AQUEOS: A system for question answering over semantic data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper presents a methodology to automatically answer natural language questions by querying an underlying domain ontology. This methodology is made up of a three-phase process, where the ontological data is firstly read and indexed, the input question is then processed by means of lexical analysis and associated with a specific question type, and finally the corresponding SPARQL queries are generated and executed in order to return the answer to the original question. This process focuses on single-verb phrases in order to guarantee a highest level of precision in providing its answer, and deals with critical lexical aspects like comparatives and superlatives by relying upon language-specific lexicons, nonetheless, it is also able to take into account more complex questions with multiple verbs, provided they meet certain specific criteria. Such a methodology has been implemented in a research prototype and is being currently experimented upon by asking questions either in the English or the Italian language, and could be applied on a number of ontology-driven applications, including advanced help desk support systems, biomedical knowledge bases and intelligent e-learning solutions.
Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014
Pages716-719
Number of pages4
DOIs
Publication statusPublished - 2014
Event6th International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014 - University of Salerno, ita
Duration: 10 Sep 201412 Sep 2014

Conference

Conference6th International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014
CityUniversity of Salerno, ita
Period10/9/1412/9/14

Keywords

  • Knowledge discovery
  • Natural language processing
  • Ontology
  • Question answering
  • RDF
  • SPARQL

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