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“Big Data, Linked Data: Classification Research at the Junction.”
Workshop Details: http://www.asis.org/asist2013/seminars_workshops_SIG_CR.html
Registration Information: http://www.asis.org/asist2013/register.html
While big data and linked data are often considered separately, classification research stands at
the juncture between these two approaches, and can therefore provide a context in which
researchers in each domain can benefit from the insights of the other. This workshop will put both young and established researchers in classification theory and practice to work on exploring this juncture, on topics ranging from Twitter to gene ontology, and from WorldCat to the Semantic Web.
This workshop forms part of the annual meeting of the ASIS&T Annual Meeting at Le Centre Sheraton, Montreal, Quebec, Canada.
List of Presentations:
Rick Szostak, University of Alberta
Classification, Ontology and the Semantic Web
Shuheng Wu, Besiki Stvilia, Florida State University
Exploring Development and Maintenance Practices in Gene Ontology
Tatiana Lukoianova, Victoria Rubin, Western University
Is Big Data Objective or Truthful?
Rebecca Green, Michael Panzer, OCLC
The Interplay of Big Data, Worldcat and Dewey
R. P. Smiraglia, University of Wisconsin, Milwaukee
Big Classification: Using the Empirical Power of Classification Interaction
Kwan Yi, Eastern Kentucky University
A Semantic Similarity Approach for Linking Tweet Messages to Library of Congress Subject Headings Using Linked Resources: A Pilot Study
Jane Greenberg, University of North Carolina, Chapel Hill
Metadata Capital via a Linked Data HIVE
Patrick Keilty and Periklis Andritsos, University of Toronto
Level-Wise Exploration of Linked and Big Data Guided by Subject Classification
Inkyung Choi and Hur-Yi Lee, University of Wisconsin, Milwaukee
Toward Social Contextualization of Big Data and Linked Data
Ali Shiri, University of Alberta
Linked Data Meets Big Data: a Knowledge Organization Systems Perspective
Lynne C. Howarth, University of Toronto
Warrant as Theoretical Vocabulary for Reconciling Big Data and Linked Data