The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential.
%0 Conference Paper
%1 augenstein12:_lodif
%A Augenstein, Isabelle
%A Padó, Sebastian
%A Rudolph, Sebastian
%B Proceedings of ESWC
%C Heraklion, Greece
%D 2012
%K conference myown
%P 210--224
%T LODifier: Generating Linked Data from Unstructured Text
%U http://dx.doi.org/10.1007/978-3-642-30284-8\_21
%X The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential.
@inproceedings{augenstein12:_lodif,
abstract = {The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential.},
added-at = {2017-04-03T19:28:28.000+0200},
address = {Heraklion, Greece},
author = {Augenstein, Isabelle and Pad{\'o}, Sebastian and Rudolph, Sebastian},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/25ba51e838574b9e7eb07f42e30627aa1/sp},
booktitle = {Proceedings of ESWC},
interhash = {946a9a0fd0054e64ccd4b0e8c474f617},
intrahash = {5ba51e838574b9e7eb07f42e30627aa1},
keywords = {conference myown},
pages = {210--224},
timestamp = {2024-02-22T12:37:22.000+0100},
title = {{LODifier}: Generating Linked Data from Unstructured Text},
url = {http://dx.doi.org/10.1007/978-3-642-30284-8\_21},
year = 2012
}
