ORIGINAL_ARTICLE
Editorial Note: From Ghostwriting to Deepfake (1)
The note addresses infamous ghostwriting business in Iran as an academic misconduct. The editor then gives a definition of plagiarism and reports his experience as a victim of plagiarism. The note continues by mentioning historical background and famous contemporary cases of plagiarism. The note concludes with the expression of regret about the failure of thesis supervisors, co-supervisors to prevent, and examiners to expose such academic misconducts. The failure, he believes, is caused by carelessness in accepting supervisory and examination responsibility for thesis which fall out of their domain of expertise simply for absorbing certain benefits.
https://nastinfo.nlai.ir/article_2431_c4af70b65f74f318a622b117bc709266.pdf
2020-10-22
7
11
10.30484/nastinfo.2020.2431
Ghostwriting
Plagiarism
F.
Khosravi
fa.khosravi@gmail.com
1
Associate Professor, National Library and Archives of Iran
AUTHOR
ORIGINAL_ARTICLE
Future of Textual Information Retrieval Systems
Purpose: To identify factors influencing the future of text information retrieval systems with a forward-looking approach.
Methodology: Document analysis and survey are used to identify factors. The research population in the document analysis section consists of literature related to the textual information retrieval field and in the survey section consists of the specialists in information retrieval. Purposive sampling is applied in both sections.
Findings: The results reveal that among the examined indicators, technology index is the most important index in the future of information retrieval systems. in technology index, artificial intelligence with an importance factor of 93 in the political index, copyright with 86 importance factor; in the socio-cultural index, business reliance on the information with 87 importance factor; and in the economic index, programs associated with 86 importance factor are among the highest.
Conclusion: Information science professionals should concentrate more on all key identified factors if they want to have a more effective contributive role in the future of textual information retrieval systems, because knowing the past, understanding the present, and focusing on these existing factors can make the future more effectively
https://nastinfo.nlai.ir/article_2406_f85ac286735aef25ecd69df4e4bb6fae.pdf
2020-10-22
12
26
10.30484/nastinfo.2020.2365.1908
Textual information retrieval systems,
Information retrieval systems,
Information retrieval
future studies
A.
Asadnia
abolfazlasadnia@gmail.com
1
PhD Candidate, Knowledge and Information Science, University of Isfahan, Isfahan, Iran
AUTHOR
M,
CheshmehSohrabi
mo.sohrabi@edu.ui.ac.ir
2
Associate Professor, University of Isfahan, Faculty of Education and Psychology, Department of Knowledge and Information Science, Isfahan, Iran
LEAD_AUTHOR
A.
shabani
shabanii@edu.ui.ac.ir
3
Professor, Knowledge and Information Science, University of Isfahan, Isfahan, Iran,
AUTHOR
A.
Asemi
af_aseme@yahoo.com
4
PhD in Knowledge and Information Science, Doctoral School of Business Informatics, Corvinus University of Budapest, Hungary, Associate Professor, University of Isfahan, Isfahan
AUTHOR
M.
Taheri Demneh
m.taheri@ast.ui.ac.ir
5
Assistant Professor, Department of Futures Studies, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran
AUTHOR
آزاد ارمکی، تقی؛ مبارکی، مهدی؛ شهبازی، زهره (1391). بررسی و شناسایی شاخصهای کاربردی توسعه اجتماعی (با استفاده از تکنیک دلفی). مطالعات توسعه اجتماعی-فرهنگی، 1 (1): 7-30.
1
Elsevier (2019). How-scopus-works. https://www.elsevier.com/solutions/scopus/how-scopus-works (accessed 15 Oct. 2019).
2
Guo, J., Fan, Y., Pang, L., Yang, L., Ai, Q., Zamani, H., ... and Cheng, X. (2019). A deep look into neural ranking models for information retrieval. arXiv preprint.1903.06902.
3
Holl, K., & Elberzhager, F. (2016). Mobile Application Quality Assurance: Reading Scenarios as Inspection and Testing Support. 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). doi:10.1109/seaa.2016.11
4
Jameson, A. )1987(. Downloading and Upload in Online Information Retrieval. Library Management, 8 (1), 2-64. https://doi.org/10.1108/eb054895
5
Jones, S.K. (1999). Information retrieval and artificial intelligence. Artificial Intelligence, 114, 257–281.
6
Kolomiyets, O., & Moens, M.F. (2011). A survey on question answering technology from an information retrieval perspective. Information Sciences, 181 (24), 5412-5434. doi: 10.1016/j.ins.2011.07.047
7
Kraaij, W., Pohlmann, R., and Hiemstra, D. (2000). Twenty-one at TREC-8: using language technology for information retrieval. NIST SPECIAL PUBLICATION SP, 246: 285-300.
8
Lawlor, R.C. (1967). Information retrieval and copyright law revision. Information (International Social Science Council), 6(1), 75-85. https://doi.org/10.1177/053901846700600103
9
Lewandowski, D. (2005). Web searching, search engines and Information Retrieval. Information Services & Use, 25 (3-4), 137-147.DOI 10.3233/ISU-2005-253-402
10
Lingpeng, Y., Donghong, J., & Li, T. (2004). Chinese information retrieval based on terms and ontology. The Fourth NTCIR Workshop, 412-414.
11
Liu, J., Kong, X., Zhou, X., Wang, L., Zhang, D., Lee, I., & Xia, F. (2019). Data Mining and Information Retrieval in the 21st century: A bibliographic review. Computer Science Review, 34. 100193. DOI: 10.1016/j.cosrev.2019.100193
12
Mandle, T. (2008). Artificial Intelligence for Information Retrieval. In Encyclopedia of artificial intelligence. London: Information Science Reference. doi: 10.4018/9781599048499.ch023
13
Prakash, S., Shashidhara, H.R., & Raju, G.T. (2013). The Role of an Information Retrieval in the Current Era of Vast Computer Science Stream. International Journal of Soft Computing and Engineering, 3 (3), 139-143.
14
Rialland, A., & Wold, K.E. (2009). Future Studies, Foresight and Scenarios as basis for better strategic decisions. Trondheim, December.
15
Singhal, A. (2008). Web Search: Challenges and Directions. In European Conference on Information Retrieval (pp. 2-2). Springer, Berlin, Heidelberg.
16
Smith, L.C. (1976). Artificial intelligence in information retrieval systems. Information Processing & Management, 12 (3), 189-222. https://doi.org/10.1016/0306-4573(76)90005-4
17
Sriram, G.G., Pravallika, T., Neelima, K., and Rahmathulla, Sh. (2014). The Information Retrieval, a Future Barrier. International Journal of Innovative Research in Computer and Communication Engineering, 2 (2), 2943-2947.
18
Wan, G.G., and Liu, Z. (2008). Content-based information retrieval and digital libraries. Information technology and libraries, 27 (1), 41-47. https://doi.org/10.6017/ital.v27i1.3262
19
ORIGINAL_ARTICLE
Proposing an Alternative Framework for a Library Classification Scheme based on Moslem Scholars Contributions
Purpose: To propose a basis for a library classification scheme based on cultural and doctrinal values of the Islamic world. Methodology: Views of Muslim scholars on classification of sciences are reviewed and compared with existing library classification schemes. Finding &Results: Since early 8th century Muslim scholars have proposed classifications for sciences in accordance to their jurisprudential-moral, ethical-educational, and moral-educational approaches, some having novel features and merits which could be used in developing alternative library classification schemes.
https://nastinfo.nlai.ir/article_2403_c443f0344fb973ad55267d8f2ee91e71.pdf
2020-10-22
28
47
10.30484/nastinfo.2020.2193.1843
Science classification
classification methods
Muslim scholars
H.
Azimi
habibazimi@yahoo.com
1
Assistant Professor &Scientific advisor to the head of the National Library and archives of I.R. Iran
LEAD_AUTHOR
آملی، شمسالدین محمد (1377). نفائسالفنون فی عرائسالعیون. انتشارات کتابفروشی اسلامیه (دوره3 جلدی).
1
ابرامی، هوشنگ (1379). شناختی از دانششناسی.تهران: نشر کتابدار.
2
ابن اکفانی، محمد (1990م). ارشادالقاصد الی اسنی المقاصد فی انواع العلوم. قاهره: دارالفکر العربی.
3
ابن خلدون، عبدالرحمان (1999م). مقدمه الکتاب العبرودیوان المبتدی والخبر. قاهره: دارالکتب المصری.
4
ابن سینا، حسین (1298ق.) رساله فی اقسام العلوم العقلیه، من کتابه تسع رسائل فی الحکمه و الطبیعیات. قسطنطنیه: مطبعه الجوائب.
5
تامسون، جیمز (1366). تاریخ اصول کتابداری، ترجمه محمود حقیقی. تهران: مرکز نشر دانشگاهی.
6
تهانوی، محمداعلی (1963م). کشاف اصطلاحات الفنون. تحقیق لطفی عبدالبدیع. قاهره: المؤسسه المصریه العامه للتألیف و النشر.
7
جابر بن حیان الف (1354ق.). کتاب اخراج مافی القوه الی الفعل، من مختار رسائل جابر بن حیان. با تصحیح پول کراوس. قاهره: مکتبه الخانجی.
8
جابر بن حیان ب (1354ق.). کتاب الحدود، فی کتاب مختار رسائل جابر بن حیان. با تصحیح پول کراوس. قاهره: مکتبه الخانجی.
9
حماده، محمدماهر (1403ق.).علم المکتبات و المعلومات. بیروت.
10
حماده، محمد ماهر (1405ق.).تنظیم المکتبه المدرسیه، با همکاری علی القاحی.چاپ سوم..
11
خمینی، روح الله (1386). شرح چهل حدیث. تهران: مؤسسه تنظیم و نشر آثار امام خمینی.
12
خوارزمی، محمد (بی تا). مفاتیحالعلوم. قاهره: مطبعه الشرق.
13
داودی، مهدی (1371). « تأثیر آراء فرانسیس بیکن و اگوست کنت بر ردهبندی دهدهی دیویی». تحقیقات اطلاع رسانی و کتابخانههای عمومی(پیام کتابخانه سابق)، شماره 5 و 6، ص96-101.
14
سیوطی، عبدالرحمن (1309ق.). اتمام الدرایه لقراء النقایه. د. م . میرزا شیرازی.
15
شعبان عبدالعزیز خلیفه (بی تا). الفهرست لابن ندیم. با همکاری ولید محمد عوزه. قاهره: مکتبه الثقافیه.
16
شعبان عبدالعزیز خلیفه (1993م). مفتاح السعاده و مصباح السیاده فی موضوعات العلوم لطاشکبری زاده. مجلد اول. القاهره: دارالعربی.
17
شیروانی، محمد امین (نسخه خطی). الفوائد الخاقانیه. نسخه خطی شمارة 5718، دارالکتب الظاهریه بدمشق.
18
صدرالدین شیرازی، محمد (1378). المظاهر الالهیه فی أسرار العلوم الکمالیه. تصحیح و تحقیق و مقدمه سیدمحمد خامنه ای. تهران: بنیاد حکمت صدرا.
19
طاهری عراقی، احمد (1372). ردهبندی دهدهی دیویی اسلام.تهران: کتابخانه ملی، ویرایش سوم.
20
طاهری عراقی، احمد (1376). ردهBP : اسلام. تهران: کتابخانه ملی جمهوری اسلامی ایران.
21
طاش کبریزاده، احمد (1993م). مفتاح السعاده و مصباح السیاده، به کوشش و تصحیح شعبان عبدالعزیز خلیفه. قاهره: دارالعربی.
22
غزالی، محمد (1328ق.). احیاء العلوم المدینه. قاهره: مطبعه کردستان العلمیه.
23
غزالی، محمد (1328ق.). الرساله اللدنیه. قاهره: مطبه کردستان العلمیه.
24
فارابی، محمد (1948م). احصاءالعلوم.تحقیق و تقدیم و تعلیق عثمان امین. قاهره: دارالفکر العربی.
25
فدایی، غلامرضا (1389). طرحی نو در طبقهبندی علوم. تهران: سازمان اسناد و کتابخانه ملی.
26
قطبالدین شیرازی، محمود (1903م). دره التاج لغره الدیباج فی الحکمه. تصحیح محمد مشکوه. تهران: مجلس.
27
کندی، یعقوب (1950). کمیه کتب ارسطو طالیس و مایحتاج الیه فی تحصیل الفلسفه. تحقیق و اخراج محمد عبدالهادی ابوریده. قاهره: دارالفکرالعربی.
28
مصاحبی نائینی، محمد علی (1377). نامه فرهنگیان. چاپ عکسی. تهران: انتشارات کتابخانه مجلس شورای اسلامی.
29
ملک، فضل الرحمن (1358). «اسلام و مدرنیته ».خلاصه کتاب، ترجمه ابوالفضل والازاده.مجله مدرسه، شماره 4، مهر1358
30
ناهد، محمدسالم (1311ق.). نظم تصنیف المعرفه عندالمسلمین. با مقدمه شعبان عبدالعزیز خلیفه. اسکندریه: دارالثقافه العلمیه.
31
نصیرالدین طوسی، محمد (1994م). رساله فصل بی بیان اقسام الحکمه علی سبیل الایجاز، من کتاب عباس سلیمان. تصنیف العلوم بین الطوسی و بیضاوی. اسکندریه: دارالمعرفه الجامعیه.
32
ولیش، هانس. اچ؛ اسمیراگلیا، ریچارد. م (1381). «ردهبندی کتابخانهای»؛ ترجمة فیروزه برومند. در: دایرهالمعارف کتابداری و اطلاعرسانی (جلد اول). تهران: کتابخانه ملی جمهوری اسلامی ایران، ص 887- 892.
33
یعقوبنژاد، محمدهادی (1390). ردهبندی علوم و چالشهای فرارو. مجله نقد و نظر، سال شانزدهم، زمستان 1390، شماره 4، صص 135-154.
34
Vickery.B.C.Classification and indexing hn science.Butterworths,1975.
35
ORIGINAL_ARTICLE
Relevance in LinkedIn from the views of Medical Librarians
Background and Purpose: Considering the important role of social networks, this study aims to identify factors affecting relevance in LinkedIn .
Method: 17 information specialists participated in the study. Data was collected using semi-structured interviews, then coded and analyzed using Dickelman method.
Results: 441 primary codes and seven categories including information system, retrieval system, document attributes, database attributes, user attributes, requests and queries, and feedback.
Conclusion: LinkedIn plays an important role in meeting the information needs of the participants
https://nastinfo.nlai.ir/article_2408_80e5f85d5fa8f91b9d1e7da2e873a157.pdf
2020-10-22
48
57
10.30484/nastinfo.2020.2470.1933
Information Recovery
relevance
social networking
LinkedIn
M.
Shirzad
mshm1362@yahoo.com
1
PhD Candidate in knowledge & Information Science, Payame Noor University, Tehran. Iran,
AUTHOR
A.
Mousavi
mousaviaf@gmail.com
2
Associate professor, Department of knowledge & Information Science, Payame Noor University, Tehran. Iran
LEAD_AUTHOR
S.
Ziaei
soraya.ziaei@gmail.com
3
Associate professor, Department of knowledge & Information Science, Payame Noor University, Tehran. Iran,
AUTHOR
F.
Soheili
fsohieli@gmail.com
4
Associate professor, Department of knowledge & Information Science, Payame Noor University
AUTHOR
M.
S,alami
salamilib@yahoo.com
5
Assistant Professor, Department of knowledge & Information Science, Payame Noor University, Tehran. Iran
AUTHOR
امیری، مقصود؛ انتظاری، علی؛ مرتجی، نجمه السادات (1395). الگوی رفتار اشتراک دانش متخصصین ایرانی در شبکههای اجتماعی تخصصی: شناسایی شاخصها. تعامل انسان و اطلاعات، 3(3): 66-81.
1
بیابانی، مریم (1398). استفاده از روشهای مبتنی بر جاسازی بردار کوئری در بازیابی اطلاعات، پایان نامه کارشناسی ارشد، دانشگاه شهید بهشتی، دانشکده مهندسی و علوم کامپیوتر.
2
بیگم مرتضوی، لیلا (1394). یک روش نوین بازیابی اطلاعات با تلفیق مدلهای فازی و فضای برداری، پایان نامه کارشناسی ارشد، دانشگاه شیراز، دانشکده مهندسی برق و کامپیوتر.
3
جوادی مقدم، سید محمد؛ عبدالرزاق نژاد، مجید؛ قادری فریز، مهناز (1396). بهبود بازیابی اطلاعات بر اساس تشابه معنایی کلمات کلیدی با استفاده از رتبهدهی مبتنی بر گراف، چهارمین کنفرانس ملی فناوری اطلاعات، کامپیوتر و مخابرات، مشهد، دانشگاه تربت حیدریه.
4
حسنزاده، محمد؛ غفاری، سعید؛ زارعی، عاطفه، کمندی، حسین (1393). کارکرد عنوان و نشانی اینترنتی در بهبود ربط نتایج بازیابی اطلاعات، پژوهشهای نظری و کاربردی در علم اطلاعات و دانششناسی، پژوهشنامه کتابداری و اطلاع رسانی 4 (1). https://doi.org/10.22067/riis.v4i1.19408
5
Berger, A., & Lafferty, J. (2017, August). Information retrieval as statistical translation. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 219-226). New York, NY, USA: ACM. https://doi.org/10.1145/3130348.3130371
6
Farhi, S. H., & Boughaci, D. (2018). Graph based model for information retrieval using a stochastic local search. Pattern Recognition Letters, 105, 234-239. https://doi.org/10.1016/j.patrec.2017.09.019
7
Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016. Pew Research Center, 11(2).
8
Losada, D. E., Parapar, J., & Barreiro, A. (2018). A rank fusion approach based on score distributions for prioritizing relevance assessments in information retrieval evaluation. Information Fusion, 39, 56-71. DOI:
9
10.1016/j.inffus.2017.04.001
10
Thangaraj, M., & Sujatha, G. (2014). An architectural design for effective information retrieval in semantic web. Expert Systems with Applications, 41(18), 8225-8233. https://doi.org/10.1016/j.eswa.2014.07.017
11
ORIGINAL_ARTICLE
Otherness in Library Organization Systems vs. Social Tagging
Purpose: To uncover the phenomenon of otherness in assigning subjects to library materials by the Library of Congress. Methodology: 384 titles on the subjects of Islam catalogued between 2016 to 2019 were retrieved from LC’s OPAC and compared with the tags assigned to them by users in the website of LibraryThing. Findings: Average number of subjects assigned by social tagging to each title was around 15, much higher than the average 3.5 by LCHS. In addition, 83% of the tags did not match either conceptual or linguistic with the subject headings assigned. Around 68% of the tags which did not match the subject heading were taken from the content of titles. Conclusion: The structure of subject headings has resulted in marginalizing some subjects, whereas tags provide an opportunity to representation others views.
https://nastinfo.nlai.ir/article_2419_c0998356dd9fafae1b7a84f83a02f9ba.pdf
2020-10-22
58
71
10.30484/nastinfo.2020.2557.1965
Tagging
Subject headings
Otherness
Marginalization
Representation
G.
Movahedian
gh.movahedian@gmail.com
1
PhD Candidate in knowledge and Information Science, University of Isfahan, Isfahan
AUTHOR
A.
Shabani
shabania@edu.ui.ac.ir
2
PhD in Knowledge and Information Science, Professor, University of Isfahan, ,
LEAD_AUTHOR
M.
Cheshmesohrabi
mo.sohrabi@edu.ui.ac.ir
3
PhD in Communication and Information Science, Associate Professor, University of Isfahan, Isfahan
AUTHOR
A.
Asemi
asemi.asefeh@uni-corvinus.hu
4
PhD in Knowledge and Information Science, Doctoral School of Business Informatics, Corvinus University of Budapest, Hungary, Associate Professor, University of Isfahan, Isfahan,
AUTHOR
دریدا، ژاک (1384). جهان وطنی و بخشایش، ترجمة امیرهوشنگ افتخاری راد. تهران: نشر گام نو.
1
خادمیان، مهدی (1395). امکان جایگزینی یا تکمیل سرعنوانهای موضوعی کتابخانه کنگره با برچسبهای اجتماعی لایبرریتینگ در حوزههای علوم انسانی، علوم اجتماعی و علوم طبیعی. پایاننامه دکتری، دانشگاه شهید چمران اهواز.
2
Bates, J. & Rowley, J. (2011). Social reproduction and exclusion in subject indexing: a comparison of public library OPACs and librarything folksonomy. Journal of Documentation, 62)2(, 431-448. https://doi.org/10.1108/00220411111124532
3
Deodato, J. (2010). Deconstructing the Library with Jacques Derrida: Creating Space for the “Other” in Bibliographic Description and Classification. In Critical Theory for Library and Information Science: Exploring the Social from Across the Disciplines. 2nd ed. Edited
4
Gloria J. Leckie, Lisa M. Given, and John E. Buschman. (pp. 75-88). Santa Barbara, CA: Libraries Unlimited.
5
Fox, M.J. (2012). Communities of practice, gender and social tagging. Tenth International ISKO Conference, 4-7 August 2012, Mysore, India.
6
Fox, M. J., & Reece, A. (2014). The impossible decision: social tagging and Derrida’s deconstructed hospitality. knowledge organization, 40(4), 260-265. https://doi.org/10.5771/0943-7444-2013-4-260
7
Green, R. (2015). Indigenous Peoples in the US, Sovereign Nations, and the DDC. Knowledge organization, 42(4), 211-221. 10.5771/0943-7444-2015-4-211
8
Hajibayova, L., & Buente, W. (2017). Representation of Indigenous cultures: Considering the Hawaiian hula. Journal of Documentation, 73(6), 1137-1148.
9
https://doi.org/10.1108/JD-01-2017-0010
10
Hajibayova, L., Buente, W., Quiroga, L., & Valeho‐Novikoff, S. (2016). Representation of Kanaka Maoli (Hawaiian) culture: A case of hula dance. In Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology, ASIST ’16. Silver Springs, MD, USA: American Society for Information Science. https://doi.org/10.1002/pra2.2016.14505301128
11
Johansson, S., & Golub, K. (2019). LibraryThing for Libraries: How Tag Moderation and Size Limitations Affect Tag Clouds. Knowledge organization, 46(4), 245-259.
12
10.5771/0943-7444-2019-4-245
13
Kemp, R. B. (2007). Classifying marginalized people, focusing on natural disaster survivors. Knowledge organization, 1(1), 44-54.
14
LibraryThing (2020). LibraryThing Concepts. Retrieved May, 12 2020 from: https://www.librarything.com/concepts.
15
Lu, C., Park, J., & Hu, X. (2010). User tags versus expert-assigned subject terms: a comparison of librarything tags and Library of Congress Subject Headings. Journal of Information Science, 36 (6), 763-779. 10.1177/0165551510386173
16
Murphy, Pauline Rafferty, (2015). Is there nothing outside the tags?: Towards a poststructuralist analysis of social tagging, Journal of Documentation, 71(3), 477-502. https://doi.org/10.1108/JD-02-2013-0026
17
Olson, H. A. (1998). Mapping beyond Dewey's boundaries: Constructing classificatory space for marginalized knowledge domains. Library Trends, 47 (2), 233–54.
18
Olson, H. A., & Schlegl, R. (2001). Standardization, objectivity, and user focus: A meta-analysis of subject access critiques. Cataloging & classification quarterly, 32(2), 61-80. https://doi.org/10.1300/J104v32n02_06
19
Olson. H. A., (2001). Patriarchal Structures of subject access and subversive techniques for change. Canadian journal of information and library science, 26(2), 1-29.
20
Rolla, P. J. (2009). User tags versus subject headings: can user-supplied data improve subject access to library collections?. Library Resources and Technical Services, 53 (3),174-184. DOI: https://doi.org/10.5860/lrts.53n3.174
21
Smith, T. (2007). Cataloging and you: Measuring the efficacy of a folksonomy for subject analysis. In J., Lussky (Ed.) Proceedings of the 18thWorkshop of the American Society for Information Science and Technology, Special Interest Group in Classification Research, Milwaukee, Wisconsin.
22
ORIGINAL_ARTICLE
Ranking and Relevance in Noormags and RICEST Databases
Purpose: The main purpose of information retrieval systems is to retrieve relevant information for users. This means that the results of the search must answer the questions provided to the system. Therefore, the evaluation of relevance is very important in such systems. In addition to relevance, the order and placement of articles are also important to the user. The retrieval systems should put more relevant articles at the top of the retrieval list. Evaluating the quality of ranking performance is a key activity in the field of information retrieval. This article assesses relevance and ranking of two databases. Methodology: The sample includes 390 Persian articles retrieved in each of the Noormags and RICeST databases. For each topic inquired were carried out in both databases in two phases within the span of one month. The first 10 articles retrieved from each database were recorded based on the system ranking. Relevance score was given by 3 subject specialists within the range of zero to ten. Spearman correlation test was used to compare the ranking of the system with the ranking of the user. Data analysis was performed using descriptive and inferential statistics using SPSS software. The distance precision formula carried out to check the accuracy of the retrieval precision of related documents in the two databases, and the expected Reciprocal Rank was used to evaluate the quality of the ranking of articles. Results: Users were far less familiar with RICeST database. Significant, consistent, and moderate correlation was found between system rankings and user rankings at the Noormags database in the first phase, i. e., ranking by users increases or decreases as the system rank increases or decreases. We found significant, consistent, and strong correlation between system’s ranking and user ranking in Noormags in the second phase. However, there was no correlation between system ranking and user ranking in RICeST database in both the first and second phases. Therefore, Noormags database ranking was found closer to the users’ ranking. Ranking quality by Noormags was relatively better than that of RICeST. Also, accuracy of the relevance precision of Noormags articles was higher than RICeST. From the users' point of view, Noormags database retrieved more relevant documents. Conclusion: Noormags' new algorithms and capabilities have increased the relevance and ranking of its output. The findings could help database administrators to upgrade their databases by taking advantage of technologies to make semantic retrieval possible.
https://nastinfo.nlai.ir/article_2409_63b2fd1429b63f461a25bec512bb60ac.pdf
2020-10-22
72
92
10.30484/nastinfo.2020.2472.1934
relevance
System Ranking
User Ranking
database
Noormags
RICEST
A.
Hajian
ahajian91@gmail.com
1
university of Isfahan
AUTHOR
M.
CheshmehSohrabi
mo.sohrabi@edu.ui.ac.ir
2
Associate professor, Knowledge and Information Science, University of Isfahan, Isfahan, Iran
LEAD_AUTHOR
اخوتی، مریم (1383). مفهوم ربط در نظامهای بازیابی اطلاعات، مروری بر نظریهها و ادبیات موجود. اطلاعشناسی، 2 (1) : 23-45.
1
امینی مقدم، مهدی (1392). قابلیتهای جدید پایگاه مجلات تخصصی نور در یک نگاه. فصلنامه رهآورد نور،44: 57-58.
2
بیزاییتس، ریکاردو؛ ریبرونتو، برتیه (1385). قلمروهای نو در بازیابی اطلاعات (ج1). ترجمه سیروس آزادی، علی جوامع و علی حسین قاسمی. تهران: چاپار، دبیزش.
3
پائو، میراندا لی (1380). مفاهیم بازیابی اطلاعات. ترجمه اسدالله آزاد و رحمتالله فتاحی. مشهد: دانشگاه فردوسی.
4
حری، عباس (1383). زنجیره داوری ربط در فرایند انتقال اطلاعات. اطلاعشناسی،2 (1) : 177-193.
5
داورپناه، محمدرضا؛ رمضانی، عباسعلی (1385). بررسی معیارهای قضاوت ربط در فضای الکترونیکی. مطالعات تربیتی و روانشناسی، 25: 5-30. https://doi.org/10.22067/fe.v7i1.1829
6
ریاحینیا، نصرت؛ رحیمی، فروغ؛ لطیفی، معصومه، و الله بخشیان، لیلی. (1394). بررسی میزان انطباق ربط سیستمی و ربط کاربرمدارانه در پایگاههای اطلاعاتی Google Scholar- ISC- SID. تعامل انسان و اطلاعات، 1 (4) : 1-11.
7
غلامی، تکتم (1386). سنجش میزان ربط در بازیابی اطلاعات در پایگاههای اطلاعاتی Ebsco، Scopus،Science Direct از دیدگاه دانشجویان کارشناسی ارشد علوم تربیتی و روانشناسی دانشگاه الزهرا (س). پایاننامه کارشناسی ارشد، گروه کتابداری و اطلاعرسانی. دانشکده علوم تربیتی و روانشناسی. دانشگاه الزهرا (س).
8
کیانی، محمدرضا (1391). رویکردهای ارزیابی نظامهای بازیابی اطلاعات: پسزمینه و چشمانداز پیشرو. کتابداری و اطلاعرسانی، 15 (2) : 243-258.
9
لنکستر، ویلفرید (1379). نظامهای بازیابی اطلاعات (ویژگیها، آزمون، و ارزیابی). ترجمه جعفر مهرادلنکستر، . شیراز: نوید شیراز.
10
میدو، چارلز تی؛ بویس، برت آر؛ کرافت، دونالداچ؛ و باری، کارول (1390). نظامهای بازیابی اطلاعات متنی. ترجمه نجلا حریری. تهران: چاپار.
11
نادی راوندی، سمیه؛ حریری، نجلا (1395). نظامهای بازیابی اطلاعات. تهران: کتابدار.
12
Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009). Expected reciprocal rank for graded relevance. Proceeding of the 18th ACM Conference on Information and Knowledge Management, 621-630. https://doi.org/10.1145/1645953.1646033
13
Chu, H., & Rosenthal, M. (1996). Search engines for the World Wide Web: A comparative study and evaluation methodology. The Annual Meeting-American Society for Information Science, 33, 127-135.
14
Clarke, S. J., & Willett, P. (1997). Estimating the recall performance of Web search engines. In Aslib proceedings, 49 (7), 184-189. https://doi.org/10.1108/eb051463
15
Cooper, W. S. (1968). Expected search length: A single measure of retrieval effectiveness based on the weak ordering action of retrieval systems. Journal of American Society of Information Science, 19 (1,, 30-41. https://doi.org/10.1002/asi.5090190108
16
Ding, W, & Marchionini, G. (1996). A comparative study of web search service performance. In: ASIS 1996 Annual Conference Proceedings, Baltimore, MD, Oct 19-24, 136-142. https://www.learntechlib.org/p/83946/
17
Johnson, F. C., Griffiths, J. R., & Hartley, R. J. (2001). DEVISE: a framework for the evaluation of Internet search engines. CERLIM (Centre for Research in Library and Information Management) , Manchester Metropolitan University.
18
Nowak, S., Lukashevich, H., Dunker, P., & Rüger, S. (2010). Performance measures for multilabel evaluation: a case study in the area of image classification. In Proceedings of the international conference on Multimedia information retrieval, Philadelphia, Pennsylvania, USA, 35-44. https://doi.org/10.1145/1743384.1743398
19
Powell, R. R., & Connaway, L. S., (2010). Basic research methods for librarians. London: Libraries Unlimited.
20
Rees, A. M. (1966). The relevance of relevance to the testing and evaluation of document retrieval systems. In Aslib Proceedings, 18 (11): 316-324. https://doi.org/10.1108/eb050068
21
Reitz, M. J. (2006). Dictionary of library and information. London: Libraries unlimited.
22
Saracevic, T. (2007) Relevance: a review of the literature and a framework for thinking on the notion in information Science. Part II. Journal of the American Society for Information Science, 58 (13) 1915-1933.https://doi.org/10.1002/asi.20681
23
Su, L. T., Chen, H. L., & Dong, X. (1998). Evaluation of Web-Based Search Engines from the End-User's Perspective: A Pilot Study. In Proceedings of the ASIS Annual Meeting, 35, 348-61.
24
Sawade, C., Bickel, S., Von Oertzen, T., Scheffer, T., & Landwehr, N. (2013). Active evaluation of ranking functions based on graded relevance. Machine learning, 92 (1), 41-64.
25
DOI 10.1007/s10994-013-5372-5
26
Tang, M. C., & Sun, Y. (2003). Evaluation of web-based search engines using user-effort measures. Library and Information Science Research Electronic Journal, 13 (2).
27
Tomaiuolo, N. G. and Packer, J. G. (1996). An analysis of Internet search engines: assessment of over 200 search queries. Computers in Libraries, 16 (6), 58-62.
28
Urhan, T. K., Rempel, H. G. Meunier‐Goddik, L, &. Penner, M. H. (2019). Information Retrieval in Food Science Research II: Accounting for Relevance When Evaluating Database Performance. Journal of food science, 84 (10), 2729-2735.
29
https://doi.org/10.1111/1750-3841.14769
30
Vaughan, L. (2004). New measurements for search engine evaluation proposed and tested. Information Processing & Management, 40 (4), 677-691.
31
https://doi.org/10.1016/S0306-4573(03)00043-8
32
ORIGINAL_ARTICLE
Factors Affecting Entrepreneurial Business in the LIS Professions: A meta Analysis
Objective: To explore future components of entrepreneurial business research in information science with a meta-analytical approach. Methodology: 1709 LIS journals articles on the topic published between 2000 and 2019 were reviewed. Sixteen were selected for meta-analysis. A total of 31 components were identified. CMA2 software and Cohen's effect size approach were used for data analysis. Finedings: The single and combined effect size of all components were significant. Physical infrastructure showed the highest effect size (0.83). Scientific morale of the educators (0.78) and "Self Confidence" (0.75) were in the next ranks. Conclusion: In order to enhance entrepreneurship business among LIS graduates, the above variables should be considered in desing and implementing programs.
https://nastinfo.nlai.ir/article_2407_ef3e12cd62b7c3f75b5ae10fb898be55.pdf
2020-10-22
94
108
10.30484/nastinfo.2020.2478.1936
Entrepreneurial Business
Information and knowledge retrieval tendency
meta-analysis
M.
Kardan Neshati
kardnneshati@gmail.com
1
PhD Candidate, Department of Information and Knowledge Science, Babol Branch, Islamic Azad University, Babol, Iran
AUTHOR
M.
Ghiasi
mighiasi@gmail.com
2
Assistant Professor, Department of Information and Knowledge Science, Babol Branch, Islamic Azad University, Babol, Iran,
LEAD_AUTHOR
S. A.A.
Razavi
aa_razavi@yahoo.com
3
Assistant Professor, Department of Information and Knowledge Science, Babol Branch, Islamic Azad University, Babol
AUTHOR
اباذری، محمدرضا؛ بابالحوائجی، فهیمه (1397). ارائه الگوی مفهومی کارآفرینی کتابداران کتابخانههای دانشگاهی بر اساس مؤلفههای انگیزشی(روانشناختی)، صلاحیتی و حمایتی. فصلنامه مطالعات دانششناسی، 4 (14): 57-79. 10.22054/JKS.2018.34645.1194
1
اباذری، محمدرضا؛ بابالحوائجی، فهیمه (1395). ویژگیهای حمایتی کارآفرینی کتابداران کتابخانههای دانشگاهی و کشف رابطه و تأثیر متغیرهای جمعیت شناختی بر ویژگیهای حمایتی کارآفرینی آنان. دانششناسی، 9 (33)، 7-16.
2
امین بیدختی، علی اکبر؛ رستگار، عباسعلی؛ نامنی، احمد. (1394). آیندهپژوهی تغییرات رویکردی آموزش عالی در توسعه سرمایه انسانی کشور؛ فصلنامه پژوهش و برنامه ریزی در آموزش عالی. ۲۱ (۳) :۳۱-۵۵.
3
حیدری، غلامرضا؛ خادمیزاده، شهناز؛ سقائیطلب، مرضیه (1394). ساخت و اعتباریابی پرسشنامهی موانع کارآفرینی در رشتهی علم اطلاعات و دانششناسی. مجله مدیریت اطلاعات و دانششناسی، 2 (4): 48-61.
4
دلاور، علی (1384).روش تحقیق در روانشناسی و علوم تربیتی. تهران: نشر ویرایش.
5
صابری، محمدکریم (1396). کارآفرینی در علم اطلاعات و دانششناسی: یک تحلیل عاملی اکتشافی. فصلنامه مطالعات ملی کتابداری و سازماندهی اطلاعات, 28(3), 29-45.
6
صفوی، زینب؛ مرادی، خدیجه (1392). چالشها و موانع ترغیب دانش آموختگان رشته کتابداری واطلاعرسانی به ایجاد کارآفرینی و اشتغالزایی. فصلنامه تحقیقات مدیریت آموزشی،2 (2): 29 -54.
7
قنادینژاد، فرزانه؛ حیدری، غلامرضا (1397). شناسایی و تحلیل موضوعات پژوهشی کارآفرینی در علم اطلاعات و دانششناسی از دیدگاه استادان و دانشجویان دکتری این رشته. پژوهشنامه کتابداری واطلاع رسانی، 8 (2): 39-62. https://doi.org/10.22067/riis.v0i0.65041
8
کاظمی،راضیه؛ سیفی، لیلی (1398). نقش کتابخانههای عمومی و دانشگاهی در ترویج و اشاعه خدمات کارآفرینی: مرور نظاممند. فصلنامه مطالعات ملی کتابداری و سازماندهی اطلاعات، 30 (2): 39-57. 10.30484/NASTINFO.2019.2324
9
محمدی، امین؛ مرجانی، سیدعباس (1393). بررسی قابلیتهای کارآفرینی کتابداران (مطالعه موردی: کتابداران کتابخانه مرکزی آستان قدس رضوی). پژوهشنامه کتابداری و اطلا عرسانی، 1 (4): 168– 153. https://doi.org/10.22067/riis.v4i1.17546
10
Amidu, G., & Umaru, I.M. (2016). Repositioning of Entrepreneurship Education for Entrepreneurial Success of Library and Information Science Students. A Study of Nasarawa State Polytechnic Lafia , Nigeria.
11
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (Seconded). Hillsdale, N J: Lawrence Erlbaum Associates.
12
Crumpton, M. and Bird, N. (2019), Educating the Entrepreneurial Librarian, Supporting Entrepreneurship and Innovation (Advances in Library Administration and Organization, Vol. 40), Emerald Publishing Limited, pp. 169-182. https://doi.org/10.1108/S0732-067120190000040011
13
Faulkner, A.E. (2018), Entrepreneurship resources in US public libraries:website analysis, Reference Services Review, 46 (1), pp. 69-90.
14
https://doi.org/10.1108/RSR-07-2017-0025
15
Gindling, T. H., & Newhouse, D. (2014). Self-employment in the developing world. World Development, 56.
16
Howit, D. & Cramer, D. (2009). Introduction to Statistics in Psycology. Translated by Pashasharifi, H.; Najafi Zand, J.; Mirhashemi, M.; Sharifi, N. & Manavipour, D., Tehran, Sokhan Press.
17
Ibrahim, N & Mas’ud, A. (2016). Moderating role of entrepreneurial orientation on the relationship between entrepreneurial skills, environmental factors and Jennings, D. F. (1994). Multiple perspectives of entrepreneurship: Text, readings, and cases (53-57). South-Western Pub.Doi: 10.5267/j.msl.2016.1.005
18
Kavoura, A. and Andersson, T. (2016), Applying Delphi method for strategic design of social entrepreneurship, Library Review, Vol. 65 No. 3, pp. 185-205. https://doi.org/10.1108/LR-06-2015-0062
19
Leonard, E., & Clementson, B. (2012). Business librarians and entrepreneurship: Innovation trends and characteristics. New Review of Information Networking, 17(1), 1-21. https://doi.org/10.1080/13614576.2012.671715.
20
Premand ,P.,Brodman, S.,Almeida, R., , Grun, R., Barouni, M. (2016). Entrepreneurship Education and Entry into Self-Employment Among University Graduates, World Development, 77 : 311–327. DOI: 10.1016/j.worlddev.2015.08.028
21
Toane, C., & Figueiredo, R. (2018). Toward core competencies for entrepreneurship librarians. Journal of Business & Finance Librarianship, 23(1), 35-62. https://doi.org/10.1080/08963568.2018.1448675
22
Ugwu, F., & Ezeani, C. N. (2012). Evaluation of entrepreneurship awareness and skills among LIS students in universities in South East Nigeria. Library Philosophy and Practice, 2 (2), 1-14.
23
ORIGINAL_ARTICLE
Semantics in Social Tagging Systems: A Systematic Review
Purpose: The objective of the present study has been to systematically review semantic research studies on social tagging systems in order to identify the researchers’ areas of interest, to investigate the impact of semantic issues on information retrieval in such systems, and to identify research gaps in this area.
Methodology: Ninety-eight studies were found by searching relevant databases. After initial investigation and consultation with two specialists in the field, 41 studies published in 2003-2018 were reviewed.
Findings: Important topics of semantic research on social tagging systems include producing an automatic semantic tagging algorithm, designing a semantic tagging system, producing an algorithm, extracting hierarchical relationship from a set of tags, and using WordNet to determine semantic relationships among tags. In addition, research gaps identified include devising a method for identifying sources containing a specific meaning of a tag without having to review all sources, exploring the possibility of using clustering methods to cluster sources or users of folksonomies, and designing a semantic tagging system which is user-friendly. All of these issues should be taken into account in future research.
Conclusion: Given the gaps identified, the subject of semantics in tagging systems needs further investigation, as it has a direct impact on search and retrieval by these systems.
https://nastinfo.nlai.ir/article_2405_d54c3aed222220f3b2a994252568e20b.pdf
2020-10-22
110
129
10.30484/nastinfo.2020.2357.1906
Folksonomies
Social Tagging Systems
Semantic Relations
Information retrieval
Systematic review
Z.
Honarjooyan
z.honarjooyan@gmail.com
1
PhD Candidate, Department of Knowledge and Information Science, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
AUTHOR
mahdieh
mirzabeigi
mmirzabeigi@gmail.com
2
Associate Professor, Department of Knowledge and Information Science, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
LEAD_AUTHOR
سعادت، رسول؛ شعبانی، احمد؛ عاصمی، عاصفه؛ چشمهسهرابی، مظفر (١٣٩٧). قابلیت ردهبندیهای مردمی در تقویت نظامهای سازماندهی دانش حرفهای: مروری بر مفاهیم و پژوهشها. مطالعات ملی کتابداری و سازماندهی اطلاعات. ٢٩(4) : ٧-٢٦.
1
Abbasi, R., & Staab, S. (2009, June). RichVSM: enRiched vector space models for folksonomies. In Proceedings of the 20th ACM conference on Hypertext and hypermedia (pp. 219-228). ACM. https://doi.org/10.1145/1557914.1557952
2
Abel, F., Henze, N., Krause, D., & Kriesell, M. (2010). Semantic enhancement of social tagging systems. In Web 2. 0 & Semantic Web (pp. 25-54). Springer, Boston, MA.
3
Alruqimi, M., & Aknin, N. (2019). Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy. Journal of King Saud University-Computer and Information Sciences, 31(1), 15-21. https://doi.org/10.1016/j.jksuci.2017.10.005
4
Angeletou, S. (2008). Semantic Enrichment of Folksonomy Tag-spaces. In Proceedings of the 7th International Semantic Web Conference (ISWC’08), pp. 889-894
5
Aurnhammer, M., Hanappe, P., & Steels, L. (2006). Augmenting Navigation for Collaborative Tagging with Emergent Semantics. International Semantic Web Conference (ISWC2006); Lecture Notes in Computer Science, Athens, Georgia, USA Retrieved January 31, 2008 from http://iswc2006.semanticweb.org/items/Aurnhammer2006ve.pdf
6
Bindelli, S., Criscione, C., Curino, C. A., Drago, M. L., Eynard, D., & Orsi, G. (2008, November). Improving search and navigation by combining ontologies and social tags. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 76-85). Springer, Berlin, Heidelberg.
7
Bitzer, S., Thoroe, L., Schumann, M. (2010). Folksonomy: Creating metadata through collaborative tagging. In T. Dumova & R. Fiordo (Eds.), Social interaction technologies and collaboration software: Concepts and trends (Chapter 14, pp. 147-157). Pennsylvania: Information Science Reference.
8
Calefato, F., Gendarmi, D., & Lanubile, F. (2007, December). Towards Social Semantic Suggestive Tagging. In SWAP (Vol. 314).
9
Cantador, I., Konstas, I., & Jose, J. M. (2011). Categorising social tags to improve folksonomy-based recommendations. Journal of Web Semantics, 9(1), 1-15.
10
Cattuto, C., Benz, D., Hotho, A., & Stumme, G. (2008a). Semantic analysis of tag similarity measures in collaborative tagging systems. arXiv preprint arXiv:0805. 2045.
11
Cattuto, C., Benz, D., Hotho, A., & Stumme, G. (2008b). Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. 615-631. https://doi.org/10.1007/978-3-540-88564-1_39
12
Dattolo, A., Eynard, D., & Mazzola, L. (2011, March). An integrated approach to discover tag semantics. In Proceedings of the 2011 ACM symposium on applied computing (pp. 814-820). ACM. https://doi.org/10.1145/1982185.1982359
13
Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A. & Zien, J. Y. (2003, May). SemTag and Seeker: Bootstrapping the semantic web via automated semantic annotation. In Proceedings of the 12th international conference on World Wide Web (pp. 178-186). ACM.
14
Dong, Hang & Wang, Wei & Coenen, Frans. (2018). Rules for Inducing Hierarchies from Social Tagging Data. 10. 1007/978-3-319-78105-1_38.
15
Eynard, Davide & Mazzola, Luca & Dattolo, Antonina. (2013). Exploiting tag similarities to discover synonyms and homonyms in folksonomies. Software: Practice and Experience. 43. https://doi.org/10.1002/spe.2150
16
Ghabayen, Ayman & Mohd Noah, Shahrul Azman. (2017). Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems. International Journal on Advanced Science, Engineering and Information Technology (IJASEIT). 7. 2063-2070. DOI: http://dx.doi.org/10.18517/ijaseit.7.5.1826
17
Giannakidou, E., Kompatsiaris, I., & Vakali, A. (2008, August). Semsoc: Semantic, social and content-based clustering in multimedia collaborative tagging systems. In 2008 IEEE International Conference on Semantic Computing (pp. 128-135). IEEE. DOI: 10.1109/ICSC.2008.73
18
Golder, S. A., & Huberman, B. A. (2006). Usage patterns of collaborative tagging systems. Journal of information science, 32(2), 198-208. DOI: 10.1177/0165551506062337
19
Heymann, P., & Garcia-Molina, H. (2006). Collaborative creation of communal hierarchical taxonomies in social tagging systems. Stanford.
20
Hope, G., Wang, T., & Barkataki, S. (2007, September). Convergence of web 2. 0 and semantic web: A semantic tagging and searching system for creating and searching blogs. In International Conference on Semantic Computing (ICSC 2007)(pp. 201-208). IEEE. DOI:10.1109/ICSC.2007.95
21
Huang, S. L., Lin, S. C., & Chan, Y. C. (2012). Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing. Information Processing & Management, 48(4), 599-617. https://doi.org/10.1016/j.ipm.2011.07.004
22
Jabeen, F., Khusro, S., Majid, A., & Rauf, A. (2016). Semantics discovery in social tagging systems: A review. Multimedia Tools and Applications, 75(1), 573-605. https://doi.org/10.1007/s11042-014-2309-3
23
Jiao, X., & Chen, Y. (2010, October). A semantic tagging system for biomedical articles. In 2010 3rd International Conference on Biomedical Engineering and Informatics (Vol. 7, pp. 2733-2738). IEEE. DOI: 10.1109/BMEI.2010.5639867
24
Kanishcheva, Olga & Nikolova, Ivelina & Angelova, Galia. (2018). Evaluation of Automatic Tag Sense Disambiguation Using the MIRFLICKR Image Collection.
25
10. 1007/978-3-319-99344-7_6.
26
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
27
Laniado, D., Eynard, D., & Colombetti, M. (2007, December). Using WordNet to turn a folksonomy into a hierarchy of concepts. In Semantic Web Application and Perspectives-Fourth Italian Semantic Web Workshop (pp. 192-201).
28
Lezcano, L., García-Barriocanal, E., & Sicilia, M. A. (2012). Bridging informal tagging and formal semantics via hybrid navigation. Journal of Information Science, 38(2), 140-155. https://doi.org/10.1177/0165551511435882
29
Li, R., Bao, S., Yu, Y., Fei, B., & Su, Z. (2007, May). Towards effective browsing of large scale social annotations. In Proceedings of the 16th international conference on World Wide Web (pp. 943-952). ACM. https://doi.org/10.1145/1242572.1242700
30
Limpens, F., Gandon, F., & Buffa, M. (2009). Collaborative semantic structuring of folksonomies (short article).
31
Limpens, F., Gandon, F., & Buffa, M. (2010, June). Helping online communities to semantically enrich folksonomies. In Web Science 2010 (pp. 1-8).
32
Majid, A., Khusro, S., & Rauf, A. (2011, July). Semantics in social tagging systems: A review. In International Conference on Computer Networks and Information Technology (pp. 191-203). IEEE.
33
Manzato, M. G., & Goularte, R. (2012, October). Automatic annotation of tagged content using predefined semantic concepts. In Proceedings of the 18th Brazilian symposium on Multimedia and the web (pp. 237-244). ACM. https://doi.org/10.1145/2382636.2382688
34
Marchetti, A., Tesconi, M., Ronzano, F., Rosella, M., & Minutoli, S. (2007, May). Semkey: A semantic collaborative tagging system. In Workshop on Tagging and Metadata for Social Information Organization at WWW (Vol. 7, pp. 8-12).
35
Min, Q. X., Nazim Uddin, M. & Jo, G. S. (2010, February). The wordNet based semantic relationship between tags in folksonomies. In 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) (Vol. 2, pp. 815-819). IEEE. DOI:10.1109/ICCAE.2010.5451821
36
Morrison, P. J. (2008). Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web. Information Processing & Management, 44(4), 1562- 1579. doi=10.1.1.495.4186&rep=rep1&type=pdf
37
Nazim Uddin, M., Duong, T. H., Nguyen, N. T., Qi, X. M., & Jo, G. S. (2013). Semantic similarity measures for enhancing information retrieval in folksonomies . Expert Systems with Applications, 40(5), 1645-1653. https://doi.org/10.1016/j.eswa.2012.09.006
38
Newman, J. (2011). Corpora and cognitive linguistics. Revista Brasileira de Linguística Aplicada, 11(2), 521-559. http://dx.doi.org/10.1590/S1984-63982011000200010
39
Panke, S., & Gaiser, B. (2009). ``With My Head Up in the Clouds'' Using Social Tagging to Organize Knowledge. Journal of Business and Technical Communication, 23(3), 318-349. https://doi.org/10.1177/1050651909333275
40
Peters, I. (2009). Folksonomies: Indexing and retrieval in web 2. 0. Berlin: De Gruyter Saur. DOI: https://doi.org/10.1515/9783598441851.toc
41
Razikin, Kh., Goh, D. H., Chua, Alton Y. K & Lee, Ch. S. (2011). Social tags for resource
42
discovery: a comparison between machine learning and user-centric approaches. Journal of Information Science, 37 (4): 391-404. DOI: 10.1177/0165551511408847
43
Rohland, M., & Streibel, O. (2009). Algorithmic extraction of tag semantics. In FIS2009: Proceedings of the 2nd international Future Internet Symposium, Berlin.
44
Shamsfard, M., Hesabi, A., Fadaei, H., Mansoory, N., Famian, A., Bagherbeigi, S., Fekri, E. and et al. (2010). Semi Automatic Development of Farsnet; the Persian Wordnet. Proceedings of 5th Global WordNet Conference (GWA2010). Mumbai, India
45
Shen, M., Wang, J., & Liu, X. (2018). Community detection in social tagging systems based semantics of tags. ICMLC https://doi.org/10.1145/3195106.3195156
46
Song, J., Zhou, Y., Jung, H., & Davis, J. (2010). Adding Context to Social Tagging Systems. In Proceedings of the 21st Australasian Conference on Information Systems.
47
Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2010). A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Transactions on Knowledge and Data Engineering, 22(2), 179-192. DOI: 10.1109/TKDE.2009.85
48
Tesconi, M., Ronzano, F., Marchetti, A., & Minutoli, S. (2008, October). Semantify del. icio. us: Automatically turn your tags into senses. In The 7th International Semantic Web Conference(p. 67).
49
Vicient, C., & Moreno, A. (2013, September). A Study on the Influence of Semantics on the Analysis of Micro-blog Tags in the Medical Domain. In International Conference on Availability, Reliability, and Security (pp. 446-459). Springer, Berlin, Heidelberg.
50
Weller, K., Peters, I., & Stock, W. (2010). Folksonomy: the collaborative knowledge organization system. In T. Dumova & R. Fiordo (Eds. ), Social interaction technologies and collaboration software: Concepts and trends (Chapter 13, pp. 132-146). Pennsylvania: Information Science Reference.
51
Yang, H. C. (2005, September). Bridging the www to the semantic web by automatic semantic tagging of web pages. In The Fifth International Conference on Computer and Information Technology (CIT'05) (pp. 238-242). IEEE. https://doi.org/10.1109/CIT.2005.81
52
Yang, W., Zhang, Z., & Huang, G. (2019, December). Building Tag Systems Based on Advanced Semantic Hierarchical Clustering. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Vol. 1, pp. 1241-1247). IEEE. DOI: 10.1109/IAEAC47372.2019.8997666
53
Yeung, A., Gibbins, N., & Shadbolt, N. (2007). Understanding the semantics of ambiguous tags in folksonomies.
54
Yi, K. (2011). An empirical study on the automatic resolution of semantic ambiguity in social tags. Proceedings of the American Society for Information Science and Technology, 48(1), 1-10. https://doi.org/10.1002/meet.2011.14504801175
55
Zhang, M., Wu, T., Ji, Q., Qi, G., & Sun, Z. (2019, July). Mining Hypernym-Hyponym Relations from Social Tags via Tag Embedding. In International Conference on Artificial Intelligence and Security (pp. 319-328). Springer, Cham
56
Zhou, M., Bao, S., Wu, X., & Yu, Y. (2007). An unsupervised model for exploring hierarchical semantics from social annotations. In The Semantic Web (pp. 680-693). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76298-0_49
57
Zorn, H. P., & Gurevych, I. (2011, December). A study of sense-disambiguated networks induced from folksonomies. In Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation (pp. 323-332).
58