Information retrieval system based semantique and big data | Zendy

Youssef Chouni | Zendy; Mohammed Erritali | Zendy; Youness Madani | Zendy; Hanane Ezzikouri | Zendy

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Information retrieval system based semantique and big data

Author(s) -

Youssef Chouni,

Mohammed Erritali,

Youness Madani,

Hanane Ezzikouri

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.04.157

Subject(s) - computer science , information retrieval , semantics (computer science) , similarity (geometry) , semantic similarity , focus (optics) , search engine indexing , set (abstract data type) , word (group theory) , natural language processing , measure (data warehouse) , artificial intelligence , data mining , linguistics , philosophy , physics , optics , image (mathematics) , programming language

In traditional word-based information retrieval systems, a document is considered a set of words representing graphs without semantics. In this paper, we focus on enriching the similarity measure by using synonymy and performance evaluation of semantic indexing approaches to a document corpus. We will also present comparisons showing that the use of synonymy with Leacock and Chodorow measures increases the semantic similarity that makes research more efficient.

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