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Erschienen in:
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Relevance Models Based on the Knowledge Gap

verfasst von : Yasin Ghafourian

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

Search systems are increasingly used for gaining knowledge through accessing relevant resources from a vast volume of content. However, search systems provide only limited support to users in knowledge acquisition contexts. Specifically, they do not fully consider the knowledge gap which we define as the gap existing between what the user knows and what the user intends to learn. The effects of considering the knowledge gap for knowledge acquisition tasks remain largely unexplored in search systems. We propose to model and incorporate the knowledge gap into search algorithms. We plan to explore to what extent the incorporation of the knowledge gap leads to an improvement in the performance of search systems in knowledge acquisition tasks. Furthermore, we aim to investigate and design a metric for the evaluation of the search systems’ performance in the context of knowledge acquisition tasks.

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Vorheriges Kapitel Graph-Enhanced Document Representation for Court Case Retrieval
Nächstes Kapitel Evidence-Based Early Rumor Verification in Social Media
Literatur
1.
2.
Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRef
3.
Gadiraju, U., Yu, R., Dietze, S., Holtz, P.: Analyzing knowledge gain of users in informational search sessions on the web. In: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pp. 2–11 (2018)
4.
Sulaiman, H.H.: Information needs and information seeking behaviour at the College of Medicine, The Sultan Qaboos University, Oman/Huda Sulaiman al-Haddabi. Ph.D. thesis, University of Malaya (2005)
5.
Nkomo, N.: A comparative analysis of the web information seeking behaviour of students and staff at the University of Zululand and the Durban University of Technology. Ph.D. thesis (2009)
6.
Igbinovia, M.O., Ikenwe, I.I.J.C.: Information seeking behavior of academic librarians for effective performance: a study of UNIBEN, AAU and AUCHI Poltechnic, Edo state, Nigeria (2014)
7.
8.
Kelly, D., Belkin, N.J.: A user modeling system for personalized interaction and tailored retrieval in interactive IR. Proc. Am. Soc. Inf. Sci. Technol. 39(1), 316–325 (2002)CrossRef
9.
Cosijn, E., Ingwersen, P.: Dimensions of relevance. Inf. Process. Manag. 36(4), 533–550 (2000)CrossRef
10.
Belkin, N.J., Oddy, R.N., Brooks, H.M.: Ask for information retrieval: part I. background and theory. J. Doc. 38(2), 61–71 (1982)
11.
Dervin, B., Nilan, M.: Information needs and uses. In: Williams, M. (ed.) Annual Review of Information Science and Technology, vol. 21, pp. 3–33 (1986)
12.
Thellefsen, M., Thellefsen, T., Sørenson, B.: A pragmatic semeiotic perspective on the concept of information need and its relevance for knowledge organization. KO Knowl. Organ. 40(4), 213–224 (2014)CrossRef
13.
14.
Stojanovic, N.: On the role of a user’s knowledge gap in an information retrieval process. In: Proceedings of the 3rd International Conference on Knowledge Capture, pp. 83–90 (2005)
15.
Soldaini, L.: The knowledge and language gap in medical information seeking. In: ACM SIGIR Forum, vol. 52, pp. 178–179. ACM, New York (2019)
16.
Zhang, Y., Liu, C.: Users’ knowledge use and change during information searching process: a perspective of vocabulary usage. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, pp. 47–56 (2020)
17.
Xiong, Y.: An automated feedback system to support student learning of conceptual knowledge in writing-to-learn activities. Ph.D. thesis, New Jersey Institute of Technology (2020)
18.
Thaker, K., Zhang, L., He, D., Brusilovsky, P.: Recommending remedial readings using student knowledge state. International Educational Data Mining Society (2020)
19.
Lindstaedt, S.N., Beham, G., Kump, B., Ley, T.: Getting to know your user – unobtrusive user model maintenance within work-integrated learning environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 73–87. Springer, Heidelberg (2009). https://​doi.​org/​10.​1007/​978-3-642-04636-0_​9CrossRef
20.
Syed, R.: Models and algorithms for understanding and supporting learning goals in information retrieval. Ph.D. thesis (2020)
21.
Zhao, W., Ran, W., Liu, H.: Paper recommendation based on the knowledge gap between a researcher’s background knowledge and research target. Inf. Process. Manag. 52(5), 976–988 (2016)CrossRef
22.
23.
Daghio, M.M., Fattori, G., Ciardullo, A.V.: Assessment of readability and learning of easy-to-read educational health materials designed and written with the help of citizens by means of two non-alternative methods. Adv. Health Sci. Educ. 11(2), 123–132 (2006)CrossRef
24.
Monica Daghio, M., Fattori, G., Ciardullo, A.V.: Evaluation of easy-to-read information material on healthy life-styles written with the help of citizens’ collaboration through networking. Promot. Educ. 13(3), 91–196 (2006)
25.
Schaumberg, A.: Variation in closeness to reality of standardized resuscitation scenarios: effects on the success of cognitive learning of medical students. Anaesthesist 64(4), 286–291 (2015)CrossRef
26.
de Resende, H.C., Slamnik-Krijestorac, N., Both, C.B., Marquez-Barja, J.: Introducing engineering undergraduate students to network management techniques: a hands-on approach using the Citylab Smart City. In: 2020 IEEE Global Engineering Education Conference (EDUCON), pp. 1316–1324. IEEE (2020)
27.
Vakkari, P., Völske, M., Potthast, M., Hagen, M., Stein, B.: Modeling the usefulness of search results as measured by information use. Inf. Process. Manag. 56(3), 879–894 (2019)CrossRef
28.
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., Dietze, S.: Predicting user knowledge gain in informational search sessions. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 75–84 (2018)
29.
Zhang, X., Liu, J., Cole, M., Belkin, N.: Predicting users’ domain knowledge in information retrieval using multiple regression analysis of search behaviors. J. Am. Soc. Inf. Sci. 66(5), 980–1000 (2015)
30.
Bloom, B.S.: Taxonomy of educational objectives, handbook I: The cognitive domain. New York: David Mckay Co Inc. as cited in file. D:/bloom. html (1956)
31.
Anderson, L.W., Bloom, B.S., et al.: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman, New York (2001)
32.
Ghafourian, Y., Knoth, P., Hanbury, A.: Information retrieval evaluation in knowledge acquisition tasks. In: Proceedings of the Third Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR), pp. 88–95 (2021)
Metadaten
Titel
Relevance Models Based on the Knowledge Gap
verfasst von
Yasin Ghafourian
Copyright-Jahr
2022
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_60