question answering

Expert Systems with Applications

Knowledge Base Question Answering systems (KBQA) aim to find answers to natural language questions over a knowledge base. This work presents a template matching approach for Complex KBQA systems (C-KBQA) using the combination of Semantic Parsing and Neural Networks techniques to classify natural language questions into answer templates. An attention mechanism was created to assist a Tree-LSTM in selecting the most important information. The approach was evaluated on the LC-Quad 1, LC-Quad 2, ComplexWebQuestion, and WebQuestionsSP datasets, and the results show that our approach outperforms other approaches on three datasets. • Knowledge base question answering systems struggle to deal with complex questions. • A template matching approach for complex question answering. • A Hereditary Attention that inherits the information in a bottom-up way. • Limitations of datasets for C-KBQA and a new cleaned version of a C-KBQA dataset.

A Hereditary Attentive Template-based Approach for Complex Knowledge Base Question Answering Systems