scholarly journals Retrieval Effectiveness of News Search Engines: A Theoretical Framework

2018 ◽  
Vol 180 (38) ◽  
pp. 17-23
Author(s):  
Mohammad Ubaidullah ◽  
Mohd. Kashif
Keyword(s):  
Search Engines ◽  
Theoretical Framework ◽  
Retrieval Effectiveness

scholarly journals A Framework for Evaluating the Retrieval Effectiveness of Search Engines

2012 ◽  
pp. 456-479 ◽  
Author(s):  
Dirk Lewandowski
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Theoretical Framework ◽  
Short Description ◽  
Next Generation ◽  
Reliable Test ◽  
General Design ◽  
Additional Information ◽  
Retrieval Effectiveness

This chapter presents a theoretical framework for evaluating next generation search engines. The author focuses on search engines whose results presentation is enriched with additional information and does not merely present the usual list of “10 blue links,” that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

A Formal Model for Managing Multiple Observation Data in Agriculture

2019 ◽  
Vol 15 (3) ◽  
pp. 79-100 ◽  
Author(s):  
Watanee Jearanaiwongkul ◽  
Frederic Andres ◽  
Chutiporn Anutariya
Keyword(s):  
Search Engines ◽  
Question Answering ◽  
Formal Model ◽  
Theoretical Framework ◽  
Similarity Function ◽  
Observation Data ◽  
Contextual Data ◽  
Rule Based ◽  
Key Characteristics

Nowadays, farmers can search for treatments for their plants using search engines and applications. Most existing works are developed in the form of rule-based question answering platforms. However, an observation could be incorrectly given by the farmer. This work recommends that diseases and treatments must be considered from a set of related observations. Thus, we develop a theoretical framework for systems to manage a farmer's observation data. We investigate and formalize desirable characteristics of such systems. The observation data is attached with a geolocation in which related contextual data is found. The framework is formalized based on algebra, in which required types and functions are identified. Its key characteristics are described by: (1) the defined type called warncons for representing observation data; (2) the similarity function for warncons; and (3) the warncons composition function for composing similar warncons. Finally, we show that the framework helps observation data to become richer and improve advice-finding.

Retrieval effectiveness of image search engines

2019 ◽  
Vol 37 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Aabid Hussain ◽  
Sumeer Gul ◽  
Tariq Ahmad Shah ◽  
Sheikh Shueb
Keyword(s):  
Image Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Keyword Search ◽  
Image Search ◽  
Image Size ◽  
Test Bed ◽  
Content Type ◽  
Retrieval Effectiveness ◽  
Search Feature

Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.

Search engine effectiveness using query classification: a study

2016 ◽  
Vol 40 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Sabha Ali ◽  
Sumeer Gul
Keyword(s):  
Search Engines ◽  
Social Networking Sites ◽  
Information Science ◽  
Research Work ◽  
Training Data ◽  
Library And Information Science ◽  
Data Set ◽  
Content Type ◽  
Retrieval Effectiveness ◽  
General Search

Purpose – The purpose of this paper is to highlight the retrieval effectiveness of search engines taking into consideration both precision and relative recall. Design/methodology/approach – The study is based on search engines that are selected on the basis of Alexa (Actionable Analytics for the web) Rank. Alexa listed top 500 sites, namely, search engines, portals, directories, social networking sites, networking tools, etc. But the scope of study is confined to only general search engines on the basis of language which was confined to English. Therefore only two general search engines are selected for the study . Alexa reports Google.com as the most visited website worldwide and Yahoo.com as the fourth most visited website globally. A total of 15 queries were selected randomly from PG students of Department of Library and Information Science during a period of eight days (from May 8 to May 15, 2014) which are classified manually into navigational, informational and transactional queries. However, queries are largely distributed on the two selected search engines to check their retrieval effectiveness as a training data set in order to define some characteristics of each type. Each query was submitted to the selected search engines which retrieved a large number of results but only the first 30 results were evaluated to limit the study in view of the fact that most of the users usually look up under the first hits of a query. Findings – The study estimated the precision and relative recall of Google and Yahoo. Queries using concepts in the field of Library and Information Science were tested and were divided into navigational queries, informational queries and transactional queries. Results of the study showed that the mean precision of Google was high with (1.10) followed by Yahoo with (0.88). While as, mean relative recall of Google was high with (0.68) followed by Yahoo with (0.31), respectively. Research limitations/implications – The study highlights the retrieval effectiveness of only two search engines. Originality/value – The research work is authentic and does not contain any plagiarized work.

scholarly journals Responsible epistemic technologies: A social-epistemological analysis of autocompleted web search

2016 ◽  
Vol 19 (12) ◽  
pp. 1945-1963 ◽  
Author(s):  
Boaz Miller ◽  
Isaac Record
Keyword(s):  
Search Engines ◽  
Social Epistemology ◽  
Web Search ◽  
Theoretical Framework ◽  
Epistemic Responsibility ◽  
Search Terms ◽  
Epistemological Analysis

Information providing and gathering increasingly involve technologies like search engines, which actively shape their epistemic surroundings. Yet, a satisfying account of the epistemic responsibilities associated with them does not exist. We analyze automatically generated search suggestions from the perspective of social epistemology to illustrate how epistemic responsibilities associated with a technology can be derived and assigned. Drawing on our previously developed theoretical framework that connects responsible epistemic behavior to practicability, we address two questions: first, given the different technological possibilities available to searchers, the search technology, and search providers, who should bear which responsibilities? Second, given the technology’s epistemically relevant features and potential harms, how should search terms be autocompleted? Our analysis reveals that epistemic responsibility lies mostly with search providers, which should eliminate three categories of autosuggestions: those that result from organized attacks, those that perpetuate damaging stereotypes, and those that associate negative characteristics with specific individuals.

Sign in / Sign up

Export Citation Format

Share Document