Accessing Information with Tags: Search and Ranking

  • Beate Navarro Bullock
  • Andreas Hotho
  • Gerd StummeEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10100)


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.


Social Bookmarking Systems Tagging System Hyperlink-Induced Topic Search (HITS) BibSonomy SocialPageRank (SPR) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.DMIR Research Group at the Computer Science InstituteUniversity of WürzburgWürzburgGermany
  2. 2.Knowledge and Data Engineering Group, Research Center for Information System DesignUniversity of KasselKasselGermany

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