Difference between revisions of "Exploring Semantically-Related Concepts from Wikipedia: the Case of Sere"
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| − | '''Exploring Semantically-Related Concepts from Wikipedia: the Case of Sere''' - scientific work related to Wikipedia quality published in 2015, written by Daniel Hienert, Dennis Wegener and Siegfried Schomisch. | + | '''Exploring Semantically-Related Concepts from Wikipedia: the Case of Sere''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Daniel Hienert]], [[Dennis Wegener]] and [[Siegfried Schomisch]]. |
== Overview == | == Overview == | ||
| − | In this paper authors present web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. Authors use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study authors examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering. | + | In this paper authors present web application SeRE designed to explore semantically related concepts. [[Wikipedia]] and [[DBpedia]] are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. Authors use the Wikipedia full text body to compute the semantic [[relatedness]] for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study authors examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering. |
Revision as of 11:52, 12 December 2019
Exploring Semantically-Related Concepts from Wikipedia: the Case of Sere - scientific work related to Wikipedia quality published in 2015, written by Daniel Hienert, Dennis Wegener and Siegfried Schomisch.
Overview
In this paper authors present web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. Authors use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study authors examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering.
