Proceedings of the third workshop on Exploiting semantic annotations in information retrieval - ESAIR '10

2010 ◽  
Keyword(s):  
Information Retrieval ◽  
The Third ◽  
Semantic Annotations

scholarly journals Report on the third workshop on exploiting semantic annotations in information retrieval (ESAIR)

2011 ◽  
Vol 45 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Jaap Kamps ◽  
Jussi Karlgren ◽  
Ralf Schenkel
Keyword(s):  
Information Retrieval ◽  
The Third ◽  
Semantic Annotations

Enhanced Artificial Social Cockroaches (EASC) for Modern Information Retrieval

2016 ◽  
Vol 10 (2) ◽  
pp. 65-94
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Amine Abdelmalek
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Relevant Information ◽  
Taboo Search ◽  
The Third ◽  
Naive Method ◽  
Substantial Problem ◽  
Simulating Annealing ◽  
Entropy Error ◽  
Measure Entropy

This article deals on an improved version of the recently developed Artificial Social Cockroaches (ASC) algorithm based on several modifications. The EASC has as input a set of artificial cockroaches and N selected shelters. It is based on a random displacement step and a set of operators (selection cockroaches, shelter attraction, congener's attraction, shelter permutation). Each cockroach must be hidden in the shelter where it feels safer (evaluation function). In the recent years with the coming of the world wide web, the amount of unstructured documents available in the digital society increases and becomes easily accessible, all this has led that satisfy the needs of users in terms of relevant information has become a substantial problem in the scientific community. The second component of the authors' study is to apply the algorithm (EASC) as an information retrieval system using multilingual pre-processing and thesaurus to solve the problems of multilingual query and searching with synonymy. The relevant documents will be rendered as a list of ranked and classified documents from the most relevant to the least relevant. Lastly the authors apply the benchmark Medline and a series of valuation measures (precision, recall, f-measure, entropy, error, accuracy, specificity, TCR, ROC) for the experimentation, also they have compared their results with the outcomes of set of existed systems (social worker bees, taboo search, genetic algorithm, simulating annealing, naïve method). The third component of the authors' system is the visualization step that ensures the presentation of the result in the form of a cobweb with some realism to be understandable by users.

scholarly journals Report on the fourth workshop on exploiting semantic annotations in information retrieval (ESAIR'11)

2012 ◽  
Vol 46 (1) ◽  
pp. 56-64
Author(s):  
Omar Alonso ◽  
Jaap Kamps ◽  
Jussi Karlgren
Keyword(s):  
Information Retrieval ◽  
Semantic Annotations

Information Retrieval in Business Industry Using Blockchain Technology and Artificial Intelligence

2021 ◽  
pp. 204-219
Author(s):  
Sheela K. ◽  
Priya C.
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Original Data ◽  
Storage And Retrieval ◽  
The Third ◽  
Blockchain Technology

Industry 5.0 promotes automation in an optimized way. Collaboration with blockchain technology and artificial intelligence helps to enrich Industry 5.0 with its quantifiers and qualifiers. In the business industry, information plays an iconic role. When we consider the issues of storage and retrieval, we need to think about blockchain technology where the data will be stored and shared in a secure way. Here, the data will be distributed across the network in an encrypted format; hence, the original data can be viewed only by the owner of the data. Blockchain stores the information in the form of blocks. Every block has three sections. The first section holds the hash value of the previous block, the second one holds the information to be stored in a block, and the third one holds the hash value of an upcoming block. It does not allow an intruder to hack or modify the data without user's knowledge as these blocks are interconnected on both the sides with their hashes. This synergy of technologies brings supremacy in the field of business industries which will be discussed in this chapter.

Exploiting semantic annotations in information retrieval

2008 ◽  
Vol 42 (1) ◽  
pp. 55-58 ◽  
Author(s):  
Omar Alonso ◽  
Hugo Zaragoza
Keyword(s):  
Information Retrieval ◽  
Semantic Annotations
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