Skip to main content
SpringerLink
Log in
Book cover

Social Information Access pp 310–343Cite as

Accessing Information with Tags: Search and Ranking

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

Abstract

With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.

In this article we review several aspects of the tagging process and its role for accessing information using search and ranking in tagging systems. A literature review of existing work in this area will be complemented by case studies which showcase findings of our own research. We will start with discussing typical properties of tagging systems, present example systems and their typical functionality, their strengths and weaknesses, the users’ motivations, and different types of tags and annotators. To get an understanding of search and ranking methods, we use the formalization of tagging systems as a tripartite graph of users, tags, and resources – known as folksonomy – and discuss its network properties.

Ranking in folksonomies is a core component of information access in such systems. We review two central algorithms, FolkRank and Adjusted Hits, before focussing on a tighter integration of Web search and folksonomies. For this, we compare search in standard search engines with tag-based search, review Social PageRank, a method for ranking web pages that is using the information of tagging systems, and discuss learning-to-rank methods which also utilize tags to improve the ranking of web pages. Finally, we present the concept of logsonomies which provide a unified view on search and tagging by considering clicks on search results as an implicit tagging process. Concluding, we discuss future options for a tighter integration of tagging and search with the goal of improving information access based on user provided content.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.google.com/.

  2. 2.

    http://www.bing.com/.

  3. 3.

    http://del.icio.us (as of June 2017, the service stopped).

  4. 4.

    http://www.connotea.org/ (as of March 2013, the service stopped).

  5. 5.

    http://www.citeulike.org.

  6. 6.

    http://www.bibsonomy.org.

  7. 7.

    http://www.flickr.com.

  8. 8.

    http://www.youtube.com.

  9. 9.

    http://technorati.com/.

  10. 10.

    https://twitter.com/.

  11. 11.

    http://www.dblp.uni-trier.de.

  12. 12.

    https://www.bibsonomy.org/persons.

  13. 13.

    http://www.technologyreview.com/tr35/profile.aspx?trid=432.

  14. 14.

    http://techcrunch.com/2011/04/27/yahoo-sells-delicious-to-youtube-founders/.

  15. 15.

    http://mashable.com/2012/10/04/delicious-redesign/ and https://blog.pinboard.in/2017/06/pinboard_acquires_delicious/.

  16. 16.

    see e.g.,http://mashable.com/2014/05/08/delicious-acquired-science-inc/.

  17. 17.

    http://pinboard.in/.

  18. 18.

    www.squidoo.com.

  19. 19.

    www.hubpages.com.

  20. 20.

    See also Sect. 1 of Chap. 6 of this book [28].

  21. 21.

    http://dir.yahoo.com/.

  22. 22.

    http://dmoz.org/.

  23. 23.

    http://www.google.de.

References

  1. Abel, F.: Contextualization, user modeling and personalization in the social web: from social tagging via context to cross-system user modeling and personalization. Ph.D. thesis, University of Hanover (2011). http://d-nb.info/1014252423

  2. Abel, F., Baldoni, M., Baroglio, C., Henze, N., Kawase, R., Krause, D., Patti, V.: Leveraging search and content exploration by exploiting context in folksonomy systems. New Rev. Hypermedia Multimed. 16(1–2), 33–70 (2010)

    Article  Google Scholar 

  3. Abel, F., Henze, N., Krause, D.: Ranking in folksonomy systems: can context help? In: Shanahan, J.G., Amer-Yahia, S., Manolescu, I., Zhang, Y., Evans, D.A., Kolcz, A., Choi, K.-S., Chowdhury, A. (eds.) Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, 26–30 October 2008, pp. 1429–1430. ACM (2008)

    Google Scholar 

  4. Adamic, L.: Zipf, power-laws, and pareto - a ranking tutorial (2002). http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html

  5. Adar, E.: User 4xxxxx9: anonymizing query logs. In: Query Logs Workshop at WWW 2006 (2007)

    Google Scholar 

  6. Al-Khalifa, H.S., Davis, H.C.: Towards better understanding of folksonomic patterns. In: Proceedings of the Eighteenth Conference on Hypertext and Hypermedia, HT 2007, pp. 163–166. ACM, New York (2007)

    Google Scholar 

  7. Amaral, L.A.N., Scala, A., Barthelemy, M., Stanley, H.: Classes of small-world networks. Proc. Nat. Acad. Sci. USA 97, 11149–11152 (2000)

    Article  Google Scholar 

  8. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the WWW 2007, Banff, Canada, pp. 501–510 (2007)

    Google Scholar 

  9. Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  10. Barabási, A.-L., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world-wide web. Phys. A Stat. Mech. Appl. 281(1–4), 69–77 (2000)

    Article  Google Scholar 

  11. Barabasi, A.-L., Bonabeau, E.: Scale-free networks. Sci. Am. 288, 60–69 (2003)

    Article  Google Scholar 

  12. Belém, F.M., Martins, E.F., Almeida, J.M., Gonçalves, M.A.: Personalized and object-centered tag recommendation methods for web 2.0 applications. Inf. Proc. Manag. 50(4), 524–553 (2014)

    Article  Google Scholar 

  13. Benz, D., Eisterlehner, F., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Managing publications and bookmarks with bibsonomy. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT 2009, pp. 323–324. ACM, New York , June 2009

    Google Scholar 

  14. Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The social bookmark and publication management system bibsonomy. VLDB J. 19(6), 849–875 (2010)

    Article  Google Scholar 

  15. Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social semantic query expansion. ACM Trans. Intell. Syst. Technol. (TIST) 4(4), 60 (2013)

    Google Scholar 

  16. Bischoff, K., Firan, C.S., Kadar, C., Nejdl, W., Paiu, R.: Automatically identifying tag types. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) ADMA 2009. LNCS (LNAI), vol. 5678, pp. 31–42. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03348-3_7

    Chapter  Google Scholar 

  17. Bogers, T.: Tag-based recommendation. In: Brusilovsky, P., He, D. (eds.) Social Information Access, LNCS, 10100, pp. 441–479. Springer, Heidelberg (2018)

    Google Scholar 

  18. Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: SoPRa: a new social personalized ranking function for improving web search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 861–864. ACM, New York (2013)

    Google Scholar 

  19. Breitman, K., Casanova, M.A.: Semantic Web: Concepts, Technologies and Applications. Springer-Verlag London Limited, New York (2007). https://doi.org/10.1007/978-1-84628-710-7

    Book  MATH  Google Scholar 

  20. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  21. Brusilovsky, P., He, D.: Introduction to social information access. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 1–18. Springer, Cham (2018)

    Google Scholar 

  22. Brusilovsky, P., Smyth, B., Shapira, B.: Social search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 213–276. Springer, Cham (2018)

    Google Scholar 

  23. Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic grounding of tag relatedness in social bookmarking systems. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 615–631. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88564-1_39

    Chapter  Google Scholar 

  24. Cattuto, C., Loreto, V., Pietronero, L.: Collaborative tagging and semiotic dynamics. CoRR, abs/cs/0605015 (2006)

    Google Scholar 

  25. Cattuto, C., Schmitz, C., Baldassarri, A., Servedio, V.D.P., Loreto, V., Hotho, A., Grahl, M., Stumme, G.: Network properties of folksonomies. AI Commun. J. 20(4), 245–262 (2007). Special Issue on “Network Analysis in Natural Sciences and Engineering”

    MathSciNet  Google Scholar 

  26. Cunha, E., Magno, G., Comarela, G., Almeida, V., Gonçalves, M.A., Benevenuto, F.: Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In: Proceedings of the Workshop on Languages in Social Media, pp. 58–65. Association for Computational Linguistics (2011)

    Google Scholar 

  27. Dellschaft, K., Staab, S.: An epistemic dynamic model for tagging systems. In: Proceedings of the Nineteenth ACM Conference on Hypertext and hypermedia, HT 2008, pp. 71–80. ACM, New York (2008)

    Google Scholar 

  28. Dimitrov, D., Helic, D., Strohmaier, M.: Tag-based navigation and visualization. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 181–212. Springer, Cham (2018)

    Google Scholar 

  29. Djuana, E., Xu, Y., Li, Y., Jøsang, A.: A combined method for mitigating sparsity problem in tag recommendation. In: 47th Hawaii International Conference on System Sciences, HICSS 2014, Waikoloa, HI, USA, 6–9 January 2014, pp. 906–915 (2014)

    Google Scholar 

  30. Doerfel, S., Jäschke, R., Stumme, G.: The role of cores in recommender benchmarking for social bookmarking systems. ACM Trans. Intell. Syst. Technol. 7(3), 40:1–40:33 (2016)

    Article  Google Scholar 

  31. Doerfel, S., Zöller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: Of course we share! testing assumptions about social tagging systems. CoRR, abs/1401.0629 (2014)

    Google Scholar 

  32. Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: What users actually do in a social tagging system: a study of user behavior in bibsonomy. ACM Trans. Web 10(2), 14:1–14:32 (2016)

    Article  Google Scholar 

  33. Dong, X., Chen, X., Guan, Y., Yu, Z., Li, S.: An overview of learning to rank for information retrieval. In: Burgin, M., Chowdhury, M.H., Ham, C.H., Ludwig, S.A., Su, W., Yenduri, S. (eds.) CSIE (3), pp. 600–606. IEEE Computer Society (2009)

    Google Scholar 

  34. Dou, Z., Song, R., Yuan, X., Wen, J.-R.: Are click-through data adequate for learning web search rankings? In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 73–82. ACM, New York (2008)

    Google Scholar 

  35. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM 1999, pp. 251–262. ACM, New York (1999)

    Google Scholar 

  36. Ferragina, P., Piccinno, F., Santoro, R.: On analyzing hashtags in Twitter. In: Ninth International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  37. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  MATH  Google Scholar 

  38. Gao, Y., Wang, M., Zha, Z.J., Shen, J., Li, X., Wu, X.: Visual-textual joint relevance learning for tag-based social image search. IEEE Trans. Image Process. 22(1), 363–376 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  39. Golder, S., Huberman, B.A.: The structure of collaborative tagging systems, August 2005

    Google Scholar 

  40. Golder, S., Huberman, B.A.: The structure of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)

    Article  Google Scholar 

  41. Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32, 198–208 (2006)

    Article  Google Scholar 

  42. Guo, Q., Liu, W., Lin, Y., Lin, H.: Query expansion based on user quality in folksonomy. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 396–405. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35341-3_35

    Chapter  Google Scholar 

  43. Gupta, M., Li, R., Yin, Z., Han, J.: Survey on social tagging techniques. SIGKDD Explor. Newsl. 12, 58–72 (2010)

    Article  Google Scholar 

  44. Halpin, H., Robu, V., Shepard, H.: The dynamics and semantics of collaborative tagging. In: Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW 2006), pp. 211–220 (2006)

    Google Scholar 

  45. Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 211–220. ACM, New York (2007)

    Google Scholar 

  46. Heymann, P., Koutrika, G., Molina, H.: Can social bookmarking improve web search? In: Proceedings of the International Conference on Web Search and Web Data Mining, WSDM 2008, pp. 195–206. ACM, Palo Alto (2008)

    Google Scholar 

  47. Heymans, M.: Introducing Google social search: i finally found my friend’s New York blog! (2009)

    Google Scholar 

  48. Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Trend detection in folksonomies. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds.) SAMT 2006. LNCS, vol. 4306, pp. 56–70. Springer, Heidelberg (2006). https://doi.org/10.1007/11930334_5

    Chapter  Google Scholar 

  49. Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: BibSonomy: a social bookmark and publication sharing system. In: de Moor, A., Polovina, S., Delugach, H. (eds.) Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, Aalborg, Denmark. Aalborg University Press, July 2006

    Google Scholar 

  50. Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006). https://doi.org/10.1007/11762256_31

    Chapter  Google Scholar 

  51. Ignatov, D., Zhuk, R., Konstantinova, N.: Learning hypotheses from triadic labeled data. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 2, pp. 474–480, August 2014

    Google Scholar 

  52. Jin, Y., Li, R., Cai, Y., Li, Q., Daud, A., Li, Y.: Semantic grounding of hybridization for tag recommendation. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds.) semantic grounding of hybridization for tag recommendation. LNCS, vol. 6184, pp. 139–150. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14246-8_16

    Chapter  Google Scholar 

  53. Joachims, T.: Optimizing search engines using clickthrough data. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 133–142 (2002)

    Google Scholar 

  54. Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and evaluating tag recommenders in a live system. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, pp. 369–372. ACM, New York (2009)

    Google Scholar 

  55. Kammerer, Y., Nairn, R., Pirolli, P., Chi, E.H.: Signpost from the masses: learning effects in an exploratory social tag search browser. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 625–634. ACM, New York (2009)

    Google Scholar 

  56. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  57. Knerr, T.: Tagging ontology - towards a common ontology for folksonomies (2006). http://tagont.googlecode.com/files/TagOntPaper.pdf

  58. Kolay, S., Dasdan, A.: The value of socially tagged URLs for a search engine. In: Quemada, J., Leon, G., Maarek, Y.S., Nejdl, W. (eds.) WWW, pp. 1203–1204. ACM (2009)

    Google Scholar 

  59. Krause, B., Hotho, A., Stumme, G.: A comparison of social bookmarking with traditional search. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 101–113. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78646-7_12

    Chapter  Google Scholar 

  60. Krause, B., Jäschke, R., Hotho, A., Stumme, G.: Logsonomy - social information retrieval with logdata. In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, HT 2008, pp. 157–166. ACM, New York (2008)

    Google Scholar 

  61. Kubatz, M., Gedikli, F., Jannach, D.: Localrank - neighborhood-based, fast computation of tag recommendations. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 258–269. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23014-1_22

    Chapter  Google Scholar 

  62. Körner, C., Benz, D., Strohmaier, M., Hotho, A., Stumme, G.: Stop thinking, start tagging - tag semantics emerge from collaborative verbosity. In: Proceedings of the 19th International World Wide Web Conference (WWW 2010). ACM, Raleigh, April 2010

    Google Scholar 

  63. Körner, C., Kern, R., Grahsl, H.-P., Strohmaier, M.: Of categorizers and describers: an evaluation of quantitative measures for tagging motivation. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, HT 2010, pp. 157–166. ACM, New York (2010)

    Google Scholar 

  64. Lancaster, F.W.: Indexing and Abstracting in Theory and Practice. University of Illinois, Chicago, Graduate School of Library and Information Science (2003)

    Google Scholar 

  65. Laniado, D., Mika, P.: Making sense of Twitter. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 470–485. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17746-0_30

    Chapter  Google Scholar 

  66. Lee, K.-P., Kim, H.-G., Kim, H.-J.: A social inverted index for social-tagging-based information retrieval. J. Inf. Sci. 38(4), 313–332 (2012)

    Article  Google Scholar 

  67. Lehmann, F., Wille, R.: A triadic approach to formal concept analysis. In: Ellis, G., Levinson, R., Rich, W., Sowa, J.F. (eds.) ICCS-ConceptStruct 1995. LNCS, vol. 954, pp. 32–43. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60161-9_27

    Chapter  Google Scholar 

  68. Li, P., Nie, J.-Y., Wang, B., He, J.: Document re-ranking using partial social tagging. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 274–281, December 2012

    Google Scholar 

  69. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 675–684. ACM, New York (2008)

    Google Scholar 

  70. Lin, Y., Lin, H., Jin, S., Ye, Z.: Social annotation in query expansion: a machine learning approach. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 405–414. ACM, New York (2011)

    Google Scholar 

  71. Lipczak, M., Milios, E.: The impact of resource title on tags in collaborative tagging systems. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, HT 2010, pp. 179–188. ACM, New York (2010)

    Google Scholar 

  72. Macdonald, C., Ounis, I.: Usefulness of quality click-through data for training. In: Proceedings of the 2009 Workshop on Web Search Click Data, WSCD 2009, pp. 75–79. ACM, New York (2009)

    Google Scholar 

  73. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  MATH  Google Scholar 

  74. Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., Symeonidis, P.: Social tagging recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 615–644. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_19

    Chapter  Google Scholar 

  75. Markines, B., Cattuto, C., Menczer, F.: Social spam detection. In: Fetterly, D., Gyöngyi, Z. (eds.) Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web AIRWeb, ACM International Conference Proceeding Series, pp. 41–48 (2009)

    Google Scholar 

  76. Marlow, C., Naaman, M., Boyd, D., Davis, M.: HT06, tagging paper, taxonomy, Flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, pp. 31–40. ACM (2006)

    Google Scholar 

  77. Mathes, A.: Folksonomies-Cooperative Classification and Communication Through Shared Metadata, Computer Mediated Communication, LIS590CMC (Doctoral Seminar). Graduate School of Library and Information Science, University of Illinois Urbana-Champaign, December 2004

    Google Scholar 

  78. Mika, P.: Ontologies are us: a unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005). https://doi.org/10.1007/11574620_38

    Chapter  Google Scholar 

  79. Morrison, P.J.: Tagging and searching: search retrieval effectiveness of folksonomies on the world wide web. Inf. Process. Manag. 44, 1562–1579 (2008)

    Article  Google Scholar 

  80. Navarro Bullock, B., Jäschke, R., Hotho, A.: Tagging data as implicit feedback for learning-to-rank. In: Proceedings of the ACM WebSci 2011, June 2011

    Google Scholar 

  81. Neubauer, N., Obermayer, K.: Hyperincident connected components of tagging networks. SIGWEB Newsl. 4:1–4:10 (2009)

    Google Scholar 

  82. Neubauer, N., Wetzker, R., Obermayer, K.: Tag spam creates large non-giant connected components. In: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web, AIRWeb 2009, pp. 49–52. ACM, New York (2009)

    Google Scholar 

  83. Newman, M.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)

    Article  Google Scholar 

  84. Niebler, T., Becker, M., Zoller, D., Doerfel, S., Hotho, A.: FolkTrails: interpreting navigation behavior in a social tagging system. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016. ACM, New York (2016)

    Google Scholar 

  85. Noll, M.G.: Understanding and leveraging the social web for information retrieval. Ph.D. thesis, Universität Potsdam, April 2010

    Google Scholar 

  86. O’Reilly, T.: What is web 2.0. design patterns and business models for the next generation of software, September 2005. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html,. Stand 12.5.2009

  87. Overell, S., Sigurbjörnsson, B., van Zwol, R.: Classifying tags using open content resources. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM 2009, pp. 64–73. ACM, New York (2009)

    Google Scholar 

  88. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report 1999–66, Stanford InfoLab, November 1999

    Google Scholar 

  89. Papadopoulos, S., Kompatsiaris, Y., Vakali, A.: A graph-based clustering scheme for identifying related tags in folksonomies. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2010. LNCS, vol. 6263, pp. 65–76. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15105-7_6

    Chapter  Google Scholar 

  90. Papadopoulos, S., Vakali, A., Kompatsiaris, Y.: Community detection in collaborative tagging systems. In: Pardede, E. (ed.) Community-Built Databases, pp. 107–131. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19047-6_5

    Chapter  Google Scholar 

  91. Peng, J., Zeng, D.D., Zhao, H., Wang, F.-Y.: Collaborative filtering in social tagging systems based on joint item-tag recommendations. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 809–818. ACM, New York (2010)

    Google Scholar 

  92. Petersen, C., Simonsen, J.G., Lioma, C.: Power law distributions in information retrieval. ACM Trans. Inf. Syst. 34(2), 8:1–8:37 (2016)

    Article  Google Scholar 

  93. Quintarelli, E.: Folksonomies: power to the people, June 2005

    Google Scholar 

  94. Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Davison, B.D., Suel, T., Craswell, N., Liu, B. (eds.) WSDM, pp. 81–90. ACM (2010)

    Google Scholar 

  95. Sen, S., Lam, S.K., Rashid, A.M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F.M., Riedl, J.: Tagging, communities, vocabulary, evolution. In: Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work, CSCW 2006, pp. 181–190. ACM, New York (2006)

    Google Scholar 

  96. Spiteri, L.: Structure and form of folksonomy tags: the road to the public library catalogue. Webology 4(2) (2007)

    Google Scholar 

  97. Stumme, G.: A finite state model for on-line analytical processing in triadic contexts. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 315–328. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32262-7_22

    Chapter  Google Scholar 

  98. Teevan, J., Ramage, D., Morris, M.R.: # TwitterSearch: a comparison of microblog search and web search. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 35–44. ACM (2011)

    Google Scholar 

  99. Vallet, D., Cantador, I., Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12275-0_37

    Chapter  Google Scholar 

  100. von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2004, pp. 319–326. ACM, New York (2004)

    Google Scholar 

  101. Wagner, C., Strohmaier, M.: The wisdom in tweetonomies: acquiring latent conceptual structures from social awareness streams. In: Proceedings of the Semantic Search 2010 Workshop (SemSearch 2010), April 2010

    Google Scholar 

  102. Wal, T.V.: Explaining and showing broad and narrow folksonomies. Blog post, February 2005

    Google Scholar 

  103. Wartena, C.: Automatic classification of social tags. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 176–183. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15464-5_19

    Chapter  Google Scholar 

  104. Watts, D.J., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  MATH  Google Scholar 

  105. Wetzker, R., Zimmermann, C., Bauckhage, C.: Analyzing social bookmarking systems: a del.icio.us cookbook. In: Mining Social Data (MSoDa) Workshop Proceedings, ECAI 2008, pp. 26–30, July 2008

    Google Scholar 

  106. Wetzker, R., Zimmermann, C., Bauckhage, C.: Detecting trends in social bookmarking systems: a del. icio. us endeavor. In: Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends, pp. 34–51 (2011)

    Google Scholar 

  107. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered sets. NATO ASIC, vol. 83, pp. 445–470. Springer, Dordrecht (1982). https://doi.org/10.1007/978-94-009-7798-3_15

    Chapter  Google Scholar 

  108. Willinge, W., Alderson, D., Doyle, J.C.: Mathematics and the internet: a source of enormous confusion and great potential. Technical report 5, May 2009

    Google Scholar 

  109. Wu, H., Zubair, M., Maly, K.: Harvesting social knowledge from folksonomies. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, HYPERTEXT 2006, pp. 111–114. ACM, New York (2006)

    Google Scholar 

  110. Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 417–426. ACM Press, New York (2006)

    Google Scholar 

  111. Yazdani, S., Ivanov, I., AnaLoui, M., Berangi, R., Ebrahimi, T.: Spam fighting in social tagging systems. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 448–461. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35386-4_33

    Chapter  Google Scholar 

  112. Yeung, C.M.A.: From user behaviours to collective semantics. Ph.D. thesis, University of Southampton (2009)

    Google Scholar 

  113. Yin, D., Hong, L., Davison, B.D.: Exploiting session-like behaviors in tag prediction. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 167–168. ACM, New York (2011)

    Google Scholar 

  114. Yue, Z., He, D.: Collaborative information search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 108–141. Springer, Cham (2018)

    Google Scholar 

  115. Zhou, D., Lawless, S., Wade, V.: Improving search via personalized query expansion using social media. Inf. Retr. 15(3–4), 218–242 (2012)

    Article  Google Scholar 

  116. Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: On publication usage in a social bookmarking system. In: Proceedings of the 2015 ACM Conference on Web Science, WebSci 2015, pp. 67:1–67:2. ACM, New York (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DMIR Research Group at the Computer Science Institute, University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    Beate Navarro Bullock & Andreas Hotho

  2. Knowledge and Data Engineering Group, Research Center for Information System Design, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Gerd Stumme

Authors
  1. Beate Navarro Bullock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Hotho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerd Stumme
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerd Stumme .

Editor information

Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Navarro Bullock, B., Hotho, A., Stumme, G. (2018). Accessing Information with Tags: Search and Ranking. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90092-6_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90091-9

  • Online ISBN: 978-3-319-90092-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation