Wikipedia Infoboxes are semi-structured data structures organized in an attribute-value fashion. Policies establish for each type of entity represented in Wikipedia the attribute names that the Infobox should contain in the form of a template. However, these requirements change over time and often users choose not to strictly obey them. As a result, it is hard to treat in an integrated way the history of the Wikipedia pages, making it difficult to analyze the temporal evolution of Wikipedia entities through their Infobox and impossible to perform direct comparison of entities of the same type. To address this challenge, we propose an approach to deal with the misalignment of the attribute names and identify clusters of synonymous Infobox attributes. Elements in the same cluster are considered as a temporal evolution of the same attribute. To identify the clusters we use two different distance metrics. The first is the co-occurrence degree that is treated as a negative distance, and the second is the co-occurrence of similar values in the attributes that are treated as a positive evidence of synonymy. We formalize the problem as a correlation clustering problem over a weighted graph constructed with attributes as nodes and positive and negative evidence as edges. We solve it with a linear programming model that shows a good approximation. Our experiments over a collection of Infoboxes of the last 13 years shows the potential of our approach.

Finding Synonymous Attributes in Evolving Wikipedia Infoboxes / Sottovia, Paolo; Paganelli, Matteo; Guerra, Francesco; Velegrakis, Yannis. - 11695(2019), pp. 169-185. ((Intervento presentato al convegno 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019 tenutosi a Bled nel 8-11 September 2019 [10.1007/978-3-030-28730-6_11].

Finding Synonymous Attributes in Evolving Wikipedia Infoboxes

Sottovia, Paolo;Paganelli, Matteo;Guerra, Francesco;
2019

Abstract

Wikipedia Infoboxes are semi-structured data structures organized in an attribute-value fashion. Policies establish for each type of entity represented in Wikipedia the attribute names that the Infobox should contain in the form of a template. However, these requirements change over time and often users choose not to strictly obey them. As a result, it is hard to treat in an integrated way the history of the Wikipedia pages, making it difficult to analyze the temporal evolution of Wikipedia entities through their Infobox and impossible to perform direct comparison of entities of the same type. To address this challenge, we propose an approach to deal with the misalignment of the attribute names and identify clusters of synonymous Infobox attributes. Elements in the same cluster are considered as a temporal evolution of the same attribute. To identify the clusters we use two different distance metrics. The first is the co-occurrence degree that is treated as a negative distance, and the second is the co-occurrence of similar values in the attributes that are treated as a positive evidence of synonymy. We formalize the problem as a correlation clustering problem over a weighted graph constructed with attributes as nodes and positive and negative evidence as edges. We solve it with a linear programming model that shows a good approximation. Our experiments over a collection of Infoboxes of the last 13 years shows the potential of our approach.
23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019
Bled
8-11 September 2019
11695
169
185
Sottovia, Paolo; Paganelli, Matteo; Guerra, Francesco; Velegrakis, Yannis
Finding Synonymous Attributes in Evolving Wikipedia Infoboxes / Sottovia, Paolo; Paganelli, Matteo; Guerra, Francesco; Velegrakis, Yannis. - 11695(2019), pp. 169-185. ((Intervento presentato al convegno 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019 tenutosi a Bled nel 8-11 September 2019 [10.1007/978-3-030-28730-6_11].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1179971
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