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Mis-categorized entities detection

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Abstract

Entity categorization, the process of categorizing entities into groups, is an important problem with many applications. However, in practice, many entities are mis-categorized, such as Google Scholar and Amazon products. In this paper, we study the problem of discovering mis-categorized entities from a given group of categorized entities. This problem is inherently hard: All entities within the same group have been “well” categorized by the state-of-the-art solutions. Apparently, it is nontrivial to differentiate them. We propose a novel rule-based framework to solve this problem. It first uses positive rules to compute disjoint partitions of entities, where the partition with the largest size is taken as the correctly categorized partition, namely the pivot partition. It then uses negative rules to identify mis-categorized entities in other partitions that are dissimilar to the entities in the pivot partition. We describe optimizations on applying these rules and discuss how to generate positive/negative rules. In addition, we propose novel strategies to resolve inconsistent rules. Extensive experimental results on real-world datasets show the effectiveness of our solution.

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Notes

  1. https://scholar.google.com/citations?view_op=top_venues.

  2. Here, we use exact string matching for example. We can also use approximate matching based on similarity functions.

  3. We assign each entity a partition ID and utilize a union-find data structure. If e and \(e'\) are verified that they satisfy a positive rule, we update their partition ID to the same ID. Assume the partition ID of e is i and that of \(e'\) is j, and \(i<j\), we change the partition ID of \(e'\) to i.

  4. \(\lambda \) is a constant that determines the approximation ratio. It has been proved in [22] that \(\lambda \) must be greater than 2.

  5. We gathered the Google Scholar data at the end of the year 2016, which may be dirtier than the current Google Scholar pages. However, mis-categorized entities still exist in the current version due to the paper assignment method applied in Google Scholar.

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Acknowledgements

This work was supported by NSF of China (Grant Nos. 61902017, 61925205, 61632016, 61521002, 61661166012), Huawei, TAL Education Group, China Postdoctoral Science Foundation (2019M650468) and China Scholarship Council. Note that Ning Wang’s partial work was supported by National Key R&D Program of China (2018YFC0809800) National Basic Research Program of China (973 Program) (Grant No. 2015CB358700), Fundamental Research Funds for the Central Universities (Grant No. 2019RC015).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Shuang Hao & Ning Wang

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Shuang Hao, Guoliang Li & Jianhua Feng

  3. Qatar Computing Research Institute, HBKU, Ar-Rayyan, Qatar

    Nan Tang

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  1. Shuang Hao
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Correspondence to Guoliang Li.

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Hao, S., Tang, N., Li, G. et al. Mis-categorized entities detection. The VLDB Journal 30, 515–536 (2021). https://doi.org/10.1007/s00778-021-00653-w

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