License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2019.19
URN: urn:nbn:de:0030-drops-103832
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10383/
Lindemann, David ;
Klaes, Christiane ;
Zumstein, Philipp
Metalexicography as Knowledge Graph
Abstract
This short paper presents preliminary considerations regarding LexBib, a corpus, bibliography, and domain ontology of Lexicography and Dictionary Research, which is currently being developed at University of Hildesheim. The LexBib project is intended to provide a bibliographic metadata collection made available through an online reference platform. The corresponding full texts are processed with text mining methods for the generation of additional metadata, such as term candidates, topic models, and citations. All LexBib content is represented and also publicly accessible as RDF Linked Open Data. We discuss a data model that includes metadata for publication details and for the text mining results, and that considers relevant standards for an integration into the LOD cloud.
BibTeX - Entry
@InProceedings{lindemann_et_al:OASIcs:2019:10383,
author = {David Lindemann and Christiane Klaes and Philipp Zumstein},
title = {{Metalexicography as Knowledge Graph}},
booktitle = {2nd Conference on Language, Data and Knowledge (LDK 2019)},
pages = {19:1--19:8},
series = {OpenAccess Series in Informatics (OASIcs)},
ISBN = {978-3-95977-105-4},
ISSN = {2190-6807},
year = {2019},
volume = {70},
editor = {Maria Eskevich and Gerard de Melo and Christian F{\"a}th and John P. McCrae and Paul Buitelaar and Christian Chiarcos and Bettina Klimek and Milan Dojchinovski},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2019/10383},
URN = {urn:nbn:de:0030-drops-103832},
doi = {10.4230/OASIcs.LDK.2019.19},
annote = {Keywords: Bibliography, Metalexicography, Full Text Collection, E-science Corpus, Text Mining, RDF Data Model}
}