Difference between revisions of "Heuristics for Semantic Path Search in Wikipedia"
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| + | | title = Heuristics for Semantic Path Search in Wikipedia | ||
| + | | date = 2014 | ||
| + | | authors = [[Valentina Franzoni]]<br />[[Marco Mencacci]]<br />[[Paolo Mengoni]]<br />[[Alfredo Milani]] | ||
| + | | doi = 10.1007/978-3-319-09153-2_25 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=2723218.2723244 | ||
| + | }} | ||
'''Heuristics for Semantic Path Search in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Valentina Franzoni]], [[Marco Mencacci]], [[Paolo Mengoni]] and [[Alfredo Milani]]. | '''Heuristics for Semantic Path Search in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Valentina Franzoni]], [[Marco Mencacci]], [[Paolo Mengoni]] and [[Alfredo Milani]]. | ||
== Overview == | == Overview == | ||
In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity [[measures]] among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of [[Wikipedia]], encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant [[features]] of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation. | In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity [[measures]] among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of [[Wikipedia]], encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant [[features]] of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation. | ||
Revision as of 22:47, 24 September 2020
Authors | Valentina Franzoni Marco Mencacci Paolo Mengoni Alfredo Milani |
|---|---|
Publication date | 2014 |
DOI | 10.1007/978-3-319-09153-2_25 |
Links | Original |
Heuristics for Semantic Path Search in Wikipedia - scientific work related to Wikipedia quality published in 2014, written by Valentina Franzoni, Marco Mencacci, Paolo Mengoni and Alfredo Milani.
Overview
In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity measures among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of Wikipedia, encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant features of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation.
