part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - BOOK AU - Manning, Christopher D. AU - Raghavan, Prabhakar AU - Schütze, Hinrich A2 - T1 - Introduction to Information Retrieval PB - Cambridge University Press AD - New York PY - 2008/ VL - IS - SP - EP - UR - http://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr_1_1?ie=UTF8&qid=1337379279&sr=8-1 M3 - KW - book KW - citedBy:doerfel2012leveraging KW - information KW - introduction KW - ir KW - retrieval L1 - SN - 9780521865715 0521865719 N1 - N1 - AB - "Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." -- Publisher's description. ER - TY - JOUR AU - Peters, Isabella AU - Stock, Wolfgang G. T1 - Folksonomies in Wissensrepräsentation und Information Retrieval JO - Information -- Wissenschaft und Praxis PY - 2008/ VL - 59 IS - 2 SP - 77 EP - 90 UR - http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/1/1204547968stock212_h.htm M3 - KW - folksonomy KW - information KW - ir KW - retrieval KW - wissensrepräsentation KW - übersicht L1 - SN - N1 - N1 - AB - Folksonomies in Wissensrepräsentation und Information Retrieval. Die populären Web 2.0-Dienste werden von Prosumern -- Produzenten und gleichsam Konsumenten -- nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente (glqqmore like this!grqq) erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities (glqqmore like me!grqq). Folksonomies in Knowledge Representation and Information Retrieval In Web 2.0 services grqqprosumers” -- 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, grqqtagsgrqq, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags’ linguistic problems. Additionally, we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). Besides recommending similar documents (glqqmore like this!grqq) folksonomies can be used for the recommendation of similar users and communities (glqqmore like me!grqq). ER -