Stochastic Adaptive Search

2002 ◽  
pp. 231-249 ◽  
Author(s):  
Graham R. Wood ◽  
Zelda B. Zabinsky
Keyword(s):  
Adaptive Search

Semantic Search on Unstructured Data

2010 ◽  
Vol 6 (2) ◽  
pp. 17-35 ◽  
Author(s):  
Alex Kohn ◽  
François Bry ◽  
Alexander Manta
Keyword(s):  
Retrieval System ◽  
Pharmaceutical Research ◽  
Information Retrieval System ◽  
Semantic Search ◽  
Unstructured Data ◽  
Search Performance ◽  
Adaptive Search ◽  
Retrieval Performance ◽  
Enterprise Search ◽  
Existing Data

Studies agree that searchers are often not satisfied with the performance of current enterprise search engines. As a consequence, more scientists worldwide are actively investigating new avenues for searching to improve retrieval performance. This paper contributes to YASA (Your Adaptive Search Agent), a fully implemented and thoroughly evaluated ontology-based information retrieval system for the enterprise. A salient particularity of YASA is that large parts of the ontology are automatically filled with facts by recycling and transforming existing data. YASA offers context-based personalization, faceted navigation, as well as semantic search capabilities. YASA has been deployed and evaluated in the pharmaceutical research department of Roche, Penzberg, and results show that already semantically simple ontologies suffice to considerably improve search performance.

scholarly journals APPROACHES TO ADAPTIVE STOCHASTIC SEARCH BASED ON THE NONEXTENSIVE q-DISTRIBUTION

2006 ◽  
Vol 16 (07) ◽  
pp. 2081-2091 ◽  
Author(s):  
GEORGE D. MAGOULAS ◽  
ARISTOKLIS ANASTASIADIS
Keyword(s):  
Search Space ◽  
Stochastic Search ◽  
Diffusion Method ◽  
Benchmark Problems ◽  
Adaptive Search ◽  
Particle Swarm Optimizer ◽  
Network Training ◽  
Nonextensive Entropy ◽  
Potential Benefits ◽  
Entropic Index

This paper explores the use of the nonextensive q-distribution in the context of adaptive stochastic searching. The proposed approach consists of generating the "probability" of moving from one point of the search space to another through a probability distribution characterized by the q entropic index of the nonextensive entropy. The potential benefits of this technique are investigated by incorporating it in two different adaptive search algorithmic models to create new modifications of the diffusion method and the particle swarm optimizer. The performance of the modified search algorithms is evaluated in a number of nonlinear optimization and neural network training benchmark problems.

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