Authors:
Wellington Franco
1
;
Caio Viktor
1
;
Artur Oliveira
1
;
Gilvan Maia
1
;
Angelo Brayner
1
;
V. Vidal
1
;
Fernando Carvalho
1
and
V. Pequeno
2
Affiliations:
1
Departamento de Computação, Federal University of Ceará, Fortaleza, Ceará, Brazil
;
2
TechLab, Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa Luís de Camões, Portugal
Keyword(s):
Question Answering Systems, Ontology, Knowledge Bases, Literature Survey.
Abstract:
Searching relevant, specific information in big data volumes is quite a challenging task. Despite the numerous
strategies in the literature to tackle this problem, this task is usually carried out by resorting to a Question
Answering (QA) systems. There are many ways to build a QA system, such as heuristic approaches, machine
learning, and ontologies. Recent research focused their efforts on ontology-based methods since the resulting
QA systems can benefit from knowledge modeling. In this paper, we present a systematic literature survey on
ontology-based QA systems regarding any questions. We also detail the evaluation process carried out in these
systems and discuss how each approach differs from the others in terms of the challenges faced and strategies
employed. Finally, we present the most prominent research issues still open in the field.