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The VLDB Journal

, Volume 27, Issue 3, pp 369–394 | Cite as

Cost-effective conceptual design using taxonomies

  • Yodsawalai Chodpathumwan
  • Ali Vakilian
  • Arash Termehchy
  • Amir Nayyeri
Regular Paper
First Online:
Received:
Revised:
Accepted:

Abstract

It is known that annotating entities in unstructured and semi-structured datasets by their concepts improves the effectiveness of answering queries over these datasets. Ideally, one would like to annotate entities of all relevant concepts in a dataset. However, it takes substantial time and computational resources to annotate concepts in large datasets, and an organization may have sufficient resources to annotate only a subset of relevant concepts. Clearly, it would like to annotate a subset of concepts that provides the most effective answers to queries over the dataset. We propose a formal framework that quantifies the amount by which annotating entities of concepts from a taxonomy in a dataset improves the effectiveness of answering queries over the dataset. Because the problem is \(\mathbf {NP}\)-hard, we propose efficient approximation and pseudo-polynomial time algorithms for several cases of the problem. Our extensive empirical studies validate our framework and show accuracy and efficiency of our algorithms.

Keywords

Concept annotation Information extraction Data curation Taxonomies Cost-effective data curation 
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Notes

Acknowledgements

This study was funded by National Science Foundation with Grant No. IIS-1421247, CCF-0938071, CCF-0938064 and CNS-0716532.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yodsawalai Chodpathumwan
    • 1
  • Ali Vakilian
    • 2
  • Arash Termehchy
    • 3
  • Amir Nayyeri
    • 3
  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Oregon State UniversityCorvallisUSA

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