Natural language query formalization to SPARQL for querying knowledge bases using Rasa

The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.