A Question Answering Framework Based on Hybridization of Deep Learning and Semantic Web Techniques

The question answering (QA) system has been existing for several years. QA systems are divided into different processes such as question processing, document processing, paragraph extraction, answer extraction, question analysis, phrase mapping, disambiguation, query construction, querying the Knowledge Base (KB), and result ordering on user response respectively. Based on these processes, many models have been developed using approaches ranging from linguistic, statistical, and pattern matching. Popular models are Feedback, Refinement and Extended VocabularY Aggregation (FREyA), PowerAqua, SemSek, Semantic Interpretation of User Queries for QA on Interlinked Data (SINA), DEep Answers for maNy Naturally Asked questions (DEANNA), gAnswer, SemGraph, OKBQA (Open Knowledge Base and Question Answering) and Semantic Question Answering (SQA), for performance evaluation, these mostly focus on higher precision, recall, and/or F-measure. However, most of these models are constrained in the following operations: the combination of knowledge bases from different sources, formalism for knowledge representation to support interoperability, optimization of query construction and generation, ranking of responses, and support for set operations (union, sorting, comparison, and aggregation on user query) during query generation. This research proposes a hybrid of recurrent neural network and semantic-web-based question answering as the (RNNSQA) framework that combines heterogeneous knowledge sources and improves on state-of-the-art query generation mechanisms to allow for integration of comprehensive question-type operations, and set-based operations listed above. First, there would be a combination of techniques that is Natural Language Processing (NLP) and Recurrent Neural Network (RNN) techniques in classifying questions into types. Secondly, the semantic web technique is then employed in generating (SPARQL Protocol and RDF (Resource Description Framework) Query Language) SPARQL-based candidate queries. The result of this study is an enhanced QA framework with improved query translation and construction capability.