Abstract
In this paper we present QuASIt, a Question Answering System for the Italian language, and the underlying cognitive architecture. The term cognitive is meant in the procedural semantics perspective, which states that the interpretation and/or production of a sentence requires the execution of some cognitive processes over both a perceptually grounded model of the world, and a linguistic knowledge acquired previously. We attempted to model these cognitive processes with the aim to make an artificial agent able both to understand and produce natural language sentences. The agent runs these processes on its inner domain representation using the linguistic knowledge also. In this sense, QuASIt is both a rule-based and ontology-based question answering system.
In the model, rules are aimed at understanding the query in terms of the linguistic typology of the question, and enabling its semantic processing as regards the search for the answer in the structured knowledge from DBPedia Italian project. Also the free explicative text in support of the query is analyzed if available. QuASIt attempts to answer for both multiple choice and essay questions. The model is presented, the implementation of the system is detailed, and some experiments are discussed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
McGuinness, D.L.: Question answering on the semantic web. IEEE Intell. Syst. 1, 82–85 (2004)
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books, Cambridge (1998)
Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley framenet project. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, ACL 1998, vol. 1, pp. 86–90. Association for Computational Linguistics, Stroudsburg, PA, USA (1998)
Pianta, E., Bentivogli, L., Girardi, C.: MultiWordNet: developing an aligned multilingual database. In: Proceedings of the First International Conference on Global WordNet, January 2002
Dean-Hall, A., Clarke, C.L.A., Kamps, J., Kiseleva, J., Voorhees, E.M.: Overview of the TREC 2015 contextual suggestion track. In: Voorhees, E.M., Ellis, A. (eds.) TREC, Volume Special Publication, pp. 500–319, National Institute of Standards and Technology (NIST) (2015)
Boubiche, D.E., Hidoussi, F., Cruz, H.T., Bouziane, A., Bouchiha, D., Doumi, N., Malki, M.: Question answering systems: survey and trends. Procedia Comput. Sci. 73(2015), 366–375 (2015). International Conference on Advanced Wireless Information and Communication Technologies (AWICT 2015)
Sag, I.A., Wasow, T., Bender, E.: Syntactic Theory: A Formal Introduction, 2nd edn. Center for the Study of Language and Information, Stanford (2003)
Basili, R., Hansen, D.H., Paggio, P., Pazienza, M.T., Zanzotto, F.M.: Ontological resources and question answering
Basili, R., Pazienza, M.T., Zanzotto, F.M.: Exploiting the feature vector model for learning linguistic representations of relational concepts. In: Workshop on Adaptive Text Extraction and Mining (ATEM 2003) Held in Conjuction with European Conference on Machine Learning (ECML 2003) (2003)
Hoffmann, T., Trousdale, G., Hoffmann, T., Trousdale, G.: Construction grammar introduction
Spranger, M., Pauw, S., Loetzsch, M., Steels, L.: Open-ended procedural semantics. In: Steels, L., Hild, M. (eds.) Language Grounding in Robots, pp. 153–172. Springer, Boston (2012)
Goldberg, A., Suttle, L.: Construction grammar. Wiley Interdisc. Rev. Cogn. Sci. 1(4), 468–477 (2010)
Hoffmann, T., Trousdale, G., Boas, H.C.: Cognitive construction grammar
Steels, L.: Introducing fluid construction grammar (2011)
Pipitone, A., Anastasio, F., Pirrone, R.: HOWERD: a hidden Markov model for automatic OWL-ERD alignment. In: ICSC, pp. 477–482. IEEE Computer Society (2016)
Pipitone, A., Pirrone, R.: A hidden Markov model for automatic generation of ER diagrams from owl ontology. In: ICSC 2014, pp. 135–142. IEEE Computer Society (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Pipitone, A., Tirone, G., Pirrone, R. (2016). QuASIt: A Cognitive Inspired Approach to Question Answering for the Italian Language. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-49130-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49129-5
Online ISBN: 978-3-319-49130-1
eBook Packages: Computer ScienceComputer Science (R0)