an article by Stefanie Haustein (Central Library at Forschungszentrum Jülich, Germany, and Heinrich Heine University Düsseldorf, Germany) and Isabella Peters (Heinrich Heine University Düsseldorf, Germany) published in First Monday Volume 17 Number 11 (November 2012)
Abstract
Qualitative journal evaluation cumulates content descriptions of single articles. Articles are either represented by author-generated keywords, professionally indexed subject headings, automatically extracted terms or, as recently introduced, by reader–generated tags as used in social bookmarking systems.
The study presented here shows that different types of keywords each reflect a different perspective on documents and that tags can be used in journal evaluation to represent a reader-specific view.
After providing a broad theoretical background and literature review, methods for extensive automatic term cleaning and calculation of term overlaps are introduced. The efficiency of tags and other metadata for journal content description is illustrated for one particular journal.
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Friday, 16 November 2012
Monday, 20 August 2012
A social inverted index for social-tagging-based information retrieval
an article by Kang-Pyo Lee, Hong-Gee Kim and Hyoung-Joo Kim (Seoul National University, South Korea) published in Journal of Information Science Volume 38 Number 4 (August 2012)
Abstract
Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query.
Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines.
One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document.
In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights.
The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights.
It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists.
The time and space overhead required for index construction and maintenance was also acceptable.
Abstract
Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query.
Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines.
One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document.
In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights.
The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights.
It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists.
The time and space overhead required for index construction and maintenance was also acceptable.
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