PBA: Partition and Blocking Based Alignment for Large Knowledge Bases

  • Yan Zhuang
  • Guoliang Li
  • Zhuojian Zhong
  • Jianhua Feng
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)

Abstract

The vigorous development of semantic web has enabled the creation of a growing number of large-scale knowledge bases across various domains. As different knowledge-bases contain overlapping and complementary information, automatically integrating these knowledge bases by aligning their classes and instances can improve the quality and coverage of the knowledge bases. Existing knowledge-base alignment algorithms have some limitations: (1) not scalable, (2) poor quality, (3) not fully automatic. To address these limitations, we develop a scalable partition-and-blocking based alignment framework, named Pba, which can automatically align knowledge bases with tens of millions of instances efficiently. Pba contains three steps. (1) Partition: we propose a new hierarchical agglomerative co-clustering algorithm to partition the class hierarchy of the knowledge base into multiple class partitions. (2) Blocking: we judiciously divide the instances in the same class partition into small blocks to further improve the performance. (3) Alignment: we compute the similarity of the instances in each block using a vector space model and align the instances with large similarities. Experimental results on real and synthetic datasets show that our algorithm significantly outperforms state-of-art approaches in efficiency, even by an order of magnitude, while keeping high alignment quality.

Keywords

Resource Description Framework Priority Queue Vector Space Model Alignment Process Uniform Resource Identifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgement

This work was supported by the National Grand Fundamental Research 973 Program of China (2015CB358700), the National Natural Science Foundation of China (61422205, 61373024, 61472198), Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology, “NExT Research Center”, Singapore (WBS:R-252-300-001-490), Huawei, Shenzhou, FDCT/116/2013/A3, MYRG105(Y1-L3)-FST13-GZ, National 863 Program of China (2012AA012600), Chinese Special Project of Science and Technology (2013zx01039-002-002) and the National Center for International Joint Research on E-Business Information Processing (2013B01035).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yan Zhuang
    • 1
    • 2
  • Guoliang Li
    • 1
  • Zhuojian Zhong
    • 3
  • Jianhua Feng
    • 1
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.PLA Navy General HospitalBeijingChina
  3. 3.Beijing University of Posts and TelecommunicationsBeijingChina

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