Difference between revisions of "Extracting Semantics from Unconstrained Navigation on Wikipedia"
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| + | | title = Extracting Semantics from Unconstrained Navigation on Wikipedia | ||
| + | | date = 2016 | ||
| + | | authors = [[Thomas Niebler]]<br />[[Daniel Schlör]]<br />[[Martin Becker]]<br />[[Andreas Hotho]] | ||
| + | | doi = 10.1007/s13218-015-0417-5 | ||
| + | | link = https://link.springer.com/content/pdf/10.1007%2Fs13218-015-0417-5.pdf | ||
| + | }} | ||
'''Extracting Semantics from Unconstrained Navigation on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Thomas Niebler]], [[Daniel Schlör]], [[Martin Becker]] and [[Andreas Hotho]]. | '''Extracting Semantics from Unconstrained Navigation on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Thomas Niebler]], [[Daniel Schlör]], [[Martin Becker]] and [[Andreas Hotho]]. | ||
== Overview == | == Overview == | ||
Semantic [[relatedness]] between words has been successfully extracted from navigation on [[Wikipedia]] pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, authors raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, authors first highlight structural differences between unconstrained navigation and game data. Then, authors adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. Authors apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. Authors confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, authors are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, authors show that unconstrained navigation data on Wikipedia is suited for extracting semantics. | Semantic [[relatedness]] between words has been successfully extracted from navigation on [[Wikipedia]] pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, authors raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, authors first highlight structural differences between unconstrained navigation and game data. Then, authors adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. Authors apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. Authors confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, authors are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, authors show that unconstrained navigation data on Wikipedia is suited for extracting semantics. | ||
Revision as of 18:27, 11 May 2020
Authors | Thomas Niebler Daniel Schlör Martin Becker Andreas Hotho |
|---|---|
Publication date | 2016 |
DOI | 10.1007/s13218-015-0417-5 |
Links | Original |
Extracting Semantics from Unconstrained Navigation on Wikipedia - scientific work related to Wikipedia quality published in 2016, written by Thomas Niebler, Daniel Schlör, Martin Becker and Andreas Hotho.
Overview
Semantic relatedness between words has been successfully extracted from navigation on Wikipedia pages. However, the navigational data used in the corresponding works are sparse and expected to be biased since they have been collected in the context of games. In this paper, authors raise this limitation and explore if semantic relatedness can also be extracted from unconstrained navigation. To this end, authors first highlight structural differences between unconstrained navigation and game data. Then, authors adapt a state of the art approach to extract semantic relatedness on Wikipedia paths. Authors apply this approach to transitions derived from two unconstrained navigation datasets as well as transitions from WikiGame and compare the results based on two common gold standards. Authors confirm expected structural differences when comparing unconstrained navigation with the paths collected by WikiGame. In line with this result, the mentioned state of the art approach for semantic extraction on navigation data does not yield good results for unconstrained navigation. Yet, authors are able to derive a relatedness measure that performs well on both unconstrained navigation data as well as game data. Overall, authors show that unconstrained navigation data on Wikipedia is suited for extracting semantics.
