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Susmita Sadhu100.34
Plaban Kumar Bhowmick2208.62