Flexible Matching for Noisy Structural Descriptions
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 658-663.
Abstract
Uncertaintyondataoftenmakesthetaskofperfectlymatching two descriptions quite ineffective. In this case, a flexible matching, measuring the similarity of two descriptions rather than their equality, is more useful. According to the convention of connecting similarity to the most common concept of distance, we present a definition of distance measure, based on a probabilistic interpretation of the matching predicate, which can cope with structural deformations. As the problem of matching two formulas of the FOPL is NP-complete, two methods are presented in order to cope with complexity: firstly, a branch-and-bound algorithm, and secondly, a heuristic method. These ideas are applied to the problem of recognizing office documents in digital form according to their page layout.
@inproceedings{ijcai1991p96,
title = {Flexible Matching for Noisy Structural Descriptions},
author = {Esposito, Floriana and Malerba, Donato and Semeraro, Giovanni},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {658--663},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/96},
}
TY - CPAPER TI - Flexible Matching for Noisy Structural Descriptions AU - Esposito, Floriana AU - Malerba, Donato AU - Semeraro, Giovanni T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 658 EP - 663 UR - https://ijcai.org/proceedings/1991/96 ER -Copied
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