Semantic mapping from natural language questions to OWL queries

Mingxia Gao, Jiming LIU*, Ning Zhong, Furong Chen, Chunnian Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


Natural language question-and-answering is one of the most convenient means for communicating with the Semantic Web, which is typically in the form of online knowledge bases encoded in Web Ontology Language (OWL). To understand a natural language question, it is essential that it can be translated into a query that is understandable by the knowledge bases. This article is concerned with the task of semantic mapping from natural language questions to OWL queries, and proposes an automatic and domain-independent mapping framework, called Three-Phases Semantic Mapping (TPSM). The TPSM framework approaches the task of semantic mapping in three interrelated phases: (i) formalizing knowledge, (ii) building semantic mapping, and (iii) combining OWL queries. First, formalizing knowledge formalizes the units of mapping in the natural language and OWL knowledge. Second, semantic mapping builds the transverse mapping between questions and OWL knowledge, as formalized in the first phase, by means of working with the so-called Fuzzy Constraint Satisfaction Problems (FCSP). Third, combining OWL queries obtains valid Resource Description Framework (RDF) models by applying predefined templates and their corresponding combining methods. We have implemented a prototype semantic mapping system based on the framework, and have conducted a series of experimental validations involving OWL knowledge bases in different domains as queried by various types of questions.

Original languageEnglish
Pages (from-to)280-314
Number of pages35
JournalComputational Intelligence
Issue number2
Publication statusPublished - May 2011

Scopus Subject Areas

  • Computational Mathematics
  • Artificial Intelligence

User-Defined Keywords

  • fuzzy constraint satisfaction problem
  • natural language question-and-answering
  • semantic mapping
  • Semantic Web


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