%0 Book Section %1 XuUszkoreitEtAl14p289 %A Xu, Feiyu %A Uszkoreit, Hans %A Li, Hong %A Adolphs, Peter %A Cheng, Xiwen %B Towards the Internet of Services: The THESEUS Research Program %C Berlin %D 2014 %E Wahlster, Wolfgang %E Grallert, Hans-Joachim %E Wess, Stefan %E Friedrich, Hermann %E Widenka, Thomas %I Springer %K v1500 springer paper ai dfki semantic web language knowledge processing information retrieval rules adaptive analysis zzz.th zzz.sw %P 289-297 %R 10.1007/978-3-319-06755-1_22 %T Domain-Adaptive Relation Extraction for the Semantic Web %X In the THESEUS Alexandria use case, information extraction (IE) has been intensively applied to extract facts automatically from unstructured documents, such as Wikipedia and online news, in order to construct ontology-based knowledge databases for advanced information access. In addition, IE is also utilized for analyzing natural language queries for the Alexandria question answering system. The DARE system, a minimally supervised machine learning system for relation extraction, developed at the DFKI LT-Lab, has been adapted and extended to the IE tasks for Alexandria. DARE is domain-adaptive and has been used to learn relation extraction rules automatically for the Alexandria-relevant relations and events. Furthermore, DARE is also applied to the Alexandria opinion mining task for detecting opinion sources, targets and polarities in online news. The DARE system and its learned rules have been integrated into the Alexandria IE pipeline.

@incollection{XuUszkoreitEtAl14p289, abstract = {In the THESEUS Alexandria use case, information extraction (IE) has been intensively applied to extract facts automatically from unstructured documents, such as Wikipedia and online news, in order to construct ontology-based knowledge databases for advanced information access. In addition, IE is also utilized for analyzing natural language queries for the Alexandria question answering system. The DARE system, a minimally supervised machine learning system for relation extraction, developed at the DFKI LT-Lab, has been adapted and extended to the IE tasks for Alexandria. DARE is domain-adaptive and has been used to learn relation extraction rules automatically for the Alexandria-relevant relations and events. Furthermore, DARE is also applied to the Alexandria opinion mining task for detecting opinion sources, targets and polarities in online news. The DARE system and its learned rules have been integrated into the Alexandria IE pipeline.}, added-at = {2015-01-15T08:41:38.000+0100}, address = {Berlin}, author = {Xu, Feiyu and Uszkoreit, Hans and Li, Hong and Adolphs, Peter and Cheng, Xiwen}, biburl = {https://www.bibsonomy.org/bibtex/2c4d2edc7629bc65513c892c783601a78/flint63}, booktitle = {Towards the Internet of Services: The {THESEUS} Research Program}, crossref = {WahlsterGrallertEtAl2014}, doi = {10.1007/978-3-319-06755-1_22}, editor = {Wahlster, Wolfgang and Grallert, Hans-Joachim and Wess, Stefan and Friedrich, Hermann and Widenka, Thomas}, file = {Springer for Professionals:2014/XuUszkoreitEtAl14p289.pdf:PDF}, groups = {public}, interhash = {1dbf88f6523aeea60080cc6ea3c26979}, intrahash = {c4d2edc7629bc65513c892c783601a78}, keywords = {v1500 springer paper ai dfki semantic web language knowledge processing information retrieval rules adaptive analysis zzz.th zzz.sw}, pages = {289-297}, publisher = {Springer}, timestamp = {2018-04-16T11:50:20.000+0200}, title = {Domain-Adaptive Relation Extraction for the Semantic Web}, username = {flint63}, year = 2014 }