A Demonstration System towards NLP and Knowledge Driven Data Platforms for Civil Engineering
Autor(en):
Zusammenfassung
We present a demonstrator that shows a concept towards a smart document platform for Civil Engineering. It demonstrates NLP-supported organisation of documents included into a graphical user interface. The demonstrator addresses the fundamental problem of data structure that includes the internal project logic of a planning project in Civil Engineering. To this end, we build a knowledge graph that includes not only domain-specific knowledge but also standardised project structures. So far included functionalities are a navigation that allows filtering according to a logic familiar to engineers and a simplified data import via automatic classification and tagging using language technologies.
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- BibTeX
Borst, Ja., Meinecke, Ch., Wiegreffe, Da. & Niekler, An.,
(2022).
A Demonstration System towards NLP and Knowledge Driven Data Platforms for Civil Engineering.
In:
Demmler, D., Krupka, D. & Federrath, H.
(Hrsg.),
INFORMATIK 2022.
Gesellschaft für Informatik, Bonn.
(S. 271-282).
DOI: 10.18420/inf2022_25
@inproceedings{mci/Borst2022,
author = {Borst,Janos AND Meinecke,Christofer AND Wiegreffe,Daniel AND Niekler,Andreas},
title = {A Demonstration System towards NLP and Knowledge Driven Data Platforms for Civil Engineering},
booktitle = {INFORMATIK 2022},
year = {2022},
editor = {Demmler, Daniel AND Krupka, Daniel AND Federrath, Hannes} ,
pages = { 271-282 } ,
doi = { 10.18420/inf2022_25 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
author = {Borst,Janos AND Meinecke,Christofer AND Wiegreffe,Daniel AND Niekler,Andreas},
title = {A Demonstration System towards NLP and Knowledge Driven Data Platforms for Civil Engineering},
booktitle = {INFORMATIK 2022},
year = {2022},
editor = {Demmler, Daniel AND Krupka, Daniel AND Federrath, Hannes} ,
pages = { 271-282 } ,
doi = { 10.18420/inf2022_25 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
| Dateien | Groesse | Format | Anzeige | |
|---|---|---|---|---|
| kikmu_03.pdf | 460.4Kb | Öffnen |
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Mehr Information
DOI: 10.18420/inf2022_25
ISBN: 978-3-88579-720-3
ISSN: 1617-5468
Datum: 2022
Sprache:
(en)
(en)
