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Folksonomy and information retrieval

Profile image of Isabella PetersIsabella Peters

2007, Proceedings of the American Society for Information Science and Technology

https://doi.org/10.1002/MEET.1450440226
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Abstract

In Web 2.0 services "prosumers" -producers and consumers -collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, "tags", without regarding any rules.

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Web 2.0 and Collaborative Tagging
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2010 Fifth International Conference on Internet and Web Applications and Services, 2010

The aim of the present paper is to analyze the user's behavior in Web 2.0 through comparing statistical data from a Web 1.0 site and an online community from Web 2.0. In order to emphasize the difference between the two concepts, we have taken into consideration the following: new vs. returning visitors, visitor loyalty, length as well as the depth of the visit to the site. Analyzing the data, one can observe that the visitor loyalty, their lengthy visits to the site and the pages they view are much higher for the site built on the principles of Web 2.0, compared to the classical sites. Likewise, we developed and implemented a collaborative tagging system that allows every registered user to publish, save and label the content with the help of tags. Therefore, this can help in building a model that not only offers customized content but also adapts the site to user needs by providing a recommendation system and aggregate collective intelligence.

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Collaborative Tagging as a User-Generated Tool for the Organization of Online Healthcare Resources
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Social Bookmarking for Scholarly Digital Libraries
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Exploring the Context of User, Creator and Intermediary Tagging
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This paper examines the results of a study of the three groups involved in creating index keywords or tags: users, authors and intermediaries. Keywords from each of the three groups were compared to determine similarities and differences in term use. Comparisons suggested that there were important differences in the contexts of the three groups that should be taken into account when assigning keywords or designing systems for the organisation of information.

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HT06, tagging paper, taxonomy, Flickr, academic article, to read
danah boyd

Proceedings of the …, 2006

In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., “tags”) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.

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The dynamics and semantics of collaborative tagging
Harry Halpin

… of the 1st Semantic Authoring and …, 2006

The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classification schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from "popular" tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.

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Technical Folksonomy of Tagging Information
Sudip Hatua

IRA International Journal of Education and Multidisciplinary Studies (ISSN 2455–2526), 2016

The Study of the various articles published in Library and Information Science Journals in the resent times shows that the keywords provided by the authors along with their articles are mostly uncontrolled. They are basically phrases. In spite of the knowledge of controlled vocabulary and various subject heading scheme they mostly are using natural word and sentences to represent the thought content of their research outcomes. This is generating new trends of representing subjects known as technical folksonomy.

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Tagging and Searching
Patrick Morrison

jasonmorrison.net

Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.

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Position paper, tagging, taxonomy, flickr, article, toread
danah boyd

In Collaborative Web Tagging …, 2006

In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., “tags”) to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems. Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photosharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.

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The exploitation of social tagging in libraries
Constantia (Ntina) Kakali

2011

Nowadays, many libraries have developed social tagging services, after the considerable use of social tagging and deployment as key components of Web 2.0. Another set of libraries have enriched the search and indexing services of their OPACs with the folksonomy of Library Thing. The evaluation of these metadata (folksonomies) and further their exploitation is one of our challenges. At the same time, we explore ways to define a methodology for the exploitation of user’s vocabulary by the traditional indexing systems maintained by information organizations. Firstly, our research focused on the user acceptance for the OPACIAL an OPAC 2.0 with social tagging functionalities. The users’ behavior was studied by qualitative evaluation using questionnaires and structured interviews. Social tags are then analyzed and categorized to identify the users’ needs. After finding that a large number of tags consist new terms for the authority file of a Library, these tags were searched in other auth...

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References (84)

  1. Aurnhammer, M., Hanappe, P., & Steels, L. (2006). Augmenting navigation for collaborative tagging with emergent semantics. Lecture Notes in Computer Science, 4273, 58-71.
  2. Bar-Ilan, J., Shoham, S., Idan, A., Miller, Y, & Shachak, A. (2006). Structured vs. unstructured tagging -A case study. In Proceedings of the 15th International WWW Conference. Collaborative Web Tagging Workshop.
  3. Brin, S., Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30, 107-117.
  4. Brooks, C.H.; Montanez, N. (2006a). An analysis of the effectiveness of tagging in blogs. Computation Approaches to Analyzing Weblogs. Papers from the 2006 AAAI Spring Symposium (pp. 9-15). Nicolov, N., Salvetti, F., Liberman, M., & Martin, J.H. (eds.), Menlo Park, Cal: AAAI Press. (Technical Report SS-06-03. American Association for Artificial Intelligence).
  5. Brooks, C.H., Montanez, N. (2006b). Improved annotation of the blogosphere via autotagging and hierarchical clustering. In Proceedings of the 15th International World Wide Web Conference (pp. 625-632). New York: ACM.
  6. Butterfield, D.S., Costello, E., Fake, C., Henderson-Begg, C.J., & Mourachow, S. (2006). Interestingness ranking of media objects. Patent Application No. US 2006/0242139 A1. Filed: Feb. 8, 2006. Publ.: Oct. 26, 2006.
  7. Butterfield, D.S., Costello, E., Fake, C., Henderson-Begg, C.J., Mourachow, S., & Schachter, J.E. (2006). Media object metadata association and ranking. span class = 'referencesource'>Patent Application No. US 2006/0242178 A1. Filed: Feb. 8, 2006. Publ.: Oct. 26, 2006.
  8. Bruza, P.D., Song, D.W., & Wong, K.F. (2000). Aboutness from a commonsense perspective. Journal of the American Society for Information Science, 51, 1090-1105.
  9. Campbell, D.G. (2006). A phenomenological framework for the relationship between the semantic Web and user-centered tagging systems. In 17th ASIS&T SIG/CR Classification Research Workshop.
  10. Cattuto, C., Loreto, V., & Pietronero, L. (2007). Semiotic dynamics and collaborative tagging. In Proceedings of the National Academy of Sciences of the United States of America, 104(5), 1461-1464.
  11. Christiaens, S. (2006). Metadata mechanisms: From ontology to folksonomy … and back. Lecture Notes in Computer Science, 4277, 199-207.
  12. Culliss, G.A. (1997). Method for organizing information. Patent-No. US 6.006.222. Granted: Dec. 21, 1999. Applied: Jan. 8, 1997. file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (27 of 33) [9/25/2007 5:25:16 PM] Folksonomy and Information Retrieval Diederich, J., Iofciu, T. (2006). Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice (TEL-CoPs'06).
  13. Dye, J. (2006). Folksonomy: A game of high-tech (and high-stakes) tag. E-Content, 29(3), 38-43.
  14. Egghe, L., Rousseau, R. (1990). Introduction to Informetrics. Elsevier. Amsterdam Fichter, D. (2006). Intranet applications for tagging and folksonomies. Online, 30(3), 43-45.
  15. Fidel, R. (2006). An ecological approach to the design of information systems. Bulletin of the American Society for Information Science and Technology, 33(1), 6-8.
  16. Furnas, G.W., Fake, C., von Ahn, L., Schachter, J., Golder, S., Fox, K., Davis, M., Marlow, C., & Naaman, M. (2006). Why do tagging systems work? CHI '06 Extended Abstracts on Human Factors in Computing Systems (pp. 36-39). New York: ACM.
  17. Garfield, E. (1979). Citation Indexing. Wiley New York Gendarmi, D., Lanubile, F. (2006). Community-driven ontology evolution based on folksonomies. Lecture Notes in Computer Science, 4277, 181-188.
  18. Golder, S.A., Huberman, B.A. (2006). Usage patterns of collaborative tagging systems. Journal of Information Science, 32(2), 198-208.
  19. Gordon-Murnane, L. (2006). Social bookmarking, folksonomies, and Web 2.0 tools. Searcher -The Magazine for Database Professionals, 14(6), 26-38.
  20. Gruber, T. (2005). Ontology of folksonomy: A mash-up of apples and oranges. 1st On-Line Conference on Metadata and Semantics Research (MTSR '05).
  21. Gruber, T. (2006). Where the social Web meets the semantic Web. Lecture Notes in Computer Science, 4273, 994.
  22. Guy, M., Tonkin, E. (2006). Folksonomies: Tidying up tags? D-Lib Magazine, 12(1).
  23. Hammond, T., Hannay, T., Lund, B., & Scott, J. (2005). Social bookmarking tools. A general review. Part 1. D- Lib Magazine, 12(1).
  24. Heymann, P., Garcia-Molina, H. (2006). Collaborative creation of communal hierarchical taxonomies in social tagging system. InfoLab Technical Report; 2006-10. Stanford: Stanford University / Computer Science Dept.
  25. Hotho, A., Jäschke, R., Schmitz, C., & Stumme, G. (2006a). Information retrieval in folksonomies: Search and ranking. Lecture Notes in Computer Science, 4011, 411-426. file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (28 of 33) [9/25/2007 5:25:16 PM] Folksonomy and Information Retrieval Hotho, A., Jäschke, R., Schmitz, C., & Stumme, G. (2006b). Trend detection in folksonomies. Lecture Notes in Computer Science, 4306, 56-70.
  26. Huang, H. (2006). Tag distribution analysis using the power law to evaluate social tagging systems: A case study in the Flickr database. 17th ASIS&T SIG/CR Classification Research Workshop. Abstracts of Posters (pp. 14-15).
  27. Hutchins, W.J. (1978). The concept of 'aboutness' in subject indexing. Aslib Proceedings, 30(5), 172-181.
  28. Ingwersen, P. (2002). Cognitive perspectives of document representation. CoLIS 4: 4th International Conference on Conceptions of Library and Information Science [pp. 285-300].
  29. Greenwood Village: Libraries Unlimited.
  30. Jung, S., Herlocker, J.L., & Webster, J. (2007). Click data as implicit relevance feedback in web search. Information Processing & Management, 43, 791-807.
  31. Kipp, M.E.I. (2006a). @toread and cool: Tagging for time, task and emotion. 17th ASIS&T SIG/CR Classification Research Workshop. Abstracts of Posters (pp. 16-17).
  32. Kipp, M.E.I. (2006b). Complementary or discrete contexts in online indexing: A comparison of user, creator and intermediary keywords. Canadian Journal of Information and Library Science, 30(3), in prep.
  33. Kipp, M.E.I. & Campbell, D.G. (2006). Patterns and inconsistencies in collaborative tagging systems: An examination of tagging practices. Proceedings of the 69th Annual Meeting of the American Society for Information Science & Technology (Vol. 43), Austin, TX (CD-ROM).
  34. Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604- 632. Kroski, E. (2005). The Hive Mind: Folksonomies and user-based tagging. Blog post; 2005-12- 07http://infotangle.blogsome.com/2005/12/07/the-hive-mind-folksonomies-and-user-based-tagging Lancaster, F.W. (2003). Indexing and Abstracting in Theory and Practice. University of Illinois. 3rd ed. Champaign Layne, S. (2002). Subject access to art images. Introduction to Art Image Access Baca, M. (ed.): [pp. 1-19].
  35. Los Angeles: Getty Research Institute.
  36. Lin, X., Beaudoin, J.E., Bui, Y., & Desai, K. (2006). Exploring characteristics of social classification. 17th ASIS&T SIG/CR Classification Research Workshop.
  37. Maron, M.E. (1977). On indexing, retrieval and the meaning of about. Journal of the American Society for Information Science, 28, 38-43.
  38. Lund, B., Hammond, T., Flack, M., & Hannay, T. (2005). Social bookmarking tools (II). A case study - Connotea. D-Lib Magazine, 11(4).
  39. Macgregor, G., McCulloch, E. (2006). Collaborative tagging as a knowledge organisation and resource discovery tool. Library Review, 55(5), 291-300.
  40. MacLaurin, M.B. (2005). Selection-based item tagging. Patent Application no. US 2007/0028171 A1. Filed: Jul. 29, 2005. Publ.: Feb. 1, 2007.
  41. Mai, J.M. (2006). Contextual analysis for the design of controlled vocabularies. Bulletin of the American Society for Information Science and Technology, 33(1), 17-19.
  42. Markey, K. (1986). Subject Access to Visual Resource Collections. Greenwood Westport Markkula, M., Sormunen, E. (2000). End-user searching challenges indexing practices in the digital newspaper photo archive. Information Retrieval, 1, 259-285.
  43. Mathes, A. (2004). Folksonomies -Cooperative Classification and Communication Through Shared Metadata. University of Illinois Urbana-Campaign / Graduate School of Library and Information Science. Urbana, Ill.
  44. Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. Proceedings of the 17th Conference on Hypertext and Hypermedia (pp. 31-40). New York: ACM. McFedries, P. (2006). Folk wisdom. IEEE Spectrum, 43(Febr.), 80.
  45. Merholz, P. (2004). Metadata for the masses. Blog post, 2004-10- 19http://www.adaptivepath.com/publications/essays/archives/000361.php
  46. Mika, P. (2005). Ontologies are us: A unified model of social networks and semantics. Lecture Notes in Computer Science, 3729, 522-536.
  47. Millen, D.R., Feinberg, J., & Kerr, B. (2006). DOGEAR. Social bookmarking in the enterprise. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 111-120). New York: ACM.
  48. Miller, G.A. (1998). Nouns in WordNet. WordNet. An Electronic Lexical Database Fellbaum, C. (ed.), (pp. 23- 46). Cambridge, Mass., London: MIT Press.
  49. Murison, J. (2005). Messageboard topic tagging: User tagging of collectively owned community content. Proceedings of the 2005 Conference on Designing for User eXperience (art. no 5). New York: American Institute of Graphic Art.
  50. O'Reilly, T. (2005). What is Web 2.0. Design patterns and business models for the next generation of software. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html
  51. Ornager, S. (1995). The newspaper image database. Empirical supported analysis of users' typology and word association clusters. Proceedings of the 18th Annual International ACM SIGIR Conference on file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (30 of 33) [9/25/2007 5:25:16 PM] Folksonomy and Information Retrieval Research and Development in Information Retrieval (pp. 212-218). New York: ACM.
  52. Ott, E.S. (2006). Picture tagging. Patent Application US 2007/0079321 A1. Filed: Feb. 17, 2006. Publ.: Apr. 5, 2007.
  53. Panofsky, E. (1975) Sinn und Deutung in der bildenden Kunst [Sense and Interpretation in Fine Arts].
  54. DuMont Köln Panofsky, E. (2006). Ikonographie und Ikonologie [Iconography and Iconology].
  55. DuMont Köln Peters, I. (2006a). Inhaltserschließung von Blogs und Podcasts im betrieblichen Wissensmanagement [Indexing of blogs and podcasts for the corporate knowledge management]. Content. 28. Online-Tagung der DGI, 58. Jahrestagung der DGI. Proceedings Ockenfeld, M. (ed.): [pp. 143-151]. Frankfurt: DGI.
  56. Peters, I. (2006b). Against folksonomies: Indexing blogs and podcasts for corporate knowledge management. Preparing for Information 2.0. Online Information 2006. Proceedings Jezzard, H. (ed.): (pp. 93-97). London: Learned Information Europe.
  57. Peters, I. (2007). Web 2.0 und Fachinformation. Folksonomies im Web 2.0 [Web 2.0 and professional information. Folksonomies in Web 2.0 services].
  58. Peters, I., Stock, W.G. (2006). Corporate Blogs im Wissensmanagement [Corporate blogs used in knowledge management].
  59. Wissensmanagement, no. 6, 40-41.
  60. Peterson, E. (2006). Beneath the metadata. Some philosophical problems with folksonomies. D-Lib Magazine, 12(11).
  61. Pluzhenskaia, M. (2006). Folksonomies or fauxsonomies: How social is social bookmarking? 17th ASIS&T SIG/CR Classification Research Workshop. Abstracts of Posters (pp. 23-24).
  62. Quintarelli, E. (2005). Folksonomies: Power to the people. ISKO Italy UniMIB meeting, Milan, June 24, 2005.
  63. Rasmussen, E.M. (1997) Indexing images. Annual Review of Information Science and Technology, 32, 169- 196. Reamy, T. (2006). Folksonomies and complexity theory: Evolved information structures. Preparing for Information 2.0. Online Information 2006. Proceedings Jezzard, H. (ed.): (pp. 111-113). London: Learned Information Europe.
  64. Rocchio, J.J. (1971). Relevance feedback in information retrieval. The SMART Retrieval System - Experiments in Automatic Document Processing Salton, G. (ed.): (pp. 313-323). Englewood Ciffs, N.J.: Prentice Hall
  65. Russell, T. (2006). Cloudalicious: Folksonomy over time. Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (p. 364). New York: ACM. file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (31 of 33) [9/25/2007 5:25:16 PM] Folksonomy and Information Retrieval Schmitz, P. (2006). Inducing ontology from Flickr tags. Proceedings of the 15th International WWW Conference. Collaborative Web Tagging Workshop.
  66. Shatfort, S. (1986). Analyzing the subject of a picture. A theoretical approach. Cataloguing and Classification Quarterly, 6(3), 39-62.
  67. Shirky, C. (2004). Folksonomy. Blog post; 2004-08- 24http://many.corante.com/archives/2004/08/25/folksonomy.php
  68. Shirky, C. (2005). Ontology is overrated: Categories, Links, and Tags. www.shirky.com/writings/ontology_overrated.html
  69. Smith, G. (2004). Folksonomy: Social classification. Blog post; 2004-08- 03http://atomiq.org/archives/2004/08/folksonomy_social_classification.html
  70. Smith, M.K. (2006). Viewer tagging in art museum: Comparisons to concepts and vocabularies of art museum visitors. 17th ASIS&T SIG/CR Classification Research Workshop.
  71. Spiteri, L.F. (2006). The use of folksonomies in public library catalogues. The Serials Librarian, 51(2), 75-89.
  72. Spyns, P., de Moor, A., Vandenbussche, J., & Meersman, R. (2006). From folksonomies to ontologies: How the twain meet. Lecture Notes in Computer Science, 4275, 738-755.
  73. Stock, W.G. (2006). On relevance distributions. Journal of the American Society for Information Science and Technology, 57(8), 1126-1129.
  74. Stock, W.G. (2007). Information Retrieval. Suchen und Finden von Informationen [Information Retrieval. Searching and Finding Information]. Oldenbourg München, Wien
  75. Stock, W.G. & Weber, S. (2006). Facets of informetrics. Information -Wissenschaft und Praxis, 57(8), 385- 389. Toffler, A. (1980). The Third Wave. Morrow New York Tonkin, E. (2006). Searching the long tail: Hidden structure in social tagging. 17th ASIS&T SIG/CR Classification Research Workshop.
  76. Trant, J. (2006a). Exploring the potential for social tagging and folksonomy in art museums: Proof of concept. New Review of Hypermedia and Multimedia, 12(1), 83-105.
  77. Trant, J. (2006b). Social classification and folksonomy in art museums: Early data from the steve.museum tagger prototype. 17th ASIS&T SIG/CR Classification Research Workshop. Vander Wal, T. (2004). Feed on this. Blog post; 2004-10- 03http://www.vanderwal.net/random/category.php?cat=153 file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (32 of 33) [9/25/2007 5:25:16 PM] Folksonomy and Information Retrieval Vander Wal, T. (2005). Explaining and showing broad and narrow folksonomies. Blog post; 2005-02- 21http://www.vanderwal.net/random/category.php?cat=153
  78. Veres, C. (2006a). The language of folksonomies: What tags reveal about user classification. Lecture Notes in Computer Science, 3999, 58-69.
  79. Veres, C. (2006b). Concept modeling by the masses: Folksonomy structure and interoperability. Lecture Notes in Computer Science, 4215, 325-338.
  80. Weiss, A. (2005). The power of collective intelligence. netWorker, 9(3), 16-23.
  81. Winget, M. (2006). User-defined classification on the online photo sharing site Flickr … Or, how I learned to stop worrying and love the million typing monkeys. 17th ASIS&T SIG/CR Classification Research Workshop.
  82. Wu, H., Zubair, M., & Maly, K. (2006). Harvesting social knowledge from folksonomies. Proceedings of the 17th Conference on Hypertext and Hypermedia (pp. 111-114). New York: ACM.
  83. Xu, Z., Fu, Y., Mao, J., & Su, D. (2006). Towards the semantic Web: Collaborative tag suggestions. Proceedings of the 15th International WWW Conference. Collaborative Web Tagging Workshop.
  84. Zhang, L., Wu, X., & Yu, Y. (2006). Emergent semantics from folksonomies: A quantitative study. Lecture Notes in Computer Science, 4090, 168-186. file:///M|/Web%20Sites/ASIST_AM07/papers/26/26_paper.html (33 of 33) [9/25/2007 5:25:16 PM]

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    Text classification with semantically enriched word embeddings
    George Giannakopoulos

    Natural Language Engineering, 2020

    The recent breakthroughs in deep neural architectures across multiple machine learning fields have led to the widespread use of deep neural models. These learners are often applied as black-box models that ignore or insufficiently utilize a wealth of preexisting semantic information. In this study, we focus on the text classification task, investigating methods for augmenting the input to deep neural networks (DNNs) with semantic information. We extract semantics for the words in the preprocessed text from the WordNet semantic graph, in the form of weighted concept terms that form a semantic frequency vector. Concepts are selected via a variety of semantic disambiguation techniques, including a basic, a part-of-speech-based, and a semantic embedding projection method. Additionally, we consider a weight propagation mechanism that exploits semantic relationships in the concept graph and conveys a spreading activation component. We enrich word2vec embeddings with the resulting semantic...

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