%0 Report %1 DierickDopichajEtAl08 %A Dierick, Francis %A Dopichaj, Philipp %A Heß, Uwe Fleischer Andreas %A Skusa, Andre %A Maaß, Christian %C Gütersloh %D 2008 %K v1205 paper ai semantic web information retrieval search language text knowledge processing ontology zzz.th %T From Content to Knowledge: Applying Wikipedia Extraction and User Contribution to Generating Relation Annotations %X Extracting and annotating relations from free text and incorporating the gained knowledge into an ontology are well-known challenges in semantic web and information extraction research. In this context we present an approach that comprises the actual relation extraction from text, methods for validating the extracted relations by automated web searches and user games as well as the presentation of the obtained knowledge within a community web application. Specically, we mine Wikipedia articles on German history, from which we extract relations between persons and qualify the relation type (e. g., married or colleagues ). The extracted relations and their annotations are then validated both automatically (i. e., evaluating search engine snippets from automatically created searches) and interactively (i. e., users are invited to play games where the assumed relations are tested). In general, the presented approach is designed to be applied to arbitrary domains and relationship types, with minor changes. Finally ways to extend the user experience on navigating as well as using knowledge related webpages with the extracted and validated semantic information are shown. The broader scope of this work is to establish methodologies that allow to incorporate user knowledge and interaction into ontology creation and maintenance.

@techreport{DierickDopichajEtAl08, abstract = {Extracting and annotating relations from free text and incorporating the gained knowledge into an ontology are well-known challenges in semantic web and information extraction research. In this context we present an approach that comprises the actual relation extraction from text, methods for validating the extracted relations by automated web searches and user games as well as the presentation of the obtained knowledge within a community web application. Specically, we mine Wikipedia articles on German history, from which we extract relations between persons and qualify the relation type (e. g., married or colleagues ). The extracted relations and their annotations are then validated both automatically (i. e., evaluating search engine snippets from automatically created searches) and interactively (i. e., users are invited to play games where the assumed relations are tested). In general, the presented approach is designed to be applied to arbitrary domains and relationship types, with minor changes. Finally ways to extend the user experience on navigating as well as using knowledge related webpages with the extracted and validated semantic information are shown. The broader scope of this work is to establish methodologies that allow to incorporate user knowledge and interaction into ontology creation and maintenance.}, added-at = {2012-05-30T10:45:05.000+0200}, address = {G\"{u}tersloh}, author = {Dierick, Francis and Dopichaj, Philipp and He{\ss}, Uwe Fleischer Andreas and Skusa, Andre and Maa{\ss}, Christian}, biburl = {https://www.bibsonomy.org/bibtex/2df6aada43c84ebb0c6a79bc772167e1c/flint63}, file = {Theseus Intranet ALEXANDRIA:2008/DierickDopichajEtAl08.pdf:PDF}, groups = {public}, institution = {Lycos Europe}, interhash = {964140c4df6c76fe97bc372ba188e576}, intrahash = {df6aada43c84ebb0c6a79bc772167e1c}, keywords = {v1205 paper ai semantic web information retrieval search language text knowledge processing ontology zzz.th}, timestamp = {2018-04-16T11:59:41.000+0200}, title = {From Content to Knowledge: Applying {Wikipedia} Extraction and User Contribution to Generating Relation Annotations}, type = {White Paper}, username = {flint63}, year = 2008 }