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Efficient assembly of social semantic networks
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Conference on Hypertext and Hypermedia archive
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
Authors
Benjamin Markines  Indiana University, Bloomington, IN, USA and Institute for Scientific Interchange Foundation, Torino, Italy
Heather Roinestad  Indiana University, Bloomington, IN, USA
Filippo Menczer  Indiana University, Bloomington, IN, USA and Institute for Scientific Interchange Foundation, Torino, Italy
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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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Collaborative Colleagues:
Benjamin Markines: colleagues
Heather Roinestad: colleagues
Filippo Menczer: colleagues