scholarly journals Comparative Analysis of Information Retrieval using Ontology Based vs Traditional Information Systems in Food Science Domain

2020 ◽  
Vol 40 (02) ◽  
pp. 437-444
Author(s):  
Padmavathi T
Keyword(s):  
Information Retrieval ◽  
Information Systems ◽  
Retrieval System ◽  
Food Science ◽  
Web Pages ◽  
Average Precision ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Science Domain ◽  
Server Application

The current methods of searching and information retrieval are imprecise, often yielding results in tens of thousands of web pages. Extraction of the actual information needed often requires extensive manual browsing of retrieved documents. In order to address these drawbacks, this paper introduces an implementation in the field of food science of the ontology-based information retrieval system, and comparison is made with conventional information systems. The ontology of Food Semantic Web Knowledge Base (FSWKB) was built using the Protégé framework which supports two main models of ontology through the editors Protégé-Frames and Protégé-OWL. The FSWKB is composed of two heterogeneous ontologies, and these are merged and processed on a separate server application making use of the Apache Jena Fuseki an SPARQL server offering SPARQL endpoint. The experimental results indicated that ontology-based information systems are more effective in terms of their retrieval capability compared to the more conventional information retrieval systems. The retrieval effectiveness was measured in terms of precision and recall. The results of the work showed that traditional search results in average precision and recall levels of 0.92 and 0.18. The ontology-based test for precision and recall has average rates of 0.96 and 0.97.

Building Proximity Models for Cross Language Information Retrieval

2015 ◽  
Vol 1 ◽  
pp. 8
Author(s):  
Lam Tung Giang ◽  
Vo Trung Hung ◽  
Huynh Cong Phap
Keyword(s):  
Information Retrieval ◽  
Information Systems ◽  
Bag Of Words ◽  
Retrieval Performance ◽  
Average Precision ◽  
Ranking Models ◽  
Retrieval Systems ◽  
Cross Language Information Retrieval ◽  
Information Retrieval Systems ◽  
Cross Language

In information retrieval systems, the proximity of query terms has been employed to enable ranking models to go beyond the ”bag of words” assumption and it can promote scores of documents where the matched query terms are close to each other. In this article, we study the integration of proximity models into cross-language information retrieval systems. The new proximity models are proposed and incorporated into existing cross-language information systems by combining the proximity score and the original score to re-rank retrieved documents. The experiment results show that the proposed models can help to improve the retrieval performance by 4%-7%, in terms of Mean Average Precision.

An Information Retrieval System Based on Portable Device

2013 ◽  
Vol 712-715 ◽  
pp. 2706-2711
Author(s):  
Xiao Qing Yu ◽  
Wen Gen Wang ◽  
Jian Hua Shi ◽  
Yun Hui Wang
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Relevant Information ◽  
Information Retrieval System ◽  
Text Indexing ◽  
Traditional System ◽  
Related Information ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Audio Information

Information retrieval is the activity to organize information in a certain way, and according to the users demand to find out the related information from a collection of resources. Retrieval process and technology can be based on metadata or full-text indexing. Most of the relevant information retrieval systems are devised on the computer. However, with the highly development of the embedded technology, some popular application have been developed on the platform. In this paper, we will introduce an information retrieval system on the iOS platform which is more convenient, practical, and effective compared with the traditional system. And we will introduce an application based on this system design. The experiments shown that this system was exactly effective utilized to retrieval audio information.

Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home

2016 ◽  
Vol 25 (03) ◽  
pp. 1650017 ◽  
Author(s):  
Hyeokju Ahn ◽  
Harksoo Kim
Keyword(s):  
Information Retrieval ◽  
Smart Home ◽  
Retrieval System ◽  
Generation Model ◽  
Query Term ◽  
Post Processing ◽  
Retrieval Systems ◽  
Sentence Retrieval ◽  
Information Retrieval Systems ◽  
Performance Results

With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g., voice-activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users’ spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs), the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post-processing model of an ASR engine. The post-processing model consists of a re-ranking and a query term generation model. The re-ranking model rearranges top-n outputs of the ASR engines using the ranking support vector machine (Ranking SVM). The query term generation model extracts meaningful content words from the re-ranked queries based on term frequencies and query rankings. In the experiments, the re-ranking model improved the top-1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain.

A User-Centered Approach for Information Retrieval

2011 ◽  
pp. 165-178 ◽  
Author(s):  
Antonio Picariello
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Statistical Information ◽  
Web Pages ◽  
General Knowledge ◽  
Test Set ◽  
Retrieval Technique ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Relevant Component

Information retrieval can take great advantages and improvements considering users’ feedbacks. Therefore, the user dimension is a relevant component that must be taken into account while planning and implementing real information retrieval systems. In this chapter, we first describe several concepts related to relevance feedback methods, and then propose a novel information retrieval technique which uses the relevance feedback concepts in order to improve accuracy in an ontology-based system. In particular, we combine the Semantic information from a general knowledge base with statistical information using relevance feedback. Several experiments and results are presented using a test set constituted of Web pages.

User Relevance Feedback in Semantic Information Retrieval

2009 ◽  
pp. 270-281
Author(s):  
Antonio Picariello ◽  
Antonio M. Rinaldi
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Semantic Information ◽  
Information Retrieval System ◽  
Web Pages ◽  
General Knowledge ◽  
Retrieval Systems ◽  
Crucial Component ◽  
Information Retrieval Systems ◽  
Semantic Information Retrieval

The user dimension is a crucial component in the information retrieval process and for this reason it must be taken into account in planning and technique implementation in information retrieval systems. In this paper we present a technique based on relevance feedback to improve the accuracy in an ontology based information retrieval system. Our proposed method combines the semantic information in a general knowledge base with statistical information using relevance feedback. Several experiments and results are presented using a test set constituted of Web pages.

Relevant Intra-Actions in Networked Environments

2011 ◽  
pp. 230-251 ◽  
Author(s):  
Theresa Dirndorfer Anderson
Keyword(s):  
Information Retrieval ◽  
Information Systems ◽  
Conceptual Framework ◽  
Case Studies ◽  
Theoretical Framework ◽  
Design Development ◽  
Retrieval Systems ◽  
Relevance Judgments ◽  
Information Retrieval Systems ◽  
Collaborative Information Retrieval

This chapter uses a study of human assessments of relevance to demonstrate how individual relevance judgments and retrieval practices embody collaborative elements that contribute to the overall progress of that person’s individual work. After discussing key themes of the conceptual framework, the chapter will discuss two case studies that serve as powerful illustrations of these themes for researchers and practitioners alike. These case studies, outcomes of a two-year ethnographic exploration of research practices, illustrate the theoretical position presented in part one of the chapter, providing lessons for the ways that people work with information systems to generate knowledge and the conditions that will support these practices. The chapter shows that collaboration does not have to be explicit to influence searcher behavior. It seeks to present both a theoretical framework and case studies that can be applied to the design, development and evaluation of collaborative information retrieval systems.

An Information Retrieval System Based on Multiple Portlets

2021 ◽  
Vol 13 (1) ◽  
pp. 74-86
Author(s):  
Glauber José Vaz ◽  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Service Oriented Architecture ◽  
Information Retrieval System ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Single User Interface ◽  
Service Oriented ◽  
Single User ◽  
Aggregated Search

Information retrieval systems built with a service-oriented architecture have numerous advantages, and portlets are a key technology to implement services which interact with each other in the presentation layer. This work presents an efficient approach for the communication between the components of an information retrieval system based on multiple portlets in a single user interface. It also presents the architecture and the main methods of the system implemented as a case of use for this approach. It is shown that the proposed solution yields the best inter-portlet communication mechanism in each situation, while possessing the ability to deliver aggregated search and superior user experience.

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