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Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Typicality-Based Across-Time Mapping of Entity Sets in Document Archives

Authors : Yijun Duan, Adam Jatowt, Sourav S. Bhowmick, Masatoshi Yoshikawa

Published in: Database Systems for Advanced Applications

Publisher: Springer International Publishing

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Abstract

News archives constitute a rich source of knowledge about the past societies. In order to effectively utilize such large and diverse accounts of the past, novel approaches need to be proposed. One of them is comparison of the past and present entities which can lay grounds for better comprehending the past and the present, as well as can support forecasting techniques. In this paper, we propose a novel research task of automatically generating across-time comparable entity pairs given two sets of entities, as well as we introduce an effective method to solve this task. The proposed model first applies the idea of typicality analysis to measure the representativeness of each entity. Then, it learns an orthogonal transformation between temporally distant entity collections. Finally, it generates a set of typical comparables based on a concise integer linear programming framework. We experimentally demonstrate the effectiveness of our method on the New York Times corpora through both qualitative and quantitative tests.

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previous chapter Selectivity Estimation on Set Containment Search
next chapter Unsupervised Entity Alignment Using Attribute Triples and Relation Triples
Footnotes
1
We experimentally set the value of \(\lambda \) to be 0.4 in Sec “Experiments”.
 
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Metadata
Title
Typicality-Based Across-Time Mapping of Entity Sets in Document Archives
Authors
Yijun Duan
Adam Jatowt
Sourav S. Bhowmick
Masatoshi Yoshikawa
Copyright Year
2019
Book
Database Systems for Advanced Applications

Print ISBN: 978-3-030-18575-6

Electronic ISBN: 978-3-030-18576-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-18576-3_21

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