research-article

A Local Method for ObjectRank Estimation

Authors:

Yuto Yamaguchi,

Toshiyuki Amagasa, and

Hiroyuki KitagawaAuthors Info & Claims

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

Pages 92 - 101

https://doi.org/10.1145/2539150.2539177

Published: 02 December 2013 Publication History

Abstract

ObjectRank is a method of link structure analysis to evaluate the importance of objects in a database. ObjectRank is known to be computationally expensive, because it requires iterative computations over a large graph. However, in many real applications, it is sufficient to compute the ObjectRank scores for only small fraction of objects. To address this problem, this paper proposes a novel method for estimating ObjectRank scores for specific objects by applying local computation over partial graphs, thereby allowing us to maintain low computational cost even for large graphs. Our basic idea is that, for a given target node, we induce a local graph by checking the edge weights and pruning the edges with considering their weights. We conduct experiments to compare our method with some comparative methods. The experimental results show that our method can reduce the computational cost while maintaining the accuracy.

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Cited By

Shiokawa H(2021)Fast ObjectRank for Large Knowledge DatabasesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_13(217-234)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_13
Sakakura YYamaguchi YAmagasa TKitagawa H(2014)An Improved Method for Efficient PageRank EstimationDatabase and Expert Systems Applications10.1007/978-3-319-10085-2_19(208-222)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10085-2_19

Index Terms

A Local Method for ObjectRank Estimation
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

753 pages

ISBN:9781450321136

DOI:10.1145/2539150

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

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IIWAS '13

IIWAS '13: The 15th International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2013

Vienna, Austria

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Cited By

Shiokawa H(2021)Fast ObjectRank for Large Knowledge DatabasesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_13(217-234)Online publication date: 30-Sep-2021
https://doi.org/10.1007/978-3-030-88361-4_13
Sakakura YYamaguchi YAmagasa TKitagawa H(2014)An Improved Method for Efficient PageRank EstimationDatabase and Expert Systems Applications10.1007/978-3-319-10085-2_19(208-222)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10085-2_19

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