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Conference Paper

Search-based Recommendation : The Case for Difficult Predictions

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Torbati,  Ghazaleh Haratinezhad
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Torbati, G. H., Weikum, G., & Yates, A. (2023). Search-based Recommendation: The Case for Difficult Predictions. In Y. Ding, J. Tang, J. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), The ACM Web Conference 2023 (pp. 318-321). New York, NY: ACM. doi:10.1145/3543873.3587374.


Cite as: https://hdl.handle.net/21.11116/0000-000C-DC45-F
Abstract
Questions on class cardinality comparisons are quite tricky to answer and
come with its own challenges. They require some kind of reasoning since web
documents and knowledge bases, indispensable sources of information, rarely
store direct answers to questions, such as, ``Are there more astronauts or
Physics Nobel Laureates?'' We tackle questions on class cardinality comparison
by tapping into three sources for absolute cardinalities as well as the
cardinalities of orthogonal subgroups of the classes. We propose novel
techniques for aggregating signals with partial coverage for more reliable
estimates and evaluate them on a dataset of 4005 class pairs, achieving an
accuracy of 83.7%.