Model for Calculating the Rank of a Web Page

Transcription

1 Model for Calculating the Rank of a Web Page Doru Anastasiu Popescu Faculty of Mathematics and Computer Science University of Piteşti, Romania dopopan@yahoo.com Abstract In the context of using the information from the Internet in all areas of activity, the mechanism of finding web pages that contain information about a particular subject has a very important role. In this article we present a way of ranking web pages in a web application that can be used by various algorithms in a search engine. Ranking web pages will be made calculating the rank of a web page according to certain design features. Key words: Page Rank, HITS, Search Engine, Web Application, HTML, XML Introduction The increasingly bigger quantity of information we have access to from the Internet creates difficulties in the search of precise information. Word Wide Web database is characterized by three fundamental things: content, structure and links that exist between documents. The documents content is diverse, but the most common are the texts. The document structure can be organized as a tree using HTML and XML tags; efforts in this area have focused on automatic extraction of DOM structures (document object model) from documents. The links allow connections between documents, on the same location or from different locations. All the documents from WWW are viewed using web pages. All web pages form a chaotic structure, from which information can be extracted with difficulty; this operation is simplified by search engines. They use various algorithms to provide users with lists of web pages with related information. Among these algorithms, two have proven time benefits; these are the PageRank and HITS. Models of software used to search information based on queries are presented in [6.6], [6.7] and [6.2]. PageRank algorithm, also used by Google's search engine, calculates the importance of web pages by taking into account not only the number of pages that have a link to it, but also the importance of the pages that access it. HITS algorithm, also used by Clever search engine, take into account the notion of authority, namely the major websites in terms of content and the hubs, which are pages that serve as indexes (lists of resources that directs users to the authorities). A comparing study of these two algorithms has been made in [6.3], [6.9], [6.6]. This article s purpose is to offer a list of web pages from a web application, ordered by their importance, which can be used in the process of searching information using queries by the search engine s algorithms. The importance in the application is given by the web page s rank for the search operation, which will be defined taking into account its content and its accessing from the outside or the inside of the application.

2 The 8 th International Conference on Virtual Learning ICVL The constructive characteristics taken into account when determining a web page s rank In the operation of searching information using a search engine, words are the most often used. Some of them have a special meaning: names of images, URL addresses, names of web pages, etc. In order to simplify the search methods, some algorithms use certain values for the web pages which are named web page s rank. This value can be calculated in different ways, many articles approaching this subject. The method a web page s rank we will present is first of all related to the web application that contains the page and, second of all, to the number of links which access the page from the inside of the application. This way, we propose to the search engines to perform searches on web applications which have their web pages sorted descending after the value of their rank. Next, we will consider a web application WA composed from the web pages P = {p 1, p 2,, p n }. When calculating the rank of a web page from P in relation with WA, we will use the next categories of words: C 1 words from the web page s title C 2 words from the content of the web page, written with bold C 3 words from the content of the web page, written with italic C 4 words from the content of the web page, written underlined C 5 character sequences which represent a URL address C 6 character sequences which represent names of files C 7 words used in tables When searching information, the importance of the web page is also very relevant, for example the number of web pages which access it through links. A method of calculating the rank of a web page from this point of view is presented in [6.10]. Words from every category have a certain weight in calculating the rank of a page. Those weights will be noted with v 1, v 2,..., v 7. Definition 1 Let p i be a web page from P. By noting with t j the number of words from WA which are from the C j category, with j from the set {1, 2,...,7}, the rank of the p i page is the next one: Rank WA (p i ) = (a i1 v 1 +a i2 v a i7 v 7 ) / (t 1 v 1 +t 2 v t 7 v 7 ) where a ij represents the number of words from p i which are from the C j category, with j from the set {1, 2,...,7}. Remark 1 For any j from the set {1, 2,...,7} we have a 1j + a 2j a nj = t j. Definition 2 Let p i be a web page from P. By noting with b i the number of links from WA s pages which access p i, the navigation rank of the page p i related to WA is the next one: RankNav WA (p i ) = (1+b i ) /(1+L), where L is the number of links from all the pages from WA. Remark 2 If WA has n=1 and p 1 does not contain links to itself, than RankNav WA (p i ) = Rank WA (p i ) / (1+L).

3 56 University of Bucharest, Faculty of Psychology and Educational Sciences Definition 3 Let p i be a web page from P. We define the rank of p i web page as being the number: Rank(p i ) = Rank WA (p i ) RankNav WA (p i ). Case study We will next consider a web application WA which contains 3 web pages P = {p 1, p 2, p 3 }, composed of HTML tags. The navigation tree in WA (as it is defined in [6.4] and [6.5]) is the next one: p 1 p 3 p 2 The web pages together with their source code are the next ones: Name html file p1.html p2.html Web page source <HTML> <HEAD> <TITLE>Butterfly Page</TITLE> </HEAD> <BODY BGCOLOR=silver> <B>Filfizon</B> butterfly <IMG SRC="butterfly.jpeg"> <I>flying</I> from <B>flower</B> to <U>flower</U> <a href="p2.html">my friend</a> <a href="p3.html">information</a> </BODY> </HTML> <HTML> <HEAD> <TITLE>Butterfly Page</TITLE> </HEAD> <BODY BGCOLOR=silver> <p>red butterfly </p> <IMG SRC="RedButterfly.jpg"> flying from flower to <U>flower</U> </BODY> </HTML> Web page

4 The 8 th International Conference on Virtual Learning ICVL p3.html <HTML> <HEAD> <TITLE>Info Page</TITLE> </HEAD> <BODY BGCOLOR=silver> <TABLE border="2"> <TD><B> Name </B> </TD> <TD><B> Picture </B> </TD> </TR> <TD>Red Butterfly</TD> <TD><IMG SRC="RedButterfly.jpg"></TD> <TD> <TD>Filfizon</TD> <TD><IMG SRC="butterfly.jpeg"></TD> </TABLE> </BODY> </HTML> Table 1 To calculate the rank of each web page from the web application we will use the same weight, more exactly v1=v2=...=v7=1. By analysing the source code of the three web pages we obtain: t 1 =2+2+2=6 (the number of words from the title of WA web pages); t 2 =2+0+2=4 (the number of words from the content of WA web pages, written with bold); t 3 =1+0+0=1 (the number of words from the content of WA web pages, written with italic); t 4 =1+1+0=2 (the number of words from the content of WA web pages, written underlined); t 5 =2+0+0=2 (the number of character sequences which represent a URL address in the web pages of WA); t 6 =3+1+2=6 (the number of files name from the source code of the web pages from WA); t 7 =0+0+5=5 (the number of words used in the tables of WA web pages). Thus, we obtain: t 1 v 1 +t 2 v t 7 v 7 = =22. By using the source code of the web pages in Table 1, we obtain the data in Table 2. Name html file p1.htm l p2.htm l p3.htm l Rank WA RankNav WA Rank ( )/22 =11/22=1/2=0.5 ( )/22 =4/22=2/11= ( )/22 =1/2=0.5 (1+2) /(1+2)=1 0.5 (1+0) /(1+2)=1/3 = (1+0) /(1+2)=1/3 = Table According to the data in Table 2, the descending order of the ranks gives us the hierarchy: p1, p3, p2 which can be used by the search engine algorithms.

5 58 University of Bucharest, Faculty of Psychology and Educational Sciences Algorithm for determining the list of the web pages from a web application depending on rank According to those presented in Section 3 we have the following algorithm: Input data - Path (address) where the web application is, in a string s. - The weights v 1, v 2,..., v 7. Output data The list of the addresses (including the name) of the web pages in descending order of the rank. Step 1. Initialization of all the variables used in Section 3 with the number 0. Step 2. Navigation tree of pages in WA is searched (in width or depth), starting from the root located in the page from the address in s and for any current web page (with the order number i, located at Adr i ) the source code is read and the next variables are updated: - t 1, t 2,..., t 7 - b i, a i1, a i2,..., a i7 (there is no need to use a two-dimensional array of 8 columns, we can use a one-dimensional array with 8 components, as the values should not be withheld from a web page to another) - L Step 3. Using the values from step 2, there are calculated: - Sum=t 1 v 1 +t 2 v t 7 v 7 - Rank i Step 4. After calculating the rank for all the web pages in WA, we get the pairs: (Rank i, Adr i ), i from {1, 2,...,n}. Step 5. The pairs (Rank 1, Adr 1 ), (Rank 2, Adr 2 ),..., (Rank 2, Adr 2 ) are ordered descending after Rank. Conclusions and future work We want to implement it in Java the model presented in the previous sections in order to obtain an efficient application for determining the list of the web pages from a web application given by its address. Then we want to accomplish a detailed study to identify the most appropriate values for the input data which should be used by the algorithm from Section 4, while we look for new factors to be taken into account in determining the rank of a website, including external web applications, such as those presented in [6.8] and [6.11]. References A. K. Sharma, Neelam Duhan (2011), Optimization of Search Results with Duplicate Page Elimination using Usage Data, ACEEE International Journal on Network Security, Vol. 02 No. 02, Apr 2011, Pag Chutisant Kerdvibulvech (2012), A New Method for Web Development using Search Engine Optimization, International Journal of Computer Science and Busness Informatics (IJCSBI), Vol. 3 No. 1, JULY 2013, Pag Nidhi Grover, Ritika Wason (2010), A Comparative Analysis of Web Page Ranking Algorithms, International Journal on Computer Science and Engineering (IJCSE), Vol. 02 No. 08, 2010, Pag

6 The 8 th International Conference on Virtual Learning ICVL Doru Anastasiu Popescu, (2009), Testing web application navigation based on component complexity, Buletin Ştiinţific, Universitatea din Piteşti, Seria Matematică şi Informatică, Nr. 15, pg Doru Anastasiu Popescu, (2011), Measuring the Quality of the Navigation in Web Sites Using the Cloning Relation, Analele Universitatii Spiru Haret, Seria Matematica-Informatica, Vol. 7, No.1, 2011, pag Dilip Kumar Sharma, A. K. Sharma (2012), Comparative Analysis Of Pagerank And Algorithms, International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 8, October 2012, Pag Laxmi Choudhary and Bhawani Shankar Burdak (2012), Role of Ranking Algorithms for Information Retrieval, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3 No. 4, July 2012, Pag Parveen Rani, Er. Sukhpreet Singh (2013), An Offline SEO (Search Engine Optimization) Based Algorithm to Calculate Web Page Rank According to Different Parameters, International Journal of Computers & Technology, Vol. 9 No. 1, pag , July 15, Shruti Aggarwal, Parneet Kaur (2013), Comparative study of Page Ranking Algorithms of Web Mining, International Journal of Computers Trends and Technology (IJCTI), Vol. 4 No. 4, pag , April, Conference Proceedings: Jon M. Kleinberg (1998), Authoritative Sources in a Hyperlinked Environment, ACM-SIAM Symposium on Discrete Algorithms. Conference Proceedings: Neelam Duhan, A.K. Sharma, Komal Kumar Bhatia (2009), Page Ranking Algorithms: A Survey, IEEE International Advance Computing Conference (IACC 2009), Patiala, India, 6-7 March 2009.