Semanitic Keyword-based Search on Structured Data Sources

Semantic Keyword-based Search on Structured Data Sources pp 141-152 | Cite as

Toward Optimized Multimodal Concept Indexing

  • Navid Rekabsaz
  • Ralf Bierig
  • Mihai Lupu
  • Allan Hanbury
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Information retrieval on the (social) web moves from a pure term-frequency-based approach to an enhanced method that includes conceptual multimodal features on a semantic level. In this paper, we present an approach for semantic-based keyword search and focus especially on its optimization to scale it to real-world sized collections in the social media domain. Furthermore, we present a faceted indexing framework and architecture that relates content to semantic concepts to be indexed and searched semantically. We study the use of textual concepts in a social media domain and observe a significant improvement from using a concept-based solution for keyword searching. We address the problem of time-complexity that is critical issue for concept-based methods by focusing on optimization to enable larger and more real-world style applications.

Keywords

Semantic indexing Concept Social web Word2Vec 

References

  1. 1.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM (JACM) 45(6), 891–923 (1998)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Baroni, M., Dinu, G., Kruszewski, G.: Dont count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proc. of the 52nd Annual Meeting of the Association for. Comput. Linguist. 1, 238–247 (2014)Google Scholar
  3. 3.
    Clinchant, S., Ah-Pine, J., Csurka, G.: Semantic combination of textual and visual information in multimedia retrieval. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval (2011)Google Scholar
  4. 4.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision (at ECCV) (2004)Google Scholar
  5. 5.
    Dang, V., Bendersky, M., Croft, W.: Two-stage learning to rank for information retrieval. In: Proceedings of European Conference on Information Retrieval (2013)Google Scholar
  6. 6.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. (JASIS) 41, 391–407 (1990)CrossRefGoogle Scholar
  7. 7.
    Depeursinge, A., Müller, H.: Fusion techniques for combining textual and visual information retrieval. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF. The Information Retrieval Series, pp. 95–114. Springer, Berlin (2010)CrossRefGoogle Scholar
  8. 8.
    Eskevich, M., Jones, G.J., Aly, R., et al.: Multimedia information seeking through search and hyperlinking. In: Proceedings of the Annual ACM International Conference on Multimedia Retrieval (2013)Google Scholar
  9. 9.
    Ionescu, B., Popescu, A., Lupu, M., Gînsca, A.L., Boteanu, B., Müller, H.: Div150cred: a social image retrieval result diversification with user tagging credibility dataset. In: ACM Multimedia Systems Conference Series (2015)Google Scholar
  10. 10.
    Ionescu, B., Radu, A.-L., Menéndez, M., Müller, H., Popescu, A., Loni, B.: Div400: a social image retrieval result diversification dataset. In: Proceedings of ACM Multimedia Systems Conference Series (2014)Google Scholar
  11. 11.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  12. 12.
    Liu, C., Wang, Y.-M.: On the connections between explicit semantic analysis and latent semantic analysis. In: Proceedings of Conference on Information and Knowledge Management, New York, NY, USA (2012)Google Scholar
  13. 13.
    Liu, N., Dellandréa, E., Chen, L., Zhu, C., Zhang, Y., Bichot, C.-E., Bres, S., Tellez, B.: Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme. Comput. Vis. Image Underst. 117, 493–512 (2013)CrossRefGoogle Scholar
  14. 14.
    Magalhaes, J., Rüger, S.: Information-theoretic semantic multimedia indexing. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 619–626. ACM (2007)Google Scholar
  15. 15.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011)Google Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
  17. 17.
    Paramita, M.L., Grubinger, M.: Photographic image retrieval. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF: Experimental Evaluation in Visual Information Retrieval, pp. 141–162. Springer, Berlin (2010)CrossRefGoogle Scholar
  18. 18.
    Pham, T.-T., Maillot, N., Lim, J.-H., Chevallet, J.-P.: Latent semantic fusion model for image retrieval and annotation. In: Proceedings of Conference on Information and Knowledge Management (2007)Google Scholar
  19. 19.
    Rekabsaz, N., Bierig, R., Ionescu, B., Hanbury, A., Lupu, M.: On the use of statistical semantics for metadata-based social image retrieval. In: Proceedings of the 13th International Workshop on Content-Based Multimedia Indexing (CBMI) (2015)Google Scholar
  20. 20.
    Sabetghadam, S., Lupu, S., Bierig, R., Rauber, A.: A combined approach of structured and non-structured IR in multimodal domain. In: Proceedings of ACM International Conference on Multimedia Retrieval (2014)Google Scholar
  21. 21.
    Sahlgren, M.: An introduction to random indexing. In: Methods and Applications of Semantic Indexing Workshop in the Proceedings of Terminology and Knowledge Engineering (2005)Google Scholar
  22. 22.
    Thomee, B., Popescu, A.: Overview of the ImageCLEF 2012 flickr photo annotation and retrieval task. In: Proceedings of Cross-Language Evaluation Forum (CLEF) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Navid Rekabsaz
    • 1
  • Ralf Bierig
    • 1
  • Mihai Lupu
    • 1
  • Allan Hanbury
    • 1
  1. 1.Information and Software Engineering GroupVienna University of TechnologyViennaAustria

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