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İlgi Sıralamalarının Artırımlı Olarak Geliştirilmesi: Pennant Erişimle Desteklenen Yeni Bir Yöntem Önerisi

Yıl 2022, , 169 - 203, 30.06.2022
https://doi.org/10.24146/tk.1062751

Öz

Amaç: İlgi sıralaması algoritmaları erişilen belgeleri arama sorgularıyla belgeler arasındaki konusal benzerlik (ilgi) derecelerine göre sıralar. Bu çalışmanın amacı; bir olasılıksal konu modelleme algoritması ile atıf verilerine dayanan “pennant erişim”in birleşiminden oluşan yeni bir ilgi sıralaması yöntemi geliştirmektir. Veri Kaynakları ve Yöntem: Geliştirdiğimiz yöntemi yaklaşık 435 bin fizik makalesinden oluşan iSearch derlemi üzerinde uyguladık. Önce 65 sorgu için derlemdeki tüm makalelerin başlıkları ve özetleri üzerinde konu modelleme algoritmasını çalıştırarak ilgi sıralamalarını elde ettik. Daha sonra pennant erişim yöntemini uygulayarak elde ettiğimiz atıf bilgilerini mevcut ilgi sıralamalarını tümleştirmek (fusion) ve daha da geliştirmek için kullandık. Böylece hem aranan konunun farklı yönlerini kapsayan hem de konuyla marjinal ilgili olan makalelerden oluşan daha iyi ilgi sıralamaları elde ettik. Maksimum Marjinal İlgi (MMR, Maximum Marginal Relevance) algoritmasının farklı ilgi sıralamaları üzerindeki etkilerini ayrı ayrı inceleyerek önerdiğimiz yöntemin erişim performansını değerlendirdik. Bulgular: Bulgular konu modelleme algoritması ile elde edilen ilgi sıralamalarında makalelerin başlıklarında ve özetlerinde geçen bazı terimlerin bazen göz ardı edilebildiğini göstermektedir. Ama bu sıralamalar atıf verilerine dayanan pennant erişimle desteklendiğinde, kullanılan terimlerin bağlamları hakkında ek bilgiler elde edilmekte ve sonuçta ilgi düzeyleri daha yüksek ve çeşitli (interdisipliner) makaleler içeren daha zenginleştirilmiş ilgi sıralamaları oluşturulmaktadır. Dahası, erişim çıktıları araştırmacıların önceliklerine göre kolayca yeniden sıralanabilmektedir (kişiselleştirme). Sonuç: Önerdiğimiz yöntemde pennant erişim tekniklerini kullanarak mevcut ilgi sıralaması algoritmalarının artırımlı olarak iyileştirilmesi üzerinde odaklandık. Bu yöntemin hesaplama yükü, sağlamlık, tekrarlanabilirlik ve ölçeklenebilirlik açılarından dinamik derlemler üzerinde sınandıktan sonra zamanla TR-Dizin, Web of Science ve Scopus gibi hem yerel hem de uluslararası bilgi sistemlerinde de kullanılabileceği kanısındayız. Özgünlük: Bu araştırmada yeni bir ilgi sıralaması yöntemi önerilmektedir. Bildiğimiz kadarıyla bu çalışma, LDA konu modelleme algoritması ile elde edilen ilgi sıralamalarının atıf verilerine dayanan pennant erişim teknikleriyle artırımlı olarak geliştirilebileceğini gösteren ilk çalışmadır.

Teşekkür

iSearch derlemiyle ilgili yardımları için iSearch Team’e (Peter Ingwersen, Birger Larsen, Haakon Lund ve Marianne Lykke), çalışmanın önceki sürümünü okuyarak değerli önerilerde bulunan Prof. Dr. Umut Al ve Prof. Dr. Fazlı Can’a teşekkür ederiz.

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Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Yıl 2022, , 169 - 203, 30.06.2022
https://doi.org/10.24146/tk.1062751

Öz

Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.

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  • Tonta, Y. (1995). Bilgi erişim sistemleri. Türk Kütüphaneciliği, 9(3), 302-314. https://eprints.rclis.org/9571/
  • Tonta, Y. ve Akbulut, M. (2021). Uluslararası dergilerde yayımlanan Türkiye adresli makalelerin atıf etkisini artıran faktörler. Türk Kütüphaneciliği, 35(3), 388-409. https://doi.org/10.24146/tk.933159
  • Vergoulis, T., Chatzopoulos, S., Kanellos, I., Deligiannis, P., Tryfonopoulos, C. ve Dalamagas, T. (2019). BIP! finder: Facilitating scientific literature search by exploiting impact-based ranking. Proceedings of the 28th ACM International Conference on Information and Knowledge Management içinde (s. 2937-2940). ACM. https://doi.org/10.1145/3357384.3357850
  • Verma, M., Yılmaz, E. ve Craswell, N. (2016). On obtaining effort based judgements for information retrieval. Proceedings of the 9th ACM International Conference on Web Search and Data Mining içinde (s. 277-286). ACM. https://doi.org/10.1145/2835776.2835840
  • Wang, X., Zhai, C. ve Roth, D. (2013). Understanding evolution of research themes: a probabilistic generative model for citations. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining içinde (s. 1115-1123). ACM. https://doi.org/10.1145/2487575.2487698
  • White, H. D. (2007a). Combining bibliometrics, information retrieval, and relevance theory. Part 1: First examples of a synthesis. Journal of the American Society for Information Science and Technology, 58, 536-559. https://doi.org/10.1002/asi.20543
  • White, H. D. (2007b). Combining bibliometrics, information retrieval, and relevance theory. Part 2: Some implications for information science. Journal of the American Society for Information Science and Technology, 58, 583-605. https://doi.org/10.1002/asi.20542
  • White, H. D. (2009). Pennants for Strindberg and Persson. Celebrating scholarly communication studies: A festschrift for Olle Persson at his 60th birthday. Special volume of the E-newsletter of the International Society for Scientometrics and Informetrics, 5, 71-83. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.168.2055&rep=rep1&type=pdf#page=73
  • White, H. D. (2010). Some new tests of relevance theory in information science. Scientometrics, 83, 653-667. https://doi.org/10.1007/s11192-009-0138-3
  • White, H. D. (2015). Co-cited author retrieval and relevance theory: examples from the humanities. Scientometrics, 102(3), 2275-2299. https://doi.org/10.1007/s11192-014-1483-4
  • White, H. D. (2016). Bag of works retrieval: TF*IDF weighting of co-cited works. Proceedings of the 3rd Workshop on Bibliometric-Enhanced Information Retrieval (BIR2016) içinde (s. 63-72). https://ceur-ws.org/Vol-1567/paper7.pdf
  • White, H. D. (2018). Bag of works retrieval: TF*IDF weighting of co-cited works with a seed. International Journal of Digital Libraries, 19, 139-149. https://doi.org/10.1007/s00799-017-0217-7
  • White, H. D. ve McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American Society for Information Science, 49(4): 327-355. https://doi.org/10.1002/(SICI)1097-4571(19980401)49:4%3C327::AID-ASI4%3E3.0.CO;2-4
  • Wilson, P. (1978). Some fundamental concepts of information retrieval. Drexel Library Quarterly, 14(2), 10-24.
  • Wilson, D. ve Sperber, D. (2002). Relevance theory. G. Ward ve L. Horn (Yay. haz.) Handbook of pragmatics içinde (s. 1-55). Blackwell. https://jeannicod.ccsd.cnrs.fr/ijn_00000101/document
  • Wu, H. C., Luk, R. W., Wong, K. F. ve Kwok, K. L. (2007). A retrospective study of a hybrid document-context based retrieval model. Information Processing & Management, 43(5), 1308-1331. https://doi.org/10.1016/j.ipm.2006.10.009
  • Wu, J., Son, G. ve Wang, S. (2020). A competency mining method based on Latent Dirichlet Allocation (LDA) model. Journal of Physics: Conference Series (Vol. 1682, No. 1, p. 012059) içinde. IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1682/1/012059/meta
  • Xia, H., Li, J., Tang, J. ve Moens MF. (2012). Plink-LDA: Using link as prior information in topic modeling. S. Lee, Z. Peng, X. Zhou, Y. S. Moon, R. Unland ve J. Yoo (Yay. haz.) Database Systems for Advanced Applications içinde (s. 213-227). Springer. https://doi.org/10.1007/978-3-642-29038-1_17
  • Xie, X., Liang, Y., Li, X. ve Tan, W. (2019). CuLDA_CGS: Solving large-scale LDA problems on GPUs. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming içinde (s. 435-436). ACM. https://doi.org/10.1145/3293883.3301496
  • Yang, H. T., Ju, J. H., Wong, Y. T., Shmulevich, I. ve Chiang, J. H. (2017). Literature-based discovery of new candidates for drug repurposing. Briefings in Bioinformatics, 18(3), 488-497. https://doi.org/10.1093/bib/bbw030
  • Yang, L., Ji, D. ve Leong, M. (2007). Document reranking by term distribution and maximal marginal relevance for Chinese information retrieval. Information Processing & Management, 43(2), 315-326. https://doi.org/10.1016/j.ipm.2006.07.011
  • Yılmaz, E., Verma, M., Craswell, N., Radlinski, F. ve Bailey, P. (2014). Relevance and effort: An analysis of document utility. Proceedings of the 23rd ACM International Conference on Information and Knowledge Management içinde (s. 91-100). ACM. https://doi.org/10.1145/2661829.2661953
  • Zarrinkalam, F. ve Kahani, M. (2012). A new metric for measuring relatedness of scientific papers based on non-textual features. Intelligent Information Management, 4(4), 99-107. https://www.scirp.org/pdf/IIM20120400001_98298896.pdf
  • Zhou, H. K., Yu, H. M. ve Hu, R. (2017). Topic discovery and evolution in scientific literature based on content and citations. Frontiers of Information Technology & Electronic Engineering, 18(10), 1511-1524. https://doi.org/10.1631/FITEE.1601125
  • Zou, L., Liu, X., Buntine, W. ve Liu, Y. (2021). Citation context-based topic models: discovering cited and citing topics from full text. Library Hi Tech, 39(4), 1063-1083. https://doi.org/10.1108/LHT-01-2021-0041
Toplam 110 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Kütüphane ve Bilgi Çalışmaları
Bölüm Araştırma Makaleleri
Yazarlar

Müge Akbulut 0000-0003-0026-6485

Yaşar Tonta 0000-0003-0285-1338

Yayımlanma Tarihi 30 Haziran 2022
Gönderilme Tarihi 25 Ocak 2022
Kabul Tarihi 10 Nisan 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Akbulut, M., & Tonta, Y. (2022). İlgi Sıralamalarının Artırımlı Olarak Geliştirilmesi: Pennant Erişimle Desteklenen Yeni Bir Yöntem Önerisi. Türk Kütüphaneciliği, 36(2), 169-203. https://doi.org/10.24146/tk.1062751

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