This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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.
%0 Conference Paper
%1 singer2013computing_www
%A Singer, Philipp
%A Niebler, Thomas
%A Strohmaier, Markus
%A Hotho, Andreas
%B Proceedings of the 22nd International Conference on World Wide Web
%D 2013
%E Carr, Leslie
%E Laender, Alberto H. F.
%E Lóscio, Bernadette Farias
%E King, Irwin
%E Fontoura, Marcus
%E Vrandecic, Denny
%E Aroyo, Lora
%E de Oliveira, José Palazzo M.
%E Lima, Fernanda
%E Wilde, Erik
%I ACM
%K extraction myown published semantics solvatio wikigame wikipedia
%P 171--172
%T Computing Semantic Relatedness from Human Navigational Paths on Wikipedia
%U http://dl.acm.org/citation.cfm?id=2487788.2487873
%X This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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.
@inproceedings{singer2013computing_www,
abstract = {This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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.},
added-at = {2017-07-10T21:17:58.000+0200},
author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas},
author+an = {2=highlight},
biburl = {https://www.bibsonomy.org/bibtex/2f3116d105cbb150651b3c873614cbd66/thoni},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
editor = {Carr, Leslie and Laender, Alberto H. F. and Lóscio, Bernadette Farias and King, Irwin and Fontoura, Marcus and Vrandecic, Denny and Aroyo, Lora and de Oliveira, José Palazzo M. and Lima, Fernanda and Wilde, Erik},
interhash = {c8651bff5f9f8130c8660f979941df42},
intrahash = {f3116d105cbb150651b3c873614cbd66},
keywords = {extraction myown published semantics solvatio wikigame wikipedia},
organization = {ACM},
pages = {171--172},
publisher = {ACM},
timestamp = {2018-12-29T12:22:59.000+0100},
title = {Computing Semantic Relatedness from Human Navigational Paths on Wikipedia},
url = {http://dl.acm.org/citation.cfm?id=2487788.2487873},
year = 2013
}