Quality Information Retrieval for the World Wide Web

Kc, Milly; Hagenbuchner, Markus; Tsoi, Ah Chung

doi:10.1109/wiiat.2008.378

Cited by 7 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Milly et al [12] introduced some quality evaluation and assessment methods to assess the quality of web pages. The proposed quality evaluation mechanisms were based on a set of quality criteria which were extracted from a targeted user survey.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Evaluating the Performance of Semantic Service Search Engine based-on SVM

Gopal¹,

Bharati²

2013

IJCA

View full text Add to dashboard Cite

Semantic service search engine is designed for DE, to provide semantic search support. Digital ecosystem (DE) is comprised of heterogeneous and distributed species which can play the dual role of service provider and service requester. DE needs to provide a reliable and trustworthy link between service providers and service requesters. So, semantic service search engine is a conceptual framework of service-ontology-based system which delivers semantic search support to DE. This paper is intended to measure the performance of the semantic service search engine based-on Support Vector Machine (SVM). The performance is evaluated through six performance indicators of information retrieval (IR). They are Precision, Recall, Mean average precision, Harmonic mean, Fallout rate and Mean Reciprocal Rank. The conclusion to this evaluation and future works are provided in the final section. General TermsQuality of service (QoS) evaluation, semantic service search engine, support vector machine, information retrieval, performance indicators.

show abstract

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Evaluating the Performance of Semantic Service Search Engine based-on SVM

Gopal¹,

Bharati²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Recent work by Kc et al 28 proposed a ranking scheme on the basis of users' perceived quality of documents on the Web. The atomization of the quality assessment procedure 28 was made possible through an algorithmic interpretation of documented cognitive processes that lead to the assessment of perceived quality of a Web document.…”

Section: Examples Of Further Applicationsmentioning

confidence: 99%

“…Query keywords were generated from the English dictionary. In each test, in addition to calculating the CLR and offset values, all the HTML pages contained in RS 1 and RS 2 were downloaded, and the quality score of each of these HTML pages was calculated using the method described in Kc et al 28. A higher quality score indicates a higher quality of the Web page.…”

Section: Examples Of Further Applicationsmentioning

confidence: 99%

“…Even then, Google only has partial information about the contents of the Web. For example, it is known that Google indexed 25 billion Web pages in 2006, whereas the estimated size of the Web had already exceeded 200 billion in 2005 1 and reached 447.98 billion by early 2007 2. As a consequence, any attempt to search for information on the Web will only yield the results covered by the search service provider.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated functional testing of online search services

Zhi

Zhang

Hagenbuchner

et al. 2010

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

SUMMARYSearch services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality.

show abstract

“…Ontology can be used for structuring and Filtering the information repository (in our case, a URL Queue i. e. Frontier). Ontology is the way of Domain knowledge representation [6], and we can use ontology engineering for filtering the URLs from the Frontier. It may be used as a conceptual framework to developers.…”

Section: Intelligent Crawler Schema 41 Domain Engineering and Web Crmentioning

confidence: 99%

Intelligent web crawler

Dahiwale

Mokhade

Raghuwanshi

2010

Proceedings of the International Conference and Workshop on Emerging Trends in Technology

View full text Add to dashboard Cite

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant.Web crawlers are one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency. This paper, introduces an intelligent web crawler that uses an ontological engineering concepts for improving its crawling performance. Intelligent crawler estimates the best path for crawling. This is the first crawler that acts intelligently without any relevance feedback or training.

show abstract

Quality Information Retrieval for the World Wide Web

Cited by 7 publications

References 8 publications

Evaluating the Performance of Semantic Service Search Engine based-on SVM

Evaluating the Performance of Semantic Service Search Engine based-on SVM

Automated functional testing of online search services

Intelligent web crawler

Contact Info

Product

Resources

About