Abstract
This paper is mainly dedicated to analyse the problem of discovering frequent temporal patterns in event sequences extracted from a large repository of newspapers. The proposed formalism and algorithms rely on Toodor, which is a document retrieval system that allows users to specify conditions over the structure, contents and temporal features of the stored documents. We develop in this work several algorithms for recognising frequent temporal patterns in terms of arc-consistency, which consist of discarding temporal occurrences that do not satisfy a temporal structure.
This work has been partially funded by the Spanish C.I.C.Y.T. project TEL97-1119.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Aramburu, M.J. and Berlanga, R. “An Approach to a Digital Library of Newspapers” Information Processing & Management Vol. 33(5), pp 645–661, Elsevier Science Ltd., 1997.
Aramburu Cabo. M. J. “Toodor: A Temporal Database Model for Historical Documents” PhD Thesis, School of Computer Science, The University of Birmingham, UK 1997.
Bettini, C., Wang, X.S., Jajodia, S. and Jia-Ling, L. “Discovering Temporal Relationships with Multiple Granularities in Time Sequences” To appear in IEEE Transations on Knowledge and Data Engineering, 1997.
Bessière, C. “Arc-consistency and Arc-consistency Again” Artificial Intelligence Vol. 65(1), pp 179–190, 1994.
Dechter, R., Meiri, I. and Pearl, J. “Temporal Constraint Networks” Artificial Intelligence Vol. 49(1), pp 61–95, 1991.
Laird, P. “Identifying and using patterns in sequential data” Algorithmic Learning Theory, 4th International Workshop, pp 1–18, Springer Verlag, Berling, 1993.
Mannila, H. and Toiven, H. “Discovering generalized episodes using minimal occurrences” Data Mining and Knowledge Discovery, 1997.
Wang, X. et. al. “Logical Design of Temporal Databases with Multiple Granularities”. ACM Transactions on Database Systems, Vol. 22(2), pp 115–170, 1997.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berlanga, R., Aramburu, M.J., Barber, F. (1998). Discovering temporal relationships in databases of newspapers. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_389
Download citation
DOI: https://doi.org/10.1007/3-540-64574-8_389
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64574-0
Online ISBN: 978-3-540-69350-5
eBook Packages: Springer Book Archive