Retrievability in an integrated retrieval system: an extended study
Tóm tắt
Tài liệu tham khảo
Adali, S., Emery, R.: A uniform framework for integrating knowledge in heterogeneous knowledge systems. In: Proceedings of the Eleventh International Conference on Data Engineering, Taipei, Taiwan, 6–10 March 1995. IEEE Computer Society, pp. 513–520 (1995). https://doi.org/10.1109/ICDE.1995.380362
Arguello, J.: Federated search in heterogeneous environments. SIGIR Forum 46(1), 78–79 (2012). https://doi.org/10.1145/2215676.2215686
Azzopardi, L., Vinay, V.: Retrievability: an evaluation measure for higher order information access tasks. In: Shanahan JG., Amer-Yahia S., Manolescu I., et al. (eds) Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, 26–30 Oct 2008. ACM, pp. 561–570 (2008). https://doi.org/10.1145/1458082.1458157
Bache, R., Azzopardi, L.: Improving Access to Large Patent Corpora, pp. 103–121. Springer-Verlag, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16175-9_4
Bashir, S., Rauber, A.: Analyzing document retrievability in patent retrieval settings. In: International Conference on Database and Expert Systems Applications, pp. 753–760. Springer (2009a). https://doi.org/10.1007/978-3-642-03573-9_63
Bashir, S., Rauber, A.: Identification of low/high retrievable patents using content-based features. In: Proceedings of the 2nd International Workshop on Patent Information Retrieval. Association for Computing Machinery, New York, NY, USA, PaIR ’09, pp. 9–16 (2009b). https://doi.org/10.1145/1651343.1651346
Bashir, S., Rauber, A.: Improving retrievability of patents with cluster-based pseudo-relevance feedback documents selection. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, CIKM ’09, pp. 1863–1866 (2009c). https://doi.org/10.1145/1645953.1646250
Bashir, S., Rauber, A.: On the relationship between query characteristics and ir functions retrieval bias. J. Am. Soc. Inf. Sci. Technol. 62(8), 1515–1532 (2011). https://doi.org/10.1002/asi.21549
Callan, J., Connell, M.: Query-based sampling of text databases. ACM Trans. Inf. Syst. (TOIS) 19(2), 97–130 (2001). https://doi.org/10.1145/382979.383040
Carevic, Z., Schüller, S., Mayr, P., et al.: Contextualised browsing in a digital library’s living lab. In: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, pp. 89–98 (2018). https://doi.org/10.1145/3197026.3197054
Carevic, Z., Roy, D., Mayr, P.: Characteristics of dataset retrieval sessions: experiences from a real-life digital library. In: International Conference on Theory and Practice of Digital Libraries, pp. 185–193. Springer (2020). https://doi.org/10.1007/978-3-030-54956-5_14
Carmel, D., Yom-Tov, E.: Estimating the Query Difficulty for Information Retrieval. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers (2010). https://doi.org/10.2200/S00235ED1V01Y201004ICR015
Carmel, D., Yom-Tov, E., Darlow, A., et al.: What makes a query difficult? In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR ’06, pp. 390–397 (2006). https://doi.org/10.1145/1148170.1148238
Cole, M., Liu, J., Belkin, N., et al.: Usefulness as the criterion for evaluation of interactive information retrieval. in: Proc HCIR, pp. 1–4 (2009)
Friedrich, T.: Looking for data. PhD thesis, Humboldt-Universität zu Berlin, Philosophische Fakultät (2020). https://doi.org/10.18452/22173
Gregory, K., Groth, P., Cousijn, H., et al.: Searching data: a review of observational data retrieval practices in selected disciplines. J. Assoc. Inf. Sci. Technol. 70(5), 419–432 (2019). https://doi.org/10.1002/asi.24165
Hienert, D., Mutschke, P.: A usefulness-based approach for measuring the local and global effect of IIR services. In: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, CHIIR ’16, pp. 153–162 (2016). https://doi.org/10.1145/2854946.2854962
Hienert, D., Kern, D., Boland, K., et al.: A digital library for research data and related information in the social sciences. In: 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 148–157. IEEE, Champaign, IL, USA (2019). https://doi.org/10.1109/JCDL.2019.00030
Kacprzak, E., Koesten, L.M., Ibáñez, L.D., et al.: A query log analysis of dataset search. In: International Conference on Web Engineering, pp. 429–436. Springer (2017). https://doi.org/10.1007/978-3-319-60131-1_29
Kacprzak, E., Koesten, L., Tennison, J., et al.: Characterising dataset search queries. In: Companion Proceedings of the The Web Conference 2018. International World Wide Web Conferences Steering Committee, WWW ’18, pp. 1485–1488 (2018). https://doi.org/10.1145/3184558.3191597
Kern, D., Mathiak, B.: Are there any differences in data set retrieval compared to well-known literature retrieval? In: International Conference on Theory and Practice of Digital Libraries, pp. 197–208. Springer (2015). https://doi.org/10.1007/978-3-319-24592-8_15
Kunze, S.R., Auer, S.: Dataset retrieval. In: 2013 IEEE Seventh International Conference on Semantic Computing, Irvine, CA, USA, 16–18 Sep 2013. IEEE Computer Society, pp. 1–8 (2013). https://doi.org/10.1109/ICSC.2013.12
Lalmas, M.: Aggregated search. In: Advanced Topics in Information Retrieval, The Information Retrieval Series, vol. 33, pp. 109–123. Springer (2011). https://doi.org/10.1007/978-3-642-20946-8_5
Roy, D., Carevic, Z., Mayr, P.: Studying retrievability of publications and datasets in an integrated retrieval system. In: JCDL ’22: The ACM/IEEE Joint Conference on Digital Libraries in 2022, Cologne, Germany, 20– 24 June 2022. ACM, p. 8 (2022). https://doi.org/10.1145/3529372.3530931
Samar, T., Traub, M.C., Ossenbruggen, J., et al.: Quantifying retrieval bias in web archive search. Int. J. Digit. Libr. 19(1), 57–75 (2018). https://doi.org/10.1007/s00799-017-0215-9
Sparck Jones, K., Walker, S., Robertson, S.: A probabilistic model of information retrieval: development and comparative experiments: part 1. Inf. Process. Manag. 36(6), 779–808 (2000). https://doi.org/10.1016/S0306-4573(00)00015-7
Traub, M.C., Samar, T., van Ossenbruggen, J., et al.: Querylog-based assessment of retrievability bias in a large newspaper corpus. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL 2016, Newark, NJ, USA, 19–23 June 2016. ACM, pp. 7–16 (2016). https://doi.org/10.1145/2910896.2910907
Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (2010). https://doi.org/10.1145/1852102.1852106
Wilkie, C., Azzopardi, L.: Best and fairest: an empirical analysis of retrieval system bias. In: Proceedings of the 36th European Conference on IR Research on Advances in Information Retrieval, vol. 8416, pp. 13–25. Springer-Verlag, Berlin, Heidelberg, ECIR 2014 (2014a). https://doi.org/10.1007/978-3-319-06028-6_2
Wilkie, C., Azzopardi, L.: A retrievability analysis: exploring the relationship between retrieval bias and retrieval performance. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, CIKM ’14, pp. 81–90 (2014b). https://doi.org/10.1145/2661829.2661948
Wilkie, C., Azzopardi, L.: A topical approach to retrievability bias estimation. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval. Association for Computing Machinery, New York, NY, USA, ICTIR ’16, pp. 119–122 (2016). https://doi.org/10.1145/2970398.2970437
Wilkie, C., Azzopardi, L.: Algorithmic bias: do good systems make relevant documents more retrievable? In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, CIKM ’17, pp. 2375–2378 (2017). https://doi.org/10.1145/3132847.3133135