Permodelan pada Information Retrieval: Literature Review
DOI:
https://doi.org/10.17977/um068v2i112022p527-535Keywords:
Information Retrieval, Model Boolean, Model Region, Pendekatan Ruang VektorAbstract
Information Retrieval (IR) is a technique for finding information stored in relevant sources according to user needs. There are various ways to use IR, but this paper focuses on modeling which is used as a framework for information retrieval. There are three types of IR models developed in this paper. IR modeling techniques will be explained as proposed in the literature with a detailed description of the modes, such as Boolean and Region models. Also included are the advantages and disadvantages of each model.
Information Retrieval (IR) adalah teknik untuk menemukan sebuah informasi yang tersimpan pada sumber yang relevan sesuai dengan kebutuhan pengguna. Ada berbagai cara memanfaatkan IR, namun pada paper ini difokuskan pada permodelan yang dipakai sebagai kerangka dalam pengambilan informasi. Ada tiga jenis model IR yang dikembangkan pada paper ini. Teknik permodelan IR akan dijelaskan sesuai yang diusulkan di literatur dengan penjabaran terperinci tentang mode-model tersebut, seperti model Boolean dan Region. Disertakan juga keunggulan dan kelemahan masing-masing model.
References
N. J. Belkin, “Interaction with texts: Information retrieval as information-seeking behavior,” Inf. Retr. ’93 von der Model. zur Anwendung. Proc. First Conf. Gesselschaft für Inform. Fachgr. Inf. Retr., pp. 55–66, 1993.
Z. Zhang and J. Tang, “Information Retrieval in Web2.0,” Springer US, Boston, MA, 2007.
M. Kobayashi and K. Takeda, “Information retrieval on the web,” ACM Comput. Surv., vol. 32, no. 2, pp. 144–173, 2000.
M. Kobayashi and K. Takeda, “Information retrieval on the web,” ACM Comput. Surv., vol. 32, no. 2, pp. 144–173, 2000.
Z. Mazur and K. Wiklak, “Music Information Retrieval on the Internet,” pp. 229–243, 2013.
D. Giustini and M. N. Boulos, “Google Scholar is not enough to be used alone for systematic reviews,” Online J Public Heal. Inf., vol. 5, no. 2, p. 214, 2013.
Y. Duan, P. Burrell, R. Mullins, and H. Jin, “A Case-Based Reasoning Approach to Enhance Web-Based Training on Internet Marketing,” Artif. Intell. Appl. …, 2005.
V. Ceausu and S. Desprès, “A semantic case-based reasoning framework for text categorization,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4825 LNCS, pp. 736–749, 2007.
M. Mao, “Ontology mapping: An information retrieval and interactive activation network based approach,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4825 LNCS, pp. 931–935, 2007.
D. Hiemstra, “Information Retrieval Models 1.1,” no. November, pp. 1–20, 2009.
R. Aknouche, O. Asfari, F. Bentayeb, and O. Boussaid, “Integrating query context and user context in an information retrieval model based on expanded language modeling,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7465 LNCS, pp. 244–258, 2012.
Joon Ho Lee, Myoung Ho Kim, Yoon Joon Lee, (1993) "Information Retrieval Based On Conceptual Distance In Is‐A Hierarchies", Journal Of Documentation, Vol. 49 Iss: 2, Pp.188 - 207
M. van Opijnen and C. Santos, “On the concept of relevance in legal information retrieval,” Artif. Intell. Law, 2017.
CADOLI, Marco; GIOVANARDI, Andrea; SCHAERF, Marco. An algorithm to evaluate quantified Boolean formulae. AAAI/IAAI, 1998, 98: 262-267.
Michalowski, M., Ambite, J.L., Thakkar, S., Tuchinda, R., Knoblock, C.A., Minton, S.: Retrieving and semantically integrating heterogeneous data from the web. Intelligent Systems, IEEE 19(3), 72–79 2004.
J. Gronski, “Semantic Web for Search,” Semant. Web - ISWC 2009, vol. 5823, pp. 957–964, 2009.
A. Bagga and B. Baldwin, “Entity-Based Cross-Document Coreferencing Using the Vector Space Model,” Proc. 36th Annu. Meet. Assoc. Comput. Linguist. 17th Int. Conf. Comput. Linguist., pp. 79–85, 1998.
A. A. Abdillah and I. B. Muktyas, “Implementasi Vector Space Model Untuk Pencarian Dokumen,” no. August, pp. 0–7, 2015.
K. Erk and S. Padó, “A structured vector space model for word meaning in context,” Proc. Conf. Empir. Methods Nat. Lang. Process., no. October, pp. 897–906, 2008.
Mole, “Vector Space Model,” 1999.
CROFT, W. Bruce; HARPER, David J. Using probabilistic models of document retrieval without relevance information. Journal of documentation, 1979, 35.4: 285-295.
J. Lafferty and C. Zhai, “Probabilistic Relevance Models Based on Document and Query Generation,” Lang. Model. Inf. Retr., vol. 1, pp. 1–10, 2003.
V. Lavrenko, “Introduction to Probabilistic Models for Information Retrieval,” Proc. 33rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. Ed. by Hsin-His Chen, Efthismis N. Efthimiadis, Jacques Savoy, Fabio Crestani Lugano Stephane Marehand-Maillet, p. p. 905, 2010.
P. V Gehler et al., “The Rate Adapting Poisson Model for Information Retrieval and Object Recognition,” 2006.
N. Friedman, D. Geiger, and M. Goldszmit, “Bayesian Network Classifiers,” Mach. Learn., vol. 29, pp. 131–163, 1997.
N. Friedman, D. Geiger, and M. Goldszmit, “Bayesian Network Classifiers,” Mach. Learn., vol. 29, pp. 131–163, 1997.
L. Uusitalo, “Advantages and challenges of Bayesian networks in environmental modelling,” Ecol. Modell., vol. 203, no. 3–4, pp. 312–318, 2007.
F. Song and W. B. Croft, “A general language model for information retrieval,” Inf. Retr. Boston., pp. 316–321, 1999.
J. M. Ponte and W. B. Croft, “A language modeling approach to information retrieval,” Proc. 21st Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 275–281, 1998.
C. Zhai, “Statistical Language Models for Information Retrieval A Critical Review,” Found. Trends® Inf. Retr., vol. 2, no. 3, pp. 137–213, 2007.
3.png)
1.png)
1.png)
