Spatiooral pseudo relevance feedback for large-scale and heterogeneous scientific repositories

Shin'Ichi Takeuchi, Yuhei Akahoshi, Bun Theang Ong, Komei Sugiura, Koji Zettsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

As larger and larger amounts of data are harvested, finding just the right piece of information out of this noisy and heterogeneous ocean of data remains challenging. Many widely adopted scientific data search engines continue to be mainly based on text semantics. However, it is not uncommon in scientific big data applications to face collected data that do not possess text information. In this scenario, search engines fail to retrieve potentially relevant data. For instance, even though Pangaea, a digital data library and a publisher for earth system science, contains more than 400,000 datasets, more than 98% lack sufficient text information. In this work, we propose a novel pseudo relevance feedback method based on spatiooral and text (STT) information for scientific big data: STT-PRF. Although STT-PRF may simultaneously use STT information, we show that the missing values in space, time or/and the text are handled efficiently. STT-PRF is especially robust even without text information. We tested our STT-PRF method using the Pangaea repository on our Cross-DB Search Platform, which is a search engine for scientific big data based on various latent correlations. Experimental evaluations on such standard metrics as nDCG and Precision/Recall show that STT-PRF outperforms the standard baseline methods.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014
EditorsPeter Chen, Peter Chen, Hemant Jain
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages669-676
Number of pages8
ISBN (Electronic)9781479950577
DOIs
Publication statusPublished - 2014 Sep 22
Event3rd IEEE International Congress on Big Data, BigData Congress 2014 - Anchorage, United States
Duration: 2014 Jun 272014 Jul 2

Publication series

NameProceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Conference

Conference3rd IEEE International Congress on Big Data, BigData Congress 2014
CountryUnited States
CityAnchorage
Period14/6/2714/7/2

Keywords

  • information retrieval
  • pseudo relegance feedback
  • query expansion
  • scientific data
  • spatiooral and text information

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Takeuchi, SI., Akahoshi, Y., Ong, B. T., Sugiura, K., & Zettsu, K. (2014). Spatiooral pseudo relevance feedback for large-scale and heterogeneous scientific repositories. In P. Chen, P. Chen, & H. Jain (Eds.), Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014 (pp. 669-676). [6906843] (Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.Congress.2014.100