Analogy-based Matching Model for Domain-specific Information Retrieval

Myriam Bounhas, Bilel Elayeb

2019

Abstract

This paper describes a new matching model based on analogical proportions useful for domain-specific Information Retrieval (IR). We first formalize the relationship between documents terms and query terms through analogical proportions and we propose a new analogical inference to evaluate document relevance for a given query. Then we define the analogical relevance of a document in the collection by aggregating two scores: the Agreement, measured by the number of common terms, and the Disagreement, measured by the number of different terms. The disagreement degree is useful to filter documents out from the response (retrieved documents), while the agreement score is convenient for document relevance confirmation. Experiments carried out on three IR Glasgow test collections highlight the effectiveness of the model if compared to the known efficient Okapi IR model.

Paper Citation

in Harvard Style

Bounhas M. and Elayeb B. (2019). Analogy-based Matching Model for Domain-specific Information Retrieval.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 496-505. DOI: 10.5220/0007342104960505

in Bibtex Style

@conference{icaart19,
author={Myriam Bounhas and Bilel Elayeb},
title={Analogy-based Matching Model for Domain-specific Information Retrieval},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={496-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007342104960505},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Analogy-based Matching Model for Domain-specific Information Retrieval
SN - 978-989-758-350-6
AU - Bounhas M.
AU - Elayeb B.
PY - 2019
SP - 496
EP - 505
DO - 10.5220/0007342104960505