Key web search algorithm based on service ontology

2018 ◽  
pp. 1-6
Author(s):  
Yang Fan
Keyword(s):  
Web Search ◽  
Search Algorithm ◽  
Service Ontology

scholarly journals An Efficient Approach for Web Indexing of Big Data through Hyperlinks in Web Crawling

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
R. Suganya Devi ◽  
D. Manjula ◽  
R. K. Siddharth
Keyword(s):  
Big Data ◽  
World Wide ◽  
Web Search ◽  
Search Algorithm ◽  
Source Code ◽  
Web Crawling ◽  
Uniform Resource Locator ◽  
Web Documents ◽  
The World ◽  
New Challenges

Web Crawling has acquired tremendous significance in recent times and it is aptly associated with the substantial development of the World Wide Web. Web Search Engines face new challenges due to the availability of vast amounts of web documents, thus making the retrieved results less applicable to the analysers. However, recently, Web Crawling solely focuses on obtaining the links of the corresponding documents. Today, there exist various algorithms and software which are used to crawl links from the web which has to be further processed for future use, thereby increasing the overload of the analyser. This paper concentrates on crawling the links and retrieving all information associated with them to facilitate easy processing for other uses. In this paper, firstly the links are crawled from the specified uniform resource locator (URL) using a modified version of Depth First Search Algorithm which allows for complete hierarchical scanning of corresponding web links. The links are then accessed via the source code and its metadata such as title, keywords, and description are extracted. This content is very essential for any type of analyser work to be carried on the Big Data obtained as a result of Web Crawling.

scholarly journals Health on the Net: Do Website Searches Return Reliable Health Information on Hemorrhoids and Their Treatment?

2017 ◽  
Vol 102 (5-6) ◽  
pp. 216-221 ◽  
Author(s):  
Matthew W. Ng ◽  
Riley Smith ◽  
Nilmini Wickramesinghe ◽  
Philip J. Smart ◽  
Nathan Lawrentschuk
Keyword(s):  
Health Information ◽  
Web Search ◽  
Search Algorithm ◽  
Treatment Options ◽  
World Health ◽  
The Internet ◽  
Quality Information ◽  
High Quality ◽  
High Quality Information ◽  
Google Search

Objective: To analyze the quality of health information on the Internet on hemorrhoids across 5 Western languages and perform a comparative analysis of website sponsors. Summary of background data: Hemorrhoids are a common condition affecting the hemorrhoid cushions of the anal canal. Many treatment options are available. Information on the Internet on hemorrhoids is considered variable, but there is little data analysis to support this. The World Health Organization's Health On the Net (HON) accredits medical and health websites based on a code of conduct and publishes a toolbar that aids identification of such accredited websites. Methods: Using the Google search engine (http://www.google.com, Google, Mountain View, California), searches were performed using 11 keywords related to hemorrhoids in English, French, German, Italian, and Spanish. Health On the Net accreditation was determined to assess quality website information. The first 150 websites in each language had their adherence to the HON principles analyzed, and English websites were analyzed to determine sponsorship source. Results: Of the 8250 websites analysed, 586 (7.1%) were found to HON-accredited. The rate of HON accreditation ranged from 2.0% (piles) to 10.0% (hemorrhoids), with higher-ranking results having higher rates of HON accreditation (P < 0.001). Conclusion: There is a paucity of high-quality information on the Internet; however, the Google search algorithm prioritizes high-quality information in its web search results.

scholarly journals Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search

2018 ◽  
Author(s):  
Tao Zhuang ◽  
Wenwu Ou ◽  
Zhirong Wang
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Mutual Influence ◽  
Search Algorithm ◽  
Probability Estimation ◽  
Sequence Generation ◽  
Optimization Framework ◽  
Ranking Order ◽  
B Test ◽  
Generation Problem

In web search, mutual influences between documents have been studied from the perspective of search result diversification. But the methods in web search is not directly applicable to e-commerce search because of their differences. And little research has been done on the mutual influences between items in e-commerce search. We propose a global optimization framework for mutual influence aware ranking in e-commerce search. Our framework directly optimizes the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two tasks. The first task is mutual influence aware purchase probability estimation. We propose a global feature extension method to incorporate mutual influences into the features of an item. We also use Recurrent Neural Network (RNN) to capture influences related to ranking orders in purchase probability estimation. The second task is to find the best ranking order based on the purchase probability estimations. We treat the second task as a sequence generation problem and solved it using the beam search algorithm. We performed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase in GMV for the search engine over a strong baseline. 

Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion

2014 ◽  
Vol 281 ◽  
pp. 248-264 ◽  
Author(s):  
Carlos Cobos ◽  
Henry Muñoz-Collazos ◽  
Richar Urbano-Muñoz ◽  
Martha Mendoza ◽  
Elizabeth León ◽  
...  
Keyword(s):  
Bayesian Information Criterion ◽  
Web Search ◽  
Search Algorithm ◽  
Information Criterion ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Search Results

ANALYSIS OF WEB SEARCH ALGORITHM HITS

2004 ◽  
Vol 15 (04) ◽  
pp. 649-662 ◽  
Author(s):  
DAWEI HONG ◽  
SHUSHUANG MAN
Keyword(s):  
Web Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Initial Values ◽  
Technical Assumption ◽  
The Stability ◽  
The Web ◽  
Made In

One of the well known Web search algorithms is HITS by Kleinberg [9]. We analyze the stability of HITS, when and how much outputs of HITS depend on initial values chosen by the algorithm. More importantly, we proposed a model for a type of hyperlink structures, which have been frequently observed on the Web, and we prove that in the model a crucial technical assumption made in HITS is satisfied, and accordingly HITS works well.

Suicide Prevention Through Online Gatekeeping Using Search Advertising Techniques

Crisis ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 267-273 ◽  
Author(s):  
Hajime Sueki ◽  
Jiro Ito
Keyword(s):  
Suicidal Ideation ◽  
Suicide Prevention ◽  
Help Seeking ◽  
Service Use ◽  
Web Search ◽  
Suicide Attempts ◽  
Consultation Service ◽  
Search Advertising ◽  
Internet Users ◽  
History Of

Abstract. Background: Nurturing gatekeepers is an effective suicide prevention strategy. Internet-based methods to screen those at high risk of suicide have been developed in recent years but have not been used for online gatekeeping. Aims: A preliminary study was conducted to examine the feasibility and effects of online gatekeeping. Method: Advertisements to promote e-mail psychological consultation service use among Internet users were placed on web pages identified by searches using suicide-related keywords. We replied to all emails received between July and December 2013 and analyzed their contents. Results: A total of 139 consultation service users were analyzed. The mean age was 23.8 years (SD = 9.7), and female users accounted for 80% of the sample. Suicidal ideation was present in 74.1%, and 12.2% had a history of suicide attempts. After consultation, positive changes in mood were observed in 10.8%, 16.5% showed intentions to seek help from new supporters, and 10.1% of all 139 users actually took help-seeking actions. Conclusion: Online gatekeeping to prevent suicide by placing advertisements on web search pages to promote consultation service use among Internet users with suicidal ideation may be feasible.

Sign in / Sign up

Export Citation Format

Share Document