loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Oliver Schmidts 1 ; Bodo Kraft 1 ; Ines Siebigteroth 1 and Albert Zündorf 2

Affiliations: 1 FH Aachen, University of Applied Sciences and Germany ; 2 University of Kassel and Germany

Keyword(s): Schema Matching, Machine Learning, Classification, Natural Language Processing, Named Entity Recognition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Coupling and Integrating Heterogeneous Data Sources ; Data Mining ; Data Warehouses and OLAP ; Databases and Information Systems Integration ; Enterprise Information Systems ; Industrial Applications of Artificial Intelligence ; Natural Language Interfaces to Intelligent Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: For small to medium sized enterprises matching schemas is still a time consuming manual task. Even expensive commercial solutions perform poorly, if the context is not suitable for the product. In this paper, we provide an approach based on concept name learning from known transformations to discover correspondences between two schemas. We solve schema matching as a classification task. Additionally, we provide a named entity recognition approach to analyze, how the classification task relates to named entity recognition. Benchmarking against other machine learning models shows that when choosing a good learning model, schema matching based on concept name similarity can outperform other approaches and complex algorithms in terms of precision and F1-measure. Hence, our approach is able to build the foundation for improved automation of complex data integration applications for small to medium sized enterprises.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.171.159.141

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Schmidts, O.; Kraft, B.; Siebigteroth, I. and Zündorf, A. (2019). Schema Matching with Frequent Changes on Semi-Structured Input Files: A Machine Learning Approach on Biological Product Data. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 208-215. DOI: 10.5220/0007723602080215

@conference{iceis19,
author={Oliver Schmidts. and Bodo Kraft. and Ines Siebigteroth. and Albert Zündorf.},
title={Schema Matching with Frequent Changes on Semi-Structured Input Files: A Machine Learning Approach on Biological Product Data},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007723602080215},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Schema Matching with Frequent Changes on Semi-Structured Input Files: A Machine Learning Approach on Biological Product Data
SN - 978-989-758-372-8
IS - 2184-4984
AU - Schmidts, O.
AU - Kraft, B.
AU - Siebigteroth, I.
AU - Zündorf, A.
PY - 2019
SP - 208
EP - 215
DO - 10.5220/0007723602080215
PB - SciTePress