Table 5 - uploaded by Muhammad Zeeshan Jhandir
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Source publication
The Internet of Things (IoT) and Big Data are among the most popular emerging fields of computer science today. IoT devices are creating an enormous amount of data daily on a different scale; hence, search engines must meet the requirements of rapid ingestion and processing followed by accurate and fast extraction. Researchers and students from the...
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Citations
... Cheng et al. (2016, p. 509) studied "a set of comprehensive empirical studies to explore the effects of multiple query evidences on largescale social image search and found that (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and EL 37,1 visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results' qualitymore complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation". Ali et al. (2017Ali et al. ( , p. 1203), on evaluating the retrieval effectiveness by a sustainable rank list between Yandex and Bing, revealed that "Yandex performs better than Bing for the given information need. The results depict that Yandex fetched more relevant documents than Bing and gave these fetched documents more appropriate rank". ...
Purpose
The purpose of this study is to assess the retrieval performance of three search engines, i.e. Google, Yahoo and Bing for navigational queries using two important retrieval measures, i.e. precision and relative recall in the field of life science and biomedicine.
Design/methodology/approach
Top three search engines namely Google, Yahoo and Bing were selected on the basis of their ranking as per Alexa, an analytical tool that provides ranking of global websites. Furthermore, the scope of study was confined to those search engines having interface in English. Clarivate Analytics' Web of Science was used for the extraction of navigational queries in the field of life science and biomedicine. Navigational queries (classified as one-word, two-word and three-word queries) were extracted from the keywords of the papers representing the top 100 contributing authors in the select field. Keywords were also checked for the duplication. Two important evaluation parameters, i.e. precision and relative recall were used to calculate the performance of search engines on the navigational queries.
Findings
The mean precision for Google scores high (2.30) followed by Yahoo (2.29) and Bing (1.68), while mean relative recall also scores high for Google (0.36) followed by Yahoo (0.33) and Bing (0.31) respectively.
Research limitations/implications
The study is of great help to the researchers and academia in determining the retrieval efficiency of Google, Yahoo and Bing in terms of navigational query execution in the field of life science and biomedicine. The study can help users to focus on various search processes and the query structuring and its execution across the select search engines for achieving desired result list in a professional search environment. The study can also act as a ready reference source for exploring navigational queries and how these queries can be managed in the context of information retrieval process. It will also help to showcase the retrieval efficiency of various search engines on the basis of subject diversity (life science and biomedicine) highlighting the same in terms of query intention.
Originality/value
Though many studies have been conducted highlighting the retrieval efficiency of search engines the current work is the first of its kind to study the retrieval effectiveness of Google, Yahoo and Bing on navigational queries in the field of life science and biomedicine. The study will help in understanding various methods and approaches to be adopted by the users for the navigational query execution across a professional search environment, i.e. “life science and biomedicine”
... Cheng et al. (2016, p. 509) studied "a set of comprehensive empirical studies to explore the effects of multiple query evidences on largescale social image search and found that (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and EL 37,1 visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results' qualitymore complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation". Ali et al. (2017Ali et al. ( , p. 1203), on evaluating the retrieval effectiveness by a sustainable rank list between Yandex and Bing, revealed that "Yandex performs better than Bing for the given information need. The results depict that Yandex fetched more relevant documents than Bing and gave these fetched documents more appropriate rank". ...
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.
High-definition (HD) video material for science education is always welcomed in elementary or secondary schools. It not only helps teachers to convey concepts but also impresses pupils. However, it is difficult for teachers to find customized HD video material besides the unified compact disks companied with teaching reference books. The teacher-customized HD video material has two requirements: (1) short (less than 3–5 min) and (2) fit to the current knowledge map. One potential pool of HD video material is HD documentaries. Nevertheless, a HD documentary is usually very long (around 45–90 min) and has its own agenda. In this chapter, a prototype system named SEARCH (Seeking Excerpted educAtional Resource in Collections of High-definition documentaries) was proposed to help teachers find short material for science education in long and high-definition documentary videos. SEARCH consists of three vital components: knowledge map extraction, documentary subtitle tagging, and hit re-ranking. The knowledge map extraction component is to extract knowledge map from teaching reference books, assigning different weights to concepts in different positions in a knowledge map. The documentary subtitle tagging component is to tag subtitles with concepts extracted in the former component, using a deep learning technique: long short-term memory. The hit re-ranking component is to re-rank multiple hits returned by scanning tags, according to different demands, such as relevance first or most viewed first. Preliminary results indicate that it can facilitate teachers to prepare their courses.
