The Ontology Definition Metamodel for Search Engine Based on Contextual Concept

2014 ◽  
Vol 926-930 ◽  
pp. 2263-2266
Author(s):  
Li Juan Diao ◽  
Jun Zhong Gu ◽  
Liang Chun
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Domain Ontology ◽  
Semantic Interoperability ◽  
Main Components ◽  
General Ontology ◽  
Multiple Domains

Ontology definition metamodel has been widely adopted in aspect of building ontology. However existing ontology metamodel is only suitable for building ontology in a certain domain. With collaboration and sharing among multiple domains, we face the seriously problem that is how to overcome semantic interoperability. For this problem, we need to combine general ontology with domain ontology and merge all existing ontologies by ontology metamodel. In this paper, we define main components of ontology metamodel and present conditional context and contextual concept unit. In addition, we introduce the method of mapping between conditional context and contextual concept unit. Finally, we use an example about information retrieval to illustrate its function and analysis its feasibility.

Reliability of a Distributed Search Engine for Fresh Information Retrieval in Large-Scale Intranet

2006 ◽  
pp. 203-216
Author(s):  
Nobuyoshi Sato ◽  
Minoru Udagawa ◽  
Minoru Uehara ◽  
Yoshifumi Sakai ◽  
Hideki Mori
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Large Scale ◽  
Distributed Search

INFORMATION RETRIEVAL BASED ON A SEARCH ENGINE WITH INVERTED INDEX FOR SOFIA (ONLINE SYSTEM FOR INTERACTIVE IMPROVEMENT OF PRIMARY CARE)

2019 ◽  
Author(s):  
Humberto Oliveira Serra ◽  
Lucas Bezerra Maia ◽  
Alexis Salomon ◽  
Nigel da Silva Lima ◽  
Rubem de Sousa Silva ◽  
...  
Keyword(s):  
Primary Care ◽  
Information Retrieval ◽  
Search Engine ◽  
Inverted Index ◽  
Online System

Cross-Lingual Information Retrieval

2005 ◽  
pp. 153-170 ◽  
Author(s):  
Christopher Yang ◽  
Kar W. Li
Keyword(s):  
Information Retrieval ◽  
Digital Library ◽  
Digital Libraries ◽  
World Wide ◽  
Relevant Information ◽  
Semantic Interoperability ◽  
Library Research ◽  
The World ◽  
Cross Lingual ◽  
Multiple Languages

Structural and semantic interoperability have been the focus of digital library research in the early 1990s. Many research works have been done on searching and retrieving objects across variations in protocols, formats, and disciplines. As the World Wide Web has become more popular in the last ten years, information is available in multiple languages in global digital libraries. Users are searching across the language boundary to identify the relevant information that may not be available in their own language. Cross-lingual semantic interoperability has become one of the focuses in digital library research in the late 1990s. In particular, research in cross-lingual information retrieval (CLIR) has been very active in recent conferences on information retrieval, digital libraries, knowledge management, and information systems. The major problem in CLIR is how to build the bridge between the representations of user queries and documents if they are of different languages.

Improving Domain Searches through Customized Search Engines

2011 ◽  
pp. 1-22
Author(s):  
Cecil Eng Huang Chua ◽  
Roger H. Chiang ◽  
Veda C. Storey
Keyword(s):  
Information Retrieval ◽  
Software Development ◽  
Search Engine ◽  
Search Engines ◽  
Information Society ◽  
The Internet ◽  
Design Features ◽  
Development Environment ◽  
Software Development Environment ◽  
Automated Software

Search engines are ubiquitous tools for seeking information from the Internet and, as such, have become an integral part of our information society. New search engines that combine ideas from separate search engines generally outperform the search engines from which they took ideas. Designers, however, may not be aware of the work of other search engine developers or such work may not be available in modules that can be incorporated into another search engine. This research presents an interoperability architecture for building customized search engines. Existing search engines are analyzed and decomposed into self-contained components that are classified into six categories. A prototype, called the Automated Software Development Environment for Information Retrieval, was developed to implement the interoperability architecture, and an assessment of its feasibility was carried out. The prototype resolves conflicts between components of separate search engines and demonstrates how design features across search engines can be integrated.

DemonD

2010 ◽  
pp. 652-668
Author(s):  
Charles Delalonde ◽  
Eddie Soulier
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Community Participation ◽  
Search Engine ◽  
Management Practices ◽  
Actor Network Theory ◽  
Social Search ◽  
Retrieval Systems ◽  
Knowledge Management Practices ◽  
Collaborative Information Retrieval

This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The authors describe traditional knowledge management practices and review post-cognitivists theories in order to define social creation in collaborative information retrieval activity. The Actor-Network theory accurately describes association processes and includes both human and non-human entities. This chapter compares this theory with the emergence of Social Search services online and Experts’ Retrieval Systems. The chapter authors suggest afterward, a social search engine named DemonD that identifies documents but more specifically users relevant to a query. DemonD relies on transparent profile construction based upon user activity, community participation, and shared documents. Individuals are invited to participate in a dedicated newsgroup and the information exchanged is capitalized. The evaluation of our service both ergonomic and through a simulation provides encouraging data.

scholarly journals Image recognition in the artificial agriculture officer

2018 ◽  
Vol 7 (3.3) ◽  
pp. 119
Author(s):  
B Lokesh ◽  
Ravoori Charishma ◽  
Natuva Hiranmai
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Search Engine ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Crop Production ◽  
Android Application ◽  
Weather Patterns ◽  
Crop Diseases ◽  
The Right

Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it  

Search Engine

2011 ◽  
pp. 74-100
Author(s):  
Eliana Campi ◽  
Gianluca Lorenzo
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Base ◽  
Search Engine ◽  
Automatic Classification ◽  
Efficient Approach ◽  
Search Results ◽  
The Creation ◽  
F Measure

This chapter presents technologies and approaches for information retrieval in a knowledge base. We intend to show that the use of ontology for domain representation and knowledge search offers a more efficient approach for knowledge management. This approach focuses on the meaning of the word, thus becoming an important element in the building of the Semantic Web. The search based on both keywords and ontology allows more effective information retrieval exploiting the Semantic of the information in a variety of data. We present a method for taxonomy building, annotating, and searching documents with taxonomy concepts. We also describe our experience in the creation of an informal taxonomy, the automatic classification, and the validation of search results with traditional measures, such as precision, recall and f-measure.

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