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Exploring Large Digital Libraries by Multimodal Criteria

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Research and Advanced Technology for Digital Libraries (TPDL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9819))

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Abstract

Digital library (DL) support for different information seeking strategies (ISS) has not evolved as fast as their amount of offered stock or presentation quality. However, several studies argue for the support of explorative ISS in conjunction to the directed query-response paradigm. Hence, this paper presents a primarily explorative research system prototype for metadata harvesting allowing multimodal access to DL stock for researchers during the research idea development phase, i.e., while the information need (IN) is vague. To address evolving INs, the prototype also allows ISS transitions, e.g., to OPACs, if accuracy is needed.

As its second contribution, the paper presents a curated data set for digital humanities researchers that is automatically enriched with metadata derived by different algorithms including content-based image features. The automatic enrichment of originally bibliographic metadata is needed to support the exploration of large metadata stock as traditional metadata does not always address vague INs.

The presented proof of concept clearly shows that use case-specific metadata facilitates the interaction with large metadata corpora.

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Notes

  1. 1.

    http://www.europeana.eu/; http://dp.la/.

  2. 2.

    Open Archives Initiative Protocol for Metadata Harvesting; https://www.openarchives.org/pmh/.

  3. 3.

    Online Public Access Catalog.

  4. 4.

    http://www.numpy.org/; http://www.scipy.org/; http://pandas.pydata.org/.

  5. 5.

    https://gephi.org/; https://d3js.org/.

  6. 6.

    http://dublincore.org/.

  7. 7.

    Although one might argue whether humans do not err.

  8. 8.

    For the sake of readability, we decided to publish the full source in form of a documented Jupyter (http://jupyter.org/) notebook as a supplement to this paper to limit the discussion of algorithmic parameters to a minimum.

  9. 9.

    For instance, the RAK main manual, the German counterpart to Anglo-American Cataloguing Rules, is a 627 pages long document.

  10. 10.

    Because of the unavailability of ground truths similar to our corpus, the limited amount of data in the prototype, and the non-destructive extension of the metadata records, we decided against a full automation of the evaluation. However, the resulting clusters are stable enough to be checked against common authority files.

  11. 11.

    The in principle optional normalization was carried out primarily to offer researchers a homogeneous image data set.

  12. 12.

    http://opencv.org/.

  13. 13.

    http://sbb.berlin/sbbrowse.

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Author information

Authors and Affiliations

  1. Department of Information and Data Management, Berlin State Library – Prussian Cultural Heritage, Berlin, Germany

    David Zellhöfer

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  1. David Zellhöfer
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Correspondence to David Zellhöfer .

Editor information

Editors and Affiliations

  1. Universität Duisburg-Essen , Duisburg, Germany

    Norbert Fuhr

  2. Hungarian Academy of Science , Budapest, Hungary

    László Kovács

  3. Leibniz Universität Hannover , Hannover, Germany

    Thomas Risse

  4. Leibniz Universität Hannover , Hannover, Germany

    Wolfgang Nejdl

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Zellhöfer, D. (2016). Exploring Large Digital Libraries by Multimodal Criteria. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2016. Lecture Notes in Computer Science(), vol 9819. Springer, Cham. https://doi.org/10.1007/978-3-319-43997-6_24

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  • DOI: https://doi.org/10.1007/978-3-319-43997-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43996-9

  • Online ISBN: 978-3-319-43997-6

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