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.
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This work is supported by IBM Research through Shared University Research grant and Ministry of Human Resource Development, Government of India the National Digital Library of India project.
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Appendix: List of Tasks for Assessment
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Sadhu, S., Bhowmick, P.K. (2019). Metadata-Based Automatic Query Suggestion in Digital Library Using Pattern Mining. In: Jatowt, A., Maeda, A., Syn, S. (eds) Digital Libraries at the Crossroads of Digital Information for the Future. ICADL 2019. Lecture Notes in Computer Science(), vol 11853. Springer, Cham. https://doi.org/10.1007/978-3-030-34058-2_20
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