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Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia

Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia

Philipp Singer, Thomas Niebler, Markus Strohmaier, Andreas Hotho
Copyright: © 2013 |Volume: 9 |Issue: 4 |Pages: 30
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466634923|DOI: 10.4018/ijswis.2013100103
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MLA

Singer, Philipp, et al. "Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia." IJSWIS vol.9, no.4 2013: pp.41-70. http://doi.org/10.4018/ijswis.2013100103

APA

Singer, P., Niebler, T., Strohmaier, M., & Hotho, A. (2013). Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia. International Journal on Semantic Web and Information Systems (IJSWIS), 9(4), 41-70. http://doi.org/10.4018/ijswis.2013100103

Chicago

Singer, Philipp, et al. "Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia," International Journal on Semantic Web and Information Systems (IJSWIS) 9, no.4: 41-70. http://doi.org/10.4018/ijswis.2013100103

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Abstract

In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.

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