- AutorIn
- Daniel Obraczka
- Titel
- Active learning of link specifications using decision tree learning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-171688
- Schriftenreihe
- Abschluss- und Qualifikationsarbeiten aus der Fakultät für Mathematik und Informatik
- Datum der Einreichung
- 02.01.2017
- Abstract (EN)
- In this work we presented an implementation that uses decision trees to learn highly accurate link specifications. We compared our approach with three state-of-the-art classifiers on nine datasets and showed, that our approach gives comparable results in a reasonable amount of time. It was also shown, that we outperform the state-of-the-art on four datasets by up to 30%, but are still behind slightly on average. The effect of user feedback on the active learning variant was inspected pertaining to the number of iterations needed to deliver good results. It was shown that we can get FScores above 0.8 with most datasets after 14 iterations.
- Freie Schlagwörter (EN)
- Link Discovery, Machine Learning, Semantic Web
- Klassifikation (DDC)
- 000
- BetreuerIn Hochschule / Universität
- Dr. Axel-Cyrille NGONGA
- Den akademischen Grad verleihende / prüfende Institution
- Universität Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-171688
- Veröffentlichungsdatum Qucosa
- 13.02.2018
- Dokumenttyp
- Bachelorarbeit
- Sprache des Dokumentes
- Englisch