Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
9-1993
Abstract
Entity identification is the problem of matching object instances from different databases which correspond to the same real-world entity. In this paper, we present a 2-step entity identification process in which attributes for matching tuples may be missing in certain tuples, and thus need to be derived prior to the matching. To match tuples, we require identity rules which specify the conditions to be satisfied by a pair of tuples, from different databases, before they can be considered as modeling the same real-world entity. We also introduce ILFD's (instance-level functional dependencies) as a form of inference rules which derive the missing identifying attributes. In order to provide more interesting integrated results to the users, we allow both identity rules and ILFD's to contain indefiniteness represented as necessary and possible support information. Based on support logic programming[2], we develop an approach to perform reasoning on the local databases using identity rules ...
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Proceedings of the International Symposium on Next Generation Database Systems and their Applications, Fukuoka, Japan, September 1996
First Page
151
Last Page
158
Publisher
NDA
City or Country
Fukuoka, Japan
Citation
LIM, Ee Peng and SRIVASTAVA, Jaideep.
Entity Identification in Database Integration: An Evidential Reasoning Approach. (1993). Proceedings of the International Symposium on Next Generation Database Systems and their Applications, Fukuoka, Japan, September 1996. 151-158.
Available at: https://ink.library.smu.edu.sg/sis_research/910
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.259
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons