Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
This work addresses entity retrieval enhancement for knowledge base applications, but it is incremental as it builds on existing neural classifiers with ensemble methods.
The paper tackled the triple scoring task for ranking entities in a knowledge base by combining outputs from multiple neural network classifiers with a supervised model, achieving the best performance in Kendall's tau and competitive results in accuracy and average score difference.
This paper describes our approach for the triple scoring task at the WSDM Cup 2017. The task required participants to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks. We propose an approach wherein the outputs of multiple neural network classifiers are combined using a supervised machine learning model. The experimental results showed that our proposed method achieved the best performance in one out of three measures (i.e., Kendall's tau), and performed competitively in the other two measures (i.e., accuracy and average score difference).