Towards Learning from User Feedback for Ontology-basedInformation Extraction (.pdf)

« (…) 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 domainexperts. The evaluation results of ConTrOn show that the enriched ontology can help improve the information extraction from technical documents. Nonetheless, the extracted information shouldbe 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 datavia 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. (…) »

source >, Opasjumruskit, Kobkaew, Schindler, Sirko, Thiele, Laura, Thiele, LauraSchäfer, Philipp Matthias, DI2KG ’19, August 05, 2019, Anchorage, AK