elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Towards Learning from User Feedback for Ontology-based Information Extraction

Opasjumruskit, Kobkaew und Schindler, Sirko und Schäfer, Philipp Matthias und Thiele, Laura (2019) Towards Learning from User Feedback for Ontology-based Information Extraction. In: Towards Learning from User Feedback for Ontology-based Information Extraction. Data Integration to Knowledge Graph Workshop, 2019-08-05, Anchorage, USA.

[img] PDF
727kB
[img] PDF
1MB

Kurzfassung

Many engineering projects involve the integration of various hardware parts from different suppliers. In preparation, parts that are best suited for the project requirements have to be selected. Information on these parts' characteristics is published in so called data sheets usually only available in textual form, e.g. as PDF files. To realize the automated processing, these characteristics have to be extracted into a machine-interpretable format. Such a process requires a lot of manual intervention and is prone to errors. Domain ontologies, among other approaches, can be used to implement the automated information extraction from the data sheets. However, ontologies rely solely on the experiences and perspectives of their creators at the time of creation. To automate the evolution of ontologies, we developed ConTrOn - Continuously Trained Ontology - that automatically extracts information from data sheets to augment an ontology created by domain experts. The evaluation results of ConTrOn show that the enriched ontology can help improve the information extraction from technical documents. Nonetheless, the extracted information should be reviewed by experts before using it in the integration process. We want to provide an intuitive way of reviewing, in which the extracted information will be highlighted on the data sheets. The experts will be able to accept, reject, or correct the extracted data via a graphical interface. This process of revision and correction can be leveraged by the system to improve itself: learning from its own mistakes and identifying common patterns to adapt in the next extraction iteration. This paper presents ideas how to use machine learning based on user feedback to improve the information extraction process.

elib-URL des Eintrags:https://elib.dlr.de/128779/
Dokumentart:Konferenzbeitrag (Vorlesung)
Titel:Towards Learning from User Feedback for Ontology-based Information Extraction
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Opasjumruskit, KobkaewKobkaew.Opasjumruskit (at) dlr.dehttps://orcid.org/0000-0002-9206-6896NICHT SPEZIFIZIERT
Schindler, SirkoSirko.Schindler (at) dlr.dehttps://orcid.org/0000-0002-0964-4457133780996
Schäfer, Philipp MatthiasP.Schaefer (at) dlr.dehttps://orcid.org/0000-0003-3931-6670NICHT SPEZIFIZIERT
Thiele, LauraLaura.Thiele (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:August 2019
Erschienen in:Towards Learning from User Feedback for Ontology-based Information Extraction
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Ontology-based information extraction Machine learning Knowledge representation Pattern recognition
Veranstaltungstitel:Data Integration to Knowledge Graph Workshop
Veranstaltungsort:Anchorage, USA
Veranstaltungsart:Workshop
Veranstaltungsdatum:5 August 2019
HGF - Forschungsbereich:keine Zuordnung
HGF - Programm:keine Zuordnung
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:keine Zuordnung
DLR - Forschungsgebiet:keine Zuordnung
DLR - Teilgebiet (Projekt, Vorhaben):keine Zuordnung
Standort: Jena
Institute & Einrichtungen:Institut für Datenwissenschaften > Softwaresysteme für die Digitalisierung
Institut für Datenwissenschaften > Sichere Digitale Systeme
Hinterlegt von: Opasjumruskit, Kobkaew
Hinterlegt am:19 Sep 2019 07:52
Letzte Änderung:24 Apr 2024 20:32

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.