Difference between revisions of "Updating Wikipedia via Dbpedia Mappings and Sparql"
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| − | '''Updating Wikipedia via Dbpedia Mappings and Sparql''' - scientific work related to Wikipedia quality published in 2017, written by Albin Ahmeti, Javier D. Fernández, Axel Polleres and Vadim Savenkov. | + | '''Updating Wikipedia via Dbpedia Mappings and Sparql''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Albin Ahmeti]], [[Javier D. Fernández]], [[Axel Polleres]] and [[Vadim Savenkov]]. |
== Overview == | == Overview == | ||
| − | DBpedia crystallized most of the concepts of the Semantic Web using simple mappings to convert Wikipedia articles (i.e., infoboxes and tables) to RDF data. This “semantic view” of wiki content has rapidly become the focal point of the Linked Open Data cloud, but its impact on the original Wikipedia source is limited. In particular, little attention has been paid to the benefits that the semantic infrastructure can bring to maintain the wiki content, for instance to ensure that the effects of a wiki edit are consistent across infoboxes. In this paper, authors present an approach to allow ontology-based updates of wiki content. Starting from DBpedia-like mappings converting infoboxes to a fragment of OWL 2 RL ontology, authors discuss various issues associated with translating SPARQL updates on top of semantic data to the underlying Wiki content. On the one hand, authors provide a formalization of DBpedia as an Ontology-Based Data Management framework and study its computational properties. On the other hand, authors provide a novel approach to the inherently intractable update translation problem, leveraging the pre-existent data for disambiguating updates. | + | DBpedia crystallized most of the concepts of the Semantic Web using simple mappings to convert [[Wikipedia]] articles (i.e., [[infoboxes]] and tables) to RDF data. This “semantic view” of wiki content has rapidly become the focal point of the Linked Open Data cloud, but its impact on the original Wikipedia source is limited. In particular, little attention has been paid to the benefits that the semantic infrastructure can bring to maintain the wiki content, for instance to ensure that the effects of a wiki edit are consistent across infoboxes. In this paper, authors present an approach to allow [[ontology]]-based updates of wiki content. Starting from [[DBpedia]]-like mappings converting infoboxes to a fragment of OWL 2 RL ontology, authors discuss various issues associated with translating SPARQL updates on top of semantic data to the underlying Wiki content. On the one hand, authors provide a formalization of DBpedia as an Ontology-Based Data Management framework and study its computational properties. On the other hand, authors provide a novel approach to the inherently intractable update translation problem, leveraging the pre-existent data for disambiguating updates. |
Revision as of 08:21, 6 May 2020
Updating Wikipedia via Dbpedia Mappings and Sparql - scientific work related to Wikipedia quality published in 2017, written by Albin Ahmeti, Javier D. Fernández, Axel Polleres and Vadim Savenkov.
Overview
DBpedia crystallized most of the concepts of the Semantic Web using simple mappings to convert Wikipedia articles (i.e., infoboxes and tables) to RDF data. This “semantic view” of wiki content has rapidly become the focal point of the Linked Open Data cloud, but its impact on the original Wikipedia source is limited. In particular, little attention has been paid to the benefits that the semantic infrastructure can bring to maintain the wiki content, for instance to ensure that the effects of a wiki edit are consistent across infoboxes. In this paper, authors present an approach to allow ontology-based updates of wiki content. Starting from DBpedia-like mappings converting infoboxes to a fragment of OWL 2 RL ontology, authors discuss various issues associated with translating SPARQL updates on top of semantic data to the underlying Wiki content. On the one hand, authors provide a formalization of DBpedia as an Ontology-Based Data Management framework and study its computational properties. On the other hand, authors provide a novel approach to the inherently intractable update translation problem, leveraging the pre-existent data for disambiguating updates.
