| Efficient assembly of social semantic networks |
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Conference on Hypertext and Hypermedia
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Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
table of contents
Pittsburgh, PA, USA
SESSION: Social linking III: similarity and retrieval
table of contents
Pages 149-156
Year of Publication: 2008
ISBN:978-1-59593-985-2
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Authors
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Benjamin Markines
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Indiana University, Bloomington, IN, USA and Institute for Scientific Interchange Foundation, Torino, Italy
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Heather Roinestad
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Indiana University, Bloomington, IN, USA
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Filippo Menczer
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Indiana University, Bloomington, IN, USA and Institute for Scientific Interchange Foundation, Torino, Italy
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Downloads (6 Weeks): 19, Downloads (12 Months): 188, Citation Count: 2
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ABSTRACT
Social bookmarks allow Web users to actively annotate individual Web resources. Researchers are exploring the use of these annotations to create implicit links between online resources. We define an implicit link as a relationship between two online resources established by the Web community. An individual may create or reinforce a relationship between two resources by applying a common tag or organizing them in a common folder. This has led to the exploration of techniques for building networks of resources, categories, and people using the social annotations. In order for these techniques to move from the lab to the real world, efficient building and maintenance of these potentially large networks remains a major obstacle. Methods for assembling and indexing these large networks will allow researchers to run more rigorous assessments of their proposed techniques. Toward this goal we explore an approach from the sparse matrix literature and apply it to our system, GiveALink.org. We also investigate distributing the assembly, allowing us to grow the network with the body of resources, annotations, and users. Dividing the network is effective for assembling a global network where the implicit links are dependent on global properties. Additionally, we explore alternative implicit link measures that remove global dependencies and thus allow for the global network to be assembled incrementally, as each participant makes independent contributions. Finally we evaluate three scalable similarity measures, two of which require a revision of the data model underlying our social annotations.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 2
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Benjamin Markines , Ciro Cattuto , Filippo Menczer , Dominik Benz , Andreas Hotho , Gerd Stumme, Evaluating similarity measures for emergent semantics of social tagging, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain
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INDEX TERMS
Primary Classification:
H.
Information Systems
H.1
MODELS AND PRINCIPLES
H.1.2
User/Machine Systems
Subjects:
Human information processing
Additional Classification:
H.
Information Systems
H.3
INFORMATION STORAGE AND RETRIEVAL
H.3.4
Systems and Software
H.5
INFORMATION INTERFACES AND PRESENTATION (I.7)
H.5.3
Group and Organization Interfaces
Subjects:
Web-based interaction;
Computer-supported cooperative work;
Evaluation/methodology
General Terms:
Algorithms,
Design,
Experimentation,
Performance
Keywords:
aggregation,
annotation,
collaborative filtering,
folksonomy,
resource,
similarity,
tag,
web 2.0
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