Information Retrieval Scheme Based on Fuzzy Ontology Framework

2013 ◽  
pp. 53-59
Author(s):  
Rongbing Wang ◽  
Na Ke
Keyword(s):  
Information Retrieval ◽  
Fuzzy Ontology ◽  
Retrieval Scheme

Query Formation and Information Retrieval with Ontology

2007 ◽  
pp. 310-323
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Retrieval System ◽  
Mobile Commerce ◽  
Information Retrieval System ◽  
Optimization Techniques ◽  
Tree Structure ◽  
Retrieval Scheme

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

scholarly journals A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

2018 ◽  
Vol 8 (8) ◽  
pp. 1383 ◽  
Author(s):  
Mingyu Li ◽  
Ning Chen
Keyword(s):  
Information Retrieval ◽  
State Of The Art ◽  
Identification System ◽  
Post Processing ◽  
Similarity Functions ◽  
Processing Level ◽  
Retrieval Scheme ◽  
The Common ◽  
Cover Song ◽  
Music Information

Similarity measurement plays an important role in various information retrieval tasks. In this paper, a music information retrieval scheme based on two-level similarity fusion and post-processing is proposed. At the similarity fusion level, to take full advantage of the common and complementary properties among different descriptors and different similarity functions, first, the track-by-track similarity graphs generated from the same descriptor but different similarity functions are fused with the similarity network fusion (SNF) technique. Then, the obtained first-level fused similarities based on different descriptors are further fused with the mixture Markov model (MMM) technique. At the post-processing level, diffusion is first performed on the two-level fused similarity graph to utilize the underlying track manifold contained within it. Then, a mutual proximity (MP) algorithm is adopted to refine the diffused similarity scores, which helps to reduce the bad influence caused by the “hubness” phenomenon contained in the scores. The performance of the proposed scheme is tested in the cover song identification (CSI) task on three cover song datasets (Covers80, Covers40, and Second Hand Songs (SHS)). The experimental results demonstrate that the proposed scheme outperforms state-of-the-art CSI schemes based on single similarity or similarity fusion.

An Enhanced Fuzzy Information Retrieval Model Based on Linguistics

2014 ◽  
Vol 519-520 ◽  
pp. 853-856
Author(s):  
Zeinab E. Al-Arab ◽  
Ahmed M. Gadallah ◽  
Hesham M. Hefny
Keyword(s):  
Information Retrieval ◽  
Ranking Algorithm ◽  
Retrieval Model ◽  
Fuzzy Information ◽  
Model Based ◽  
Fuzzy Ontology ◽  
Confidence Degree ◽  
Subjective View ◽  
Fuzzy Information Retrieval

The paper proposes a linguistic based fuzzy ontology information retrieval model. The model deals with linguistic based queries in multi domains. Such linguistics are user defined, reflecting his subjective view. The model also proposes a ranking algorithm that ranks the set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree.

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