Web Resources on Medical Tourism

2019 ◽  
pp. 174-195
Author(s):  
S. Naseehath
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Medical Tourism ◽  
Engine Performance ◽  
Link Analysis ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
General Search

Webometric research has fallen into two main categories, namely link analysis and search engine evaluation. Search engines are also used to collect data for link analysis. A set of measurements is proposed for evaluating web search engine performance. Some measurements are adapted from the concepts of recall and precision, which are commonly used in evaluating traditional information retrieval systems. Others are newly developed to evaluate search engine stability, which is unique to web information retrieval systems. Overlapping of search results, annual growth of search results on each search engines, variation of results on search using synonyms are also used to evaluate the relative efficiency of search engines. In this study, the investigator attempts to conduct a webometric study on the topic medical tourism in Kerala using six search engines; these include three general search engines, namely Bing, Google, and Lycos, and three metasearch engines, namely Dogpile, ixquick, and WebCrawler.

A Study on Effective Measurement of Search Results from Search Engines

2019 ◽  
Vol 27 (1) ◽  
pp. 196-221 ◽  
Author(s):  
Jin Zhang ◽  
Xin Cai ◽  
Taowen Le ◽  
Wei Fei ◽  
Feicheng Ma
Keyword(s):  
Information Retrieval ◽  
Information Management ◽  
Search Engine ◽  
Information Search ◽  
Search Engines ◽  
Internet Technology ◽  
Global Information ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems

This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.

User Models for Adaptive Information Retrieval on the Web

2012 ◽  
Vol 3 (3) ◽  
pp. 1-19
Author(s):  
Max Chevalier ◽  
Christine Julien ◽  
Chantal Soulé-Dupuy
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
User Profile ◽  
User Model ◽  
User Models ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Adaptive Information Retrieval ◽  
The Web

Searching information can be realized thanks to specific tools called Information Retrieval Systems IRS (also called “search engines”). To provide more accurate results to users, most of such systems offer personalization features. To do this, each system models a user in order to adapt search results that will be displayed. In a multi-application context (e.g., when using several search engines for a unique query), personalization techniques can be considered as limited because the user model (also called profile) is incomplete since it does not exploit actions/queries coming from other search engines. So, sharing user models between several search engines is a challenge in order to provide more efficient personalization techniques. A semantic architecture for user profile interoperability is proposed to reach this goal. This architecture is also important because it can be used in many other contexts to share various resources models, for instance a document model, between applications. It is also ensuring the possibility for every system to keep its own representation of each resource while providing a solution to easily share it.

Experiments with Language-based Aids in Information Retrieval Systems

1988 ◽  
Vol 11 (1-2) ◽  
pp. 33-46 ◽  
Author(s):  
Tove Fjeldvig ◽  
Anne Golden
Keyword(s):  
Information Retrieval ◽  
Text Retrieval ◽  
Considerable Improvement ◽  
Controlled Experiments ◽  
Compound Words ◽  
Search Results ◽  
Retrieval Systems ◽  
Complete Search ◽  
Information Retrieval Systems ◽  
Search Quality

The fact that a lexeme can appear in various forms causes problems in information retrieval. As a solution to this problem, we have developed methods for automatic root lemmatization, automatic truncation and automatic splitting of compound words. All the methods have as their basis a set of rules which contain information regarding inflected and derived forms of words – and not a dictionary. The methods have been tested on several collections of texts, and have produced very good results. By controlled experiments in text retrieval, we have studied the effects on search results. These results show that both the method of automatic root lemmatization and the method of automatic truncation make a considerable improvement on search quality. The experiments with splitting of compound words did not give quite the same improvement, however, but all the same this experiment showed that such a method could contribute to a richer and more complete search request.

Search Variability and Interlibrary Services

Infolib ◽  
2020 ◽  
Vol 24 (4) ◽  
pp. 16-21
Author(s):  
Irina Krasilnikova ◽  
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
Electronic Resources ◽  
Scientific Publications ◽  
Retrieval Systems ◽  
Electronic Catalogs ◽  
Information Retrieval Systems ◽  
Modern Information ◽  
Foreign Materials ◽  
Search For Information

The urgency of the problem is associated with an increase in the number of electronic resources in many information and library institutions, the need to search for information from any sources, including external ones, the provision of documents from a group of funds (corporations), the presence of electronic catalogs and search systems. Finding information from catalogs and other search engines has always preceded the execution of orders in the interlibrary service. Borrowing and using documents from different collections (provision of interlibrary services) is possible only if there is up-to-date metadata of modern information retrieval systems (ISS). The purpose of the article is to summarize the results of studying several types of search engines. At the same time, attention was drawn to new scientific publications on the topic under study. An analysis of domestic and foreign materials on the options for searching for information is presented, which is very necessary for users, including those who are remote in the provision of interlibrary services.

Spatio-Temporal Based Personalization for Mobile Search

2012 ◽  
pp. 386-409 ◽  
Author(s):  
Ourdia Bouidghaghen ◽  
Lynda Tamine
Keyword(s):  
Information Retrieval ◽  
Contextual Information ◽  
The Internet ◽  
Search Results ◽  
Search Result ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Spatio Temporal ◽  
Mobile Context

The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user’s search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.

Search Engine Performance Comparisons

2011 ◽  
pp. 148-164
Author(s):  
Xiannong Meng ◽  
Song Xing
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Engine Performance ◽  
Process Time ◽  
Average Process ◽  
Search Results ◽  
Time Average ◽  
Evaluation Techniques ◽  
Average User

This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The search engines involved in the study include the Microsoft Search Engine (MSE) when it was in its beta test stage, AllTheWeb, and Yahoo. In a few comparisons, other search engines such as Google, Vivisimo are also included. The study collects statistics such as the average user response time, average process time for a query reported by MSE, as well as the number of pages relevant to a query reported by all search engines involved. The project also studies the quality of search results generated by MSE and other search engines using RankPower as the metric. We found MSE performs well in speed and diversity of the query results, while weaker in other statistics, compared to some other leading search engines. The contribution of this chapter is to review the performance evaluation techniques for search engines and use different measures to assess and compare the quality of different search engines, especially MSE.

scholarly journals Large-scale image search with text for information retrieval

2021 ◽  
Vol 4 (1) ◽  
pp. 87-89
Author(s):  
Janardan Bhatta
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Image Feature ◽  
Image Search ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Text Features ◽  
Text Query

Searching images in a large database is a major requirement in Information Retrieval Systems. Expecting image search results based on a text query is a challenging task. In this paper, we leverage the power of Computer Vision and Natural Language Processing in Distributed Machines to lower the latency of search results. Image pixel features are computed based on contrastive loss function for image search. Text features are computed based on the Attention Mechanism for text search. These features are aligned together preserving the information in each text and image feature. Previously, the approach was tested only in multilingual models. However, we have tested it in image-text dataset and it enabled us to search in any form of text or images with high accuracy.

On The Reuse of Past Searches in Information Retrieval

2015 ◽  
Vol 6 (2) ◽  
pp. 72-92 ◽  
Author(s):  
Claudio Gutiérrez-Soto ◽  
Gilles Hubert
Keyword(s):  
Monte Carlo ◽  
Information Retrieval ◽  
Search Results ◽  
Monte Carlo Algorithms ◽  
Log Files ◽  
Retrieval Systems ◽  
Cosine Measure ◽  
Information Retrieval Systems ◽  
Single User ◽  
Baseline Approach

When using information retrieval systems, information related to searches is typically stored in files, which are well known as log files. By contrast, past search results of previously submitted queries are ignored most of the time. Nevertheless, past search results can be profitable for new searches. Some approaches in Information Retrieval exploit the previous searches in a customizable way for a single user. On the contrary, approaches that deal with past searches collectively are less common. This paper deals with such an approach, by using past results of similar past queries submitted by other users, to build the answers for new submitted queries. It proposes two Monte Carlo algorithms to build the result for a new query by selecting relevant documents associated to the most similar past query. Experiments were carried out to evaluate the effectiveness of the proposed algorithms using several dataset variants. These algorithms were also compared with the baseline approach based on the cosine measure, from which they reuse past results. Simulated datasets were designed for the experiments, following the Cranfield paradigm, well established in the Information Retrieval domain. The empirical results show the interest of our approach.

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