Tree (set theory)

Mathematics

Knowledge base

Knowledge-based systems

Statistics

Machine learning

Mathematical analysis

Artificial intelligence

Natural language

Matching (statistics)

Parsing

Programming language

Natural language processing

Information retrieval

Computer science

Question answering

Template

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.

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