Automated conversion from natural language query to SPARQL query

Haemin Jung, Wooju Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Nowadays, domain ontologies are widely used as background knowledge bases. However, end users of ontology-based question answering (QA) systems are unaware of major concepts of ontology or the structure of domain ontology schema. Thus, it has been essential to provide an efficient method to reduce this gap. Namely, the critical issue for ontology-based QA systems is how to generate a SPARQL query from a user’s natural language query (NLQ). Therefore, we proposed a method to generate SPARQL queries from Korean natural language queries. When an input query comes in, we split it into a set of tokens and map each token to certain resources in the ontology. Subsequently, a graph generation process creates multiple “query graphs” by arranging the resources and identifying relationships between them. To identify relations between resources, we applied a path search algorithm based on the structure of domain ontology schema. We score query graphs by measuring the degree to which the graph reflected the general user’s intent, and the best-rated query graph is converted into a SPARQL query. We implemented a prototype system to evaluate the proposed method for the music domain ontology and conclude that our query conversion process can convert Korean natural language queries into semantically equivalent SPARQL queries. We anticipate that, after appropriate modification, the process can be applied to other languages.

Original languageEnglish
Pages (from-to)501-520
Number of pages20
JournalJournal of Intelligent Information Systems
Issue number3
Publication statusPublished - 2020 Dec 1

Bibliographical note

Funding Information:
This research was supported by the project ”Development of NLQ-KBQA Model for KB Query” of SK Telecom Co., Ltd. 1 2 3

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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