A Hereditary Attentive Template-based Approach for Complex Knowledge Base Question Answering Systems
Jorão Gomes, Rômulo C. de Mello, Victor Ströele, Jairo Francisco de Souza
Abstract
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.
Topics & Concepts
Question answeringComputer scienceKnowledge baseParsingArtificial intelligenceNatural language processingNatural languageMatching (statistics)TemplateTree (set theory)Knowledge-based systemsInformation retrievalMachine learningProgramming languageStatisticsMathematicsMathematical analysisTopic ModelingNatural Language Processing TechniquesSemantic Web and Ontologies