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Hybrid entity clustering using crowds and data

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Abstract

Query result clustering has attracted considerable attention as a means of providing users with a concise overview of results. However, little research effort has been devoted to organizing the query results for entities which refer to real-world concepts, e.g., people, products, and locations. Entity-level result clustering is more challenging because diverse similarity notions between entities need to be supported in heterogeneous domains, e.g., image resolution is an important feature for cameras, but not for fruits. To address this challenge, we propose a hybrid relationship clustering algorithm, called Hydra, using co-occurrence and numeric features. Algorithm Hydra captures diverse user perceptions from co-occurrence and disambiguates different senses using feature-based similarity. In addition, we extend Hydra into \({\mathsf{Hydra }_\mathsf{gData }}\) with different sources, i.e., entity types and crowdsourcing. Experimental results show that the proposed algorithms achieve effectiveness and efficiency in real-life and synthetic datasets.

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Notes

  1. www.freebase.com.

  2. www.mpi-inf.mpg.de/yago-naga/yago.

  3. We adopt refined \(R_{ij}\) from [53] to discourage an extreme case of merging two distance clusters with large size difference, i.e., \(|C_{i_1}| \gg |C_{i_2}|\). More details on this refined notion can be found in [53].

  4. These entity types in Table 1 are collected from Freebase (www.freebase.com).

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Acknowledgments

This research was supported by the Ministry of Knowledge Economy (MKE), Korea and Microsoft Research, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency). (NIPA-2012-H0503-12-1036).

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Authors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Jongwuk Lee, Hyunsouk Cho, Jin-Woo Park, Young-rok Cha & Seung-won Hwang

  2. Microsoft Research Asia, Beijing, People’s Republic of China

    Zaiqing Nie

  3. Renmin University of China, Beijing, People’s Republic of China

    Ji-Rong Wen

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  1. Jongwuk Lee
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Correspondence to Seung-won Hwang.

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Lee, J., Cho, H., Park, JW. et al. Hybrid entity clustering using crowds and data. The VLDB Journal 22, 711–726 (2013). https://doi.org/10.1007/s00778-013-0328-8

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