An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google, Yahoo, Msn and Hakia

Tümer, Duygu; Shah, Mohammad Ahmed; Bitirim, Yıltan

doi:10.1109/icimp.2009.16

Cited by 60 publications

(38 citation statements)

References 8 publications

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“…Hakia, unlike other search engines, does not track the search history or monitors the search behavior. Hakia provides user privacy and place the cookie with user permission [4]. It utilizes work on search engine instead of personalized history.…”

Section: B Recommendationmentioning

confidence: 99%

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Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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Abstract-Semantic Web promises to add metadata to web content to make it understandable to computers. Search is the most widely used activity on web. Semantic search engines have already changed the way we search the data on web. Uren. V, Yuanguilei Uren et al., proposed requirement space pyramid arguing that iterative and exploratory search modes are important to the usability of search engines. It identified the types of semantic queries the users need to make, the issues concerning the search development and the problems intrinsic to semantic search in particular. We have extensively examined the semantic search engines and have done broad survey to analyse the semantic search engines. Comparative analysis of the semantic search engines have been done on the basis of factors cited in the pyramid. The research provides deep understanding of five main semantic search engines based on comparative analysis that may help for future work for semantic web in general and for semantic search engines in particular.Index Terms-Semantic web, semantic search engine, requirement space pyramid. I. INTRODUCTIONSemantic Web [1], the future of web, promises to provide content that will be understandable to computer. Search is the most important and popular applications of the web. Semantic search [2] is one of the most significant applications of semantic web. Recently a number of semantic search engines have been introduced. Semantic Search engine is a tool that produces precise results to user queries by retrieving data semantically [3]. The purpose of this paper is to compare semantic search engines along with their salient characteristic approaches. This paper focuses mainly on five semantic search engines including Hakia, Sensebot, Powerset, Lexxe and Cognition. There are many benchmarks and standards used in every field and computer science is not an exception. Google and other search engines have revolutionized the world with their services but with the idea of semantic search, the rise of semantic search engines has changed the requirements and issues related with search engines. A pyramid scheme has been used as model for discussion to present the requirements, issues and challenges for semantic search engines. The remaining paper has been formatted as follows that related work have discussed in section II. Comparative evaluation of the semantic search engines on the basis of the pyramid has been described in Manuscript received November 8, 2012; revised January 28, 2013 section III. The semantic search engines along with their GUI's have been discussed in section IV while finally the concluding remarks include the issues and challenges of semantic search engines. II. RELATED WORKSSemantic search engine is new field and realtively less work has been done in this domain. Hakia has been compared with google, yahoo and msn in [4] and that of Hakia with dogpile. A relation-based page rank algorithm has been proposed to be used as semantic web search engine. In this work, relevance is measured as probability of finding con...

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Section: B Recommendationmentioning

confidence: 99%

“…The challenge is to reduce these possibilities down to few dozens and fuse the possible knowledge into different ontologies together. This is accomplished using commercial ontology [4]. Sensebot takes semantic result from Google, Yahoo and Live and summarizes them into one concise digest on the topic of query [16].…”

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confidence: 99%

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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“…In the experiment, 2 different sets of queries (as 17 queries and 5 queries) were defined and used. One of the findings is that the search engine Arama was the best both in terms of mean precision ratio and in terms of mean normalized recall ratio; Tümer et al selected 3 keyword-based search engines (Google, Yahoo, and MSN) and a semantic-search engine (Hakia) and evaluated their semantic search performance [6]. Although Yahoo was the best in terms of mean precision ratio and Google was the best in terms of mean normalized recall ratio, semantic search performances of 3 keyword-based and 1 semantic search engines were not good; Zhang and Fei studied to find out the effect of several search features of the search engines, Yahoo, Google, and Live Search, on information retrieval performance [7].…”

Section: Introductionmentioning

confidence: 99%

A comparative evaluation of popular search engines on finding Turkish documents for a specific time period

Bitirim

Görür

2017

Teh. vjesn.

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A COMPARATIVE EVALUATION OF POPULAR SEARCH ENGINES ON FINDING TURKISH DOCUMENTS FOR A SPECIFIC TIME PERIOD Yıltan Bitirim, Abdül Kadir GörürOriginal scientific paper This study evaluates the popular search engines, Google, Yahoo, Bing, and Ask, on finding Turkish documents by comparing their current performances with their performances measured six years ago. Furthermore, the study reveals the current information retrieval effectiveness of the search engines. First of all, the Turkish queries were run on the search engines separately. Each retrieved document was classified and precision ratios were calculated at various cut-off points for each query and engine pair. Afterwards, these ratios were compared with the six years ago ratios for the evaluations. Besides the descriptive statistics, Mann-Whitney U and Kruskal-Wallis H statistical tests were used in order to find out statistically significant differences. All search engines, except Google, have better performance today. Bing has the most increased performance compared to six years ago. Nowadays: Yahoo has the highest mean precision ratios at various cut-off points; all search engines have their highest mean precision ratios at cut-off point 5; dead links were encountered in Google, Bing, and Ask; and repeated documents were encountered in Google and Yahoo. Keywords: information retrieval; performance evaluation; search engine; Turkish Usporedna evaluacija popularnih mehanizama za pretraživanje u pronalaženju turskih dokumenata određenog vremenskog razdobljaIzvorni znastveni članak U ovom se istraživanju ocjenjuju popularni mehanizmi za pretraživanje, Google, Yahoo, Bing, i Ask, pri traženju turskih dokumenata usporedbom njihovog sadašnjeg rada s radom izmjerenim prije šest godina. Nadalje, istraživanje pokazuje sadašnju učinkovitost mehanizama u pronalaženju podataka. Najprije su učinjeni upiti za turske riječi odvojeno na svakom mehanizmu. Svaki pronađeni dokument je klasificiran, a izračunati su omjeri točnosti na raznim cut-off točkama za svaki upit i svaki mehanizam. Zatim su ti omjeri uspoređeni s onima od prije šest godina zbog procjene. Pored opisne statistike, korišteni su Mann-Whitney U i Kruskal-Wallis H statistički testovi kako bi se pronašle statistički značajne razlike. Svi mehanizmi za ispitivanje osim Google-a danas su učinkovitiji. Bing je njviše napredovao u odnosu na prije šest godina. Danas Yahoo ima najviše prosječne omjere točnosti u raznim cutoff točkama. Svi mehanizmi za pretraživanje imaju najviše prosječne omjere točnosti u cut-off točki 5; ugašene veze (dead links) su nađene u Google-u, Bingu i Asku, a ponovljeni dokumenti u Google-u i Yahoo-u.

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“…One of the most common strategies is the use of a search engine to classify web files that contain interesting material. The most known and useful in terms of the precision in the last years is Google [14], the most popular search engine in the world, even though most of them work in the same way, and only differ in classification and relevance criteria [15]. Google was designed with the purpose of giving priority to the amount of visits a page has, and in this way it assigns priority to each page [16].…”

Section: B Contents Searchmentioning

confidence: 99%

Exploring the Relevance of Search Engines: An Overview of Google as a Case Study

Beltrán-Alfonso¹,

Torres-Tautiva²,

Gaona-García³

et al. 2017

IJIMAI

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-The huge amount of data on the Internet and the diverse list of strategies used to try to link this information with relevant searches through Linked Data have generated a revolution in data treatment and its representation. Nevertheless, the conventional search engines like Google are kept as strategies with good reception to do search processes. The following article presents a study of the development and evolution of search engines, more specifically, to analyze the relevance of findings based on the number of results displayed in paging systems with Google as a case study. Finally, it is intended to contribute to indexing criteria in search results, based on an approach to Semantic Web as a stage in the evolution of the Web.

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An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google, Yahoo, Msn and Hakia

Cited by 60 publications

References 8 publications

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

A comparative evaluation of popular search engines on finding Turkish documents for a specific time period

Exploring the Relevance of Search Engines: An Overview of Google as a Case Study

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