TY  - CONF
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Winkens, Marvin
A1  - Zündorf, Albert
T1  - Catalog integration of low-quality product data by attribute label ranking
T2  - Proceedings of the 9th International Conference on Data Science, Technology and Applications DATA - Volume 1
N2  - 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.
Y1  - 2020
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/9301
SN  - 978-989-758-440-4
N1  - 9th International Conference on Data Science, Technologies and Applications (DATA 2020), 7 - 9 July 2020, online
SP  - 90
EP  - 101
PB  - SciTePress
CY  - Setúbal, Portugal
ER  -