The paper investigates the use of Machine Learning (ML) to support experts validating skos:exactMatch links. It trains ML techniques provided by RapidMiner with manually validated links and shows how to use the obtained predictive models for saving expert efforts. The obtained results are preliminary but encouraging: the trained predictive models reduce up to 70% the number of manual checking required from experts, leaving only 10% of the wrong links unnoticed. Cutting the 70% of the expert burden is crucial, especially when dealing with the validation of large sets of links.

Applying predictive models to support skos:ExactMatch validation

Riccardo Albertoni
2019

Abstract

The paper investigates the use of Machine Learning (ML) to support experts validating skos:exactMatch links. It trains ML techniques provided by RapidMiner with manually validated links and shows how to use the obtained predictive models for saving expert efforts. The obtained results are preliminary but encouraging: the trained predictive models reduce up to 70% the number of manual checking required from experts, leaving only 10% of the wrong links unnoticed. Cutting the 70% of the expert burden is crucial, especially when dealing with the validation of large sets of links.
2019
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
Inglese
Emmanouel Garoufallou, Francesca Fallucchi, Ernesto William De Luca
Metadata and Semantic Research
13th International Conference on Metadata and Semantic Research (MTSR 2019)
187
193
978-3-030-36598-1
https://link.springer.com/chapter/10.1007%2F978-3-030-36599-8_16
Springer
Cham Heidelberg New York Dordrecht London
SVIZZERA
Sì, ma tipo non specificato
28-31/10/2019
Rome, Italy
Linkset correctness
Quality
Expert validation
Predictive Models
First online 4/12/2019
1
restricted
Albertoni, Riccardo
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   eEnvironmental services for advanced applications within INSPIRE
   eENVplus
   FP7
   325232
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/366583
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