Skip to main content

Recommending Scientific Videos Based on Metadata Enrichment Using Linked Open Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11057)

Abstract

The amount of available videos in the Web has significantly increased not only for entertainment etc., but also to convey educational or scientific information in an effective way. There are several web portals that offer access to the latter kind of video material. One of them is the TIB AV-Portal of the Leibniz Information Centre for Science and Technology (TIB), which hosts scientific and educational video content. In contrast to other video portals, automatic audiovisual analysis (visual concept classification, optical character recognition, speech recognition) is utilized to enhance metadata information and semantic search. In this paper, we propose to further exploit and enrich this automatically generated information by linking it to the Integrated Authority File (GND) of the German National Library. This information is used to derive a measure to compare the similarity of two videos which serves as a basis for recommending semantically similar videos. A user study demonstrates the feasibility of the proposed approach.

Keywords

  • Video recommendation
  • Semantic enrichment
  • Linked data

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-00066-0_25
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-00066-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

Notes

  1. 1.

    As of June 16, 2017.

References

  1. Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198. ACM (2016)

    Google Scholar 

  2. Davidson, J., et al.: The Youtube video recommendation system. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 293–296. ACM (2010)

    Google Scholar 

  3. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)

  4. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    CrossRef  Google Scholar 

  5. Reiner, U.: Automatic analysis of dewey decimal classification notations. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications, pp. 697–704. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78246-9_82

    CrossRef  Google Scholar 

  6. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-49257-7_15

    CrossRef  Google Scholar 

  7. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)

    CrossRef  Google Scholar 

  8. Vahdat, A., Zhou, G.-T., Mori, G.: Discovering video clusters from visual features and noisy tags. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 526–539. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_34

    CrossRef  Google Scholar 

  9. Waitelonis, J., Sack, H.: Augmenting video search with linked open data. In: I-SEMANTICS, pp. 550–558 (2009)

    Google Scholar 

  10. Waitelonis, J., Sack, H.: Towards exploratory video search using linked data. Multimedia Tools Appl. 59(2), 645–672 (2012)

    CrossRef  Google Scholar 

  11. Wang, J., Zhu, X., Gong, S.: Video semantic clustering with sparse and incomplete tags. In: AAAI, pp. 3618–3624 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Otto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Medrek, J., Otto, C., Ewerth, R. (2018). Recommending Scientific Videos Based on Metadata Enrichment Using Linked Open Data. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J. (eds) Digital Libraries for Open Knowledge. TPDL 2018. Lecture Notes in Computer Science(), vol 11057. Springer, Cham. https://doi.org/10.1007/978-3-030-00066-0_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00066-0_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00065-3

  • Online ISBN: 978-3-030-00066-0

  • eBook Packages: Computer ScienceComputer Science (R0)