A NEW APPROACH TOWARDS VERTICAL SEARCH ENGINES - Intelligent Focused Crawling and Multilingual Semantic Techniques

2010 ◽  
Keyword(s):  
Search Engines ◽  
Focused Crawling ◽  
New Approach ◽  
Vertical Search ◽  
Semantic Techniques

Combining text and link analysis for focused crawling—An application for vertical search engines

2007 ◽  
Vol 32 (6) ◽  
pp. 886-908 ◽  
Author(s):  
G. Almpanidis ◽  
C. Kotropoulos ◽  
I. Pitas
Keyword(s):  
Search Engines ◽  
Link Analysis ◽  
Focused Crawling ◽  
Vertical Search

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

scholarly journals A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

2019 ◽  
Vol 14 (06) ◽  
pp. 27 ◽  
Author(s):  
Kamal El Guemmat ◽  
Sara Ouahabi
Keyword(s):  
Search Engines ◽  
Learning Objects ◽  
Learning Object ◽  
Automatic Indexing ◽  
New Approach ◽  
Learning Object Metadata ◽  
Depth Extraction ◽  
Semantic Distances ◽  
Linguistic Structures

Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.

A New Approach for Building a Scalable and Adaptive Vertical Search Engine

2008 ◽  
Vol 4 (1) ◽  
pp. 52-79 ◽  
Author(s):  
H. Arafat Ali ◽  
Ali I. El Desouky ◽  
Ahmed I. Saleh
Keyword(s):  
Search Engine ◽  
New Approach ◽  
Vertical Search ◽  
Vertical Search Engine

Speech Engines

2018 ◽  
Author(s):  
James Grimmelmann
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
The Internet ◽  
Internet Search ◽  
High Quality ◽  
New Approach ◽  
To Receive ◽  
Relevance Assessments

98 Minnesota Law Review 868 (2014)Academic and regulatory debates about Google are dominated by two opposing theories of what search engines are and how law should treat them. Some describe search engines as passive, neutral conduits for websites’ speech; others describe them as active, opinionated editors: speakers in their own right. The conduit and editor theories give dramatically different policy prescriptions in areas ranging from antitrust to copyright. But they both systematically discount search users’ agency, regarding users merely as passive audiences.A better theory is that search engines are not primarily conduits or editors, but advisors. They help users achieve their diverse and individualized information goals by sorting through the unimaginable scale and chaos of the Internet. Search users are active listeners, affirmatively seeking out the speech they wish to receive. Search engine law can help them by ensuring two things: access to high-quality search engines, and loyalty from those search engines.The advisor theory yields fresh insights into long-running disputes about Google. It suggests, for example, a new approach to deciding when Google should be liable for giving a website the “wrong” ranking. Users’ goals are too subjective for there to be an absolute standard of correct and incorrect rankings; different search engines necessarily assess relevance differently. But users are also entitled to complain when a search engine deliberately misleads them about its own relevance assessments. The result is a sensible, workable compromise between the conduit and editor theories.

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