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KEYSTONE WG3: Activities and Results Overview on User Interaction

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Semantic Keyword-Based Search on Structured Data Sources (IKC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10546))

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Abstract

User Interaction WG investigates issues related to the semantic disambiguation of the queries based on the context and on the keyword annotations with respect to some reference ontologies, the development of languages for keyword searching and the use of users’ feedbacks for improving results.

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Notes

  1. 1.

    http://www.keystone-cost.eu/keystone/6th-mc-meeting-and-winter-2017-wg-meeting/.

References

  1. Alexopoulos, P., Wallace, M.: Creating domain-specific semantic lexicons for aspect-based sentiment analysis. In: 10th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2015, Trento, Italy, 5–6 November 2015, pp. 1–6 (2015). https://doi.org/10.1109/SMAP.2015.7370083

  2. Audhkhasi, K., Rosenberg, A., Sethy, A., Ramabhadran, B., Kingsbury, B.: End-to-end ASR-free keyword search from speech. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, 5–9 March 2017, pp. 4840–4844 (2017). https://doi.org/10.1109/ICASSP.2017.7953076

  3. Belhajjame, K., Grigori, D., Harmassi, M., Yahia, M.B.: Keyword-based search of workflow fragments and their composition. Trans. Comput. Collect. Intell. 26, 67–90 (2017). https://doi.org/10.1007/978-3-319-59268-8_4

    Google Scholar 

  4. Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: Selectivity-based keyword extraction method. Int. J. Semantic Web Inf. Syst. (IJSWIS) 12(3), 1–26 (2016)

    Article  Google Scholar 

  5. Cadegnani, S., Guerra, F., Ilarri, S., del Carmen Rodríguez-Hernández, M., Lado, R.T., Velegrakis, Y., Amaro, R.: Exploiting linguistic analysis on urls for recommending web pages: a comparative study. Trans. Comput. Collect. Intell. 26, 26–45 (2017). https://doi.org/10.1007/978-3-319-59268-8_2

    Google Scholar 

  6. Jean, P., Harispe, S., Ranwez, S., Bellot, P., Montmain, J.: Uncertainty detection in natural language: a probabilistic model. In: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, Nîmes, France, 13–15 June 2016, pp. 10:1–10:10 (2016). https://doi.org/10.1145/2912845.2912873

  7. Kökciyan, N., Türkay, R., Üsküdarli, S., Yolum, P., Bakir, B., Acar, B.: Semantic description of liver CT images: an ontological approach. IEEE J. Biomed. Health Inform. 18(4), 1363–1369 (2014). https://doi.org/10.1109/JBHI.2014.2298880

    Article  Google Scholar 

  8. Slaimi, F., Sellami, S., Boucelma, O., Ben, H.A.: A multigraph approach for web services recommendation. In: Debruyne, C., Panetto, H., Meersman, R., Dillon, T., Kühn, E., O’Sullivan, D., Ardagna, C.A. (eds.) OTM 2016. LNCS, vol. 10033, pp. 282–299. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48472-3_16

    Chapter  Google Scholar 

  9. Stanković, R., Krstev, C., Obradović, I., Kitanović, O.: Improving document retrieval in large domain specific textual databases using lexical resources. Trans. Comput. Collect. Intell. 26, 162–185 (2017). https://doi.org/10.1007/978-3-319-59268-8_8

    Google Scholar 

  10. Terziyan, V., Golovianko, M., Cochez, M.: TB-structure: collective intelligence for exploratory keyword search. In: Calì, A., Gorgan, D., Ugarte, M. (eds.) KEYSTONE 2016. LNCS, vol. 10151, pp. 171–178. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53640-8_15

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Aix-Marseille University, CNRS, ENSAM, Toulon University, LSIS UMR 7296, 13397, Marseille, France

    Omar Boucelma

Authors
  1. Omar Boucelma
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Correspondence to Omar Boucelma .

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Editors and Affiliations

  1. Gdańsk University of Technology, Gdańsk, Poland

    Julian Szymański

  2. UniversitĂ  degli Studi di Trento, Trento, Italy

    Yannis Velegrakis

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Boucelma, O. (2018). KEYSTONE WG3: Activities and Results Overview on User Interaction. In: Szymański, J., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2017. Lecture Notes in Computer Science(), vol 10546. Springer, Cham. https://doi.org/10.1007/978-3-319-74497-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-74497-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74496-4

  • Online ISBN: 978-3-319-74497-1

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