Abstract
Query result diversification has drawn great research interests in recent years. Most previous work focuses on finding a locally diverse subset of a given finite result set, in which the results are as dissimilar to each other as possible. However, such a setup may not always hold. Firstly, we may need the result set to be globally diverse with respect to all possible demands behind a given query. Secondly, the result set may not be given before diversification. In this paper, we address these two problems in the scenario of keyword search on graphs. We first reasonably formalize a problem of coverage-oriented diversified keyword search on graphs. It aims to find both locally and globally diverse and also relevant results simultaneously while searching on graphs. The global diversity is defined as a query-dependent metric called coverage, which dynamically assigns weights to potential query demands with respect to their topological distances to the given keywords. Then, we present a search algorithm to solve our problem. It guarantees to return the optimal diverse result set, and can eliminate unnecessary and redundant diversity computation. Lastly, we perform both effectiveness and efficiency evaluation of our approach on DBPedia. Compared with the local diversification approach, our approach can improve the coverage and reduce the redundancy of search results remarkably.
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Change history
14 March 2020
In the originally published version of chapter 10 the funding information in the acknowledgement section was incomplete. This has now been corrected.
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Acknowledgement
This paper was supported by National Natural Science Foundation of China under Grant No. 61202036, 61502349 and 61572376 and Natural Science Foundation of Hubei Province under Grant No. 2018CFB616.
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Zhong, M., Wang, Y., Zhu, Y. (2018). Coverage-Oriented Diversification of Keyword Search Results on Graphs. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_10
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DOI: https://doi.org/10.1007/978-3-319-91458-9_10
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