STEM: stacked threshold-based entity matching for knowledge base generation
- Publication date
- Publisher
- 'IOS Press'
Abstract
One of the major issues encountered in the generation of knowledge bases is the integration of data coming from
a collection of heterogeneous data sources. A key essential task when integrating data instances is the entity matching. Entity
matching is based on the definition of a similarity measure among entities and on the classification of the entity pair as a match
if the similarity exceeds a certain threshold. This parameter introduces a trade-off between the precision and the recall of the
algorithm, as higher values of the threshold lead to higher precision and lower recall, and lower values lead to higher recall
and lower precision. In this paper, we propose a stacking approach for threshold-based classifiers. It runs several instances of
classifiers corresponding to different thresholds and use their predictions as a feature vector for a supervised learner. We show that
this approach is able to break the trade-off between the precision and recall of the algorithm, increasing both at the same time and
enhancing the overall performance of the algorithm. We also show that this hybrid approach performs better and is less dependent
on the amount of available training data with respect to a supervised learning approach that directly uses properties’ similarity
values. In order to test the generality of the claim, we have run experimental tests using two different threshold-based classifiers
on two different data sets. Finally, we show a concrete use case describing the implementation of the proposed approach in the
generation of the 3cixty Nice knowledge base