Conference Proceeding
Refine
Year of publication
Institute
- Fachbereich Elektrotechnik und Informationstechnik (302)
- Fachbereich Energietechnik (259)
- Fachbereich Medizintechnik und Technomathematik (241)
- Fachbereich Luft- und Raumfahrttechnik (208)
- Fachbereich Maschinenbau und Mechatronik (207)
- Solar-Institut Jülich (167)
- IfB - Institut für Bioengineering (152)
- Fachbereich Bauingenieurwesen (139)
- Fachbereich Wirtschaftswissenschaften (69)
- ECSM European Center for Sustainable Mobility (59)
- INB - Institut für Nano- und Biotechnologien (52)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (48)
- Fachbereich Chemie und Biotechnologie (35)
- Nowum-Energy (22)
- Kommission für Forschung und Entwicklung (17)
- Fachbereich Architektur (13)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (10)
- FH Aachen (7)
- Fachbereich Gestaltung (4)
- IaAM - Institut für angewandte Automation und Mechatronik (3)
- Arbeitsstelle fuer Hochschuldidaktik und Studienberatung (2)
- Institut fuer Angewandte Polymerchemie (2)
- Verwaltung (2)
- Digitalisierung in Studium & Lehre (1)
- Freshman Institute (1)
- Kommission für Planung und Finanzen (1)
- Senat (1)
Language
- English (1162)
- German (475)
- Multiple languages (1)
- Spanish (1)
Document Type
- Conference Proceeding (1639) (remove)
Keywords
- Biosensor (25)
- Blitzschutz (15)
- CAD (11)
- Finite-Elemente-Methode (11)
- civil engineering (11)
- Bauingenieurwesen (10)
- Lightning protection (9)
- Einspielen <Werkstoff> (6)
- Telekommunikationsmarkt (6)
- shakedown analysis (6)
Untersuchungen zur Tragfähigkeit und Steifigkeit eines neuartigen Wandelements in Holzbauweisen
(2018)
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.