Building the Inference Component of an Historical Information Retrieval System
Proceedings of the Seventh International Joint Conference on Artificial Intelligence. INFERENCE and REASONING. Pages 401-407.
Abstract
The principal characteristic of the RESEDA system is being able to question an historicaldatabase about the causal relationships which may exist between different attested !acts in the database, but which are not explicitly recorded . In order to do this the designers of RESEDA have created from scratch a methodology for the formalization and generalization of the reasoning used by historians in concrete situations. The description of the present state of this methodology is the object of this paper.
Gian Piero Zarri. 1981. Building the Inference Component of an Historical Information Retrieval System. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence, IJCAI-81. IJCAI, 401–407. https://ijcai.org/proceedings/1981/78
Zarri, G. P. (1981). Building the Inference Component of an Historical Information Retrieval System. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence, IJCAI-81 (pp. 401–407). IJCAI. https://ijcai.org/proceedings/1981/78
@inproceedings{ijcai1981p78,
title = {Building the Inference Component of an Historical Information Retrieval System},
author = {Zarri, Gian Piero},
booktitle = {Proceedings of the Seventh International Joint Conference on
Artificial Intelligence, {IJCAI-81}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Roger C. Schank},
pages = {401--407},
year = {1981},
month = {8},
note = {INFERENCE and REASONING},
url = {https://ijcai.org/proceedings/1981/78},
}
TY - CPAPER TI - Building the Inference Component of an Historical Information Retrieval System AU - Zarri, Gian Piero T2 - Proceedings of the Seventh International Joint Conference on Artificial Intelligence, IJCAI-81 PB - International Joint Conferences on Artificial Intelligence PY - 1981 SP - 401 EP - 407 UR - https://ijcai.org/proceedings/1981/78 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: