Translating Natural Language Queries to SPARQL
Shreya Satish Bhajikhaye · 2021
The Semantic Web is an extensive knowledge base that contains facts in the form of RDF triples. These facts are not easily accessible to the average user because to use them requires an understanding of ontologies and a query language like SPARQL. Question answering systems form a layer of abstraction on linked data to overcome these issues. These systems allow the user to input a question in a natural language and receive the equivalent SPARQL query. The user can then execute the query on the database to fetch the desired results. The standard techniques involved in translating natural language questions to SPARQL queries are natural language processing, machine learning, and information retrieval. In this report, we describe our English language to SPARQL query translation system. The input for the proposed system reads a complete question in the English language, identifies the type of query to be built, and finds the triples from the question to fit in the query. The system contains two components – template classification which uses the Tree-LSTM technique to identify the query template, and the entity recognition module which uses external libraries to recognize the triples in the question. The Lc-QuAD database, with 200 questions across two unique SPARQL templates, was used to train and evaluate the model. The system queries the Wikidata database to answer the questions and gives 60% correct results.