Avenue U

2.5 Graph-Based ranking algorithm

In the previous sections, all the methods of extracting LSs disregard the natural language’s consistency in the web pages by only considering the discrete terms. The document’s terms are arranged in alphabetic order before applying TF or DF, which totally destroys the linguistic information in web pages. Take an example from Thomas A. Phelps and Robert Wilensk’s paper ^[3],

http://www.cs.berkeley.edu/˜wilensky/NLP.html cannot be located using this query “texttiling wilensky disambiguation subtopic iago” in Google at current time, as shown in Figure2.8, Google only return 4 results with highly related content but none of them have the same URL as required, however it was claimed as successful in January, 2008. Meanwhile it can be returned by Yahoo search with the same query but a different address like this in Figure2.9:

http://www.eecs.berkeley.edu/Faculty/Homepages/wilensky.html/NLP.html?lexical-signature=texttiling+wilensky+disambiguation+subtopic+iago

This shows the fact that 2 different URLs can open the same page. About different URLs binding on the same web page, it is not discussed in this project. It is supposed to be a successful retrieval if document similarity is taken as a measurement, however, the URL matching measurement will clearly put it into a false retrieval.

Figure2.8

Figure2.9

From Figure2.8 and Figure2.9, we can see the un-stable performance from traditional LS generation techniques even when they were as typical samples as in papers years before. The studies on URL and its page content change have been researched before, such as Martin Klein and Michael L. Nelson studied the pages ranging from 1996 to 2007 ^[3], but they are not included in this project. One single page bound by different URL actually happens quite often, taking Binghamton University’s home page as an example, http://www.binghamton.edu/index.html and http://www2.binghamton.edu actually connect to the same page. In chapter 3, section 3.4 shows typical examples in Yahoo news pages that Yahoo changes the same news page’s URL all the time.

One approach of solving such kind of difficulty, which mainly focuses on finding relative or similar web pages rather than locating web pages only by URLs matching, is referenced by automatically generating key words and summarizations for academic papers. They are introduced as having capabilities by reserving both the underlying language information and relatively stable performance, because there are fewer chances to have two documents with the same content but different URLs, and even this happens, Martin and Michael studied the graph-based algorithms and concluded that they actually had the ability to improve the relative/similar web page re-finding/re-location when the original copy is lost ^[3].

Graph-based ranking algorithm is a way of deciding the importance of a vertex within a graph, by taking all text as global information and recursively computing from the entire graph rather than relying only on local vertex-specific information ^[9][10].The basic idea implemented by a graph-based ranking model is a vertex can receive and cast “voting” or “recommendation” to the others ^[12][13]. When one vertex links to another one, it is basically casting a vote to the other in the graph. The higher the number of votes is received by a vertex, the higher the importance of the vertex is taken ^[9]. Figure2.10 (a) is an example showing how it works when a vertex casts all its weight to the other vertices. Figure2.10 (b) is an example a vertex casts 90% of its weight to the others while it keeps 10%. Page-Rank is a typical implementation of this graph-based ranking algorithm. The score of a vertex V_i is defined as:

 ^[11] 2-5

Where d is a damping factor that can be set between 0 and 1. In 2-5, j is the vertex points to i and Out(V_j) is the score delivered from j to i.

    

(a)                                                                      (b)

Figure2.10

Meanwhile, graph-based ranking algorithm can also be split into 2 groups as Figure2.11 shows the combinations: weighted (on edges) graph and un-weighted (on edges) graph, undirected-graph and directed-graph. One group’s condition can be combined with the other group’s condition.

Figure2.11

Because undirected un-weighted (on both edges and vertices) graph does not have actual meaning in this project, it is not discussed. Figure2.10 (a) and (b) are 2 examples of directed graph, with weights on vertex, but without value on the edges. Figure2.12 is an example of undirected weighted (on edges) graph. It is the case that assuming out-degree of a vertex is equal to the in-degree of the vertex, take undirected edges like bi-directions edges ^[9][10].^. The weight from i to j and from j to i are same. The weight’s recursive computation formula:

  ^[9] 2-6

 and  show V_i, V_j and V_k are connected but cannot show any direction among V_i, V_j and V_k. Figure2.13 is an example of directed weighted graph. It is the case that a weight is added according to the direction from one vertex to another. The weight from i to j is w_ij, but the weight from j to i is 0.

Figure2.12

Figure2.13

  ^[9] 2-7

Compared to the un-directed weighted graph’s formula 2-6,  and  in formula 2-7 show the direction between V_j, V_i and V_k, V_j.

posted @ 2009-06-15 10:21 JosephQuinn 阅读(468) | 评论 (0) | 编辑 收藏

2.4 Michal Cutler’s Study on HTML Structure

In 1997, Michal Cutler proposed a method that makes use of structures and hyperlinks of HTML documents to improve the effectiveness of retrieving HTML documents ^[6]. She classified the HTML into categories based on HTML’s tags, such as Title, H1, H2, H3, H4, H5, H6 and so on, and claimed that the terms in different HTML tags have different weight. Based on this idea, a new method for extracting lexical signatures from a web page can use the terms that have the highest weights that are computed with the HTML tag structures taken into consideration ^[6].

It is quite necessary to outline Cutler’s two papers both: “Using the Structure of HTML Documents to Improve Retrieval” ^[6] and “A New Study on Using HTML Structures to Improve Retrieval” ^[7].

First of all, she raised an excellent idea of differentiating the term weights for the different HTML tags. The first paper classified an HTML page into following categories in Table2.1. The detailed specifications and functions of each tag are not listed here in this section. She also mentioned that the tag importance is Anchor > H1 – H2 > H3 – H6 > Strong > Title > Plain Text ^[6].

Table2.1 ^[6]

The second paper classified an HTML page into following categories in Table2.2. The later paper combined all the header tags together but split the strong tags into 2 categories: list and strong. Meanwhile, the second paper considered the text in Title tag and Header tag to be more important than the others rather than Anchor and Header tags are the 2 most important categories in Table2.1 ^[6]

The tags <dl>, <ol> and <ul>’s functions are listed in Appendix A.

Table2.2 ^[6]

The basic ideas behind the two papers’ categories are the same: split the text into different classes based on their tags and then associate them with different weights. When a term appears in more than one class, it only counts terms which appear in higher level. For example, <H1><A href=”http//www.binghamton.edu”>university</A><H1>, ‘university’ is classified into Header category rather than Anchor directory according to Table2.2 ^[6], but it is in Anchor category according to Table2.1 ^[6].

Figure2.5 is a snapshot from http://research.binghamton.edu/. The text in the squares is either in Strong tag or Anchor tag, they are highlighted with either in bigger font size or different color rather than regular black. Apparently, it is consistent with the author’s intention that he/she wants people to notice these lines which should draw more attention to the highlighted content and have more weight than the other un-highlighted text.

Figure2.5

However, difficulties come along with applying different weight to different HTML tags. Take the following piece of HTML as an example, in Figure2.6, which is from Yahoo news page:

Figure2.6

Take a careful look at the red square and orange square, “Mario left a comment: Obama’s ….”, is separated into 2 different parts, the terms in blue are in Anchor tag which have HREF links to the other pages, while, ‘left a comment’ in orange square is taken off from the Anchor tag, and clearly showed in a Strong text style as compared to “to see what your Connections are…”. However, Yahoo put ‘left a comment’ into a pre-defined <P> tag and set it into a Strong style. This can lead the conventional ways in parsing HTML becoming inaccurate and destroy the original order in the text. As Figure2.7 shows, the <P> tags and <A> tags are mixed together, which can lead to confusion in differentiating the text in those 2 kinds of tags if the program is not designed carefully.

Figure2.7

On the other hand, because these 2 papers focused on their test search engine WEBOR ^[7] which was developed by Weiyi Meng and Michal Cutler, Culter’s theory and research were apparently going on with clearly understanding of the working mechanism in WEBOR. Meanwhile, Cutler also had the access to control and modify WEBOR itself according to the requirement of changing CIV ^[6][7].

The conclusion could be unclear in applying this LS extraction method to Google, Yahoo or other commercial SEs which keep their searching mechanism as top secrets from others.

posted @ 2009-06-15 09:00 JosephQuinn 阅读(394) | 评论 (0) | 编辑 收藏

2.3 Robust Hyperlinks

‘Robust hyperlinks’ is a typical implementation from applying LSs in locating web pages. URL combined with LSs can not only re-find the desired web page, but also discover the most relevant pages if the desired page is missed or lost. Thomas A. Phelps and Robert Wilensky in their “Robust Hyperlinks Cost Just Five Words Each” ^[2] exhibited the problem when the desired page was deleted, renamed, moved, or changed, and demonstrated a novel approach to this issue by argumenting LSs in URLs so that they themselves became robust hyperlinks ^[2]. A novel compatible with traditional URL called “robust hyper link aware” URL can be like:

http://www.something.dom/a/b/c?lexical-signature="w1+w2+w3+w4+w5"

where w1, w2, w3, w4 and w5 are 5 terms extracted from the original page by TF-IDF. They are probably the first ones who raised the idea of lexical signature on a typical web page and explore its the application value.

posted @ 2009-06-15 08:48 JosephQuinn 阅读(190) | 评论 (0) | 编辑 收藏

2.2 Martin Klein and Michael Nelson’s study on Lexical Signature

Researchers have spent a lot of efforts in exploring how many LSs can give a best result. Martin Klein and Michael L. Nelson conclude 5 to 7 LSs are good enough in robust hyperlinks ^[2] after extensive experiments. Martin and Michael did not only conclude LS is a small set of terms derived from a document which can capture the “aboutness” of that document ^[3], but also defined a LS from a web page can discover the page at a different URL as well as to find relevant pages on internet ^[3]. Through their experiments on huge amount of web pages from 1996 – 2007 which were downloaded from Internet Archive, http://www.archive.org/index.php, they claimed that 5-, 6- and 7-term LSs performed the best in returning the interested URLs among the top 10 from Google, Yahoo, MSN live, Internet Archive, European Archive, CiteSeer and NSDL ^[3]. By apply equation 2-1 to 2-2, the LS score versus number of terms in each query were derived in Figure2.4.

Figure2.4 LS Performance by Number of Terms ^[3]

Their experiments also showed that 50% URLs are returned as the top1 result, and 30% URLs were failed to re-locate/find by choosing LS in decreasing TF-IDF order ^[3] when they were reviewing Phelps and Wilensky’s research. Meanwhile, they also carefully studied the techniques for estimating IDF values which is a non-trivial issue in generating LS for the web pages. In their recent paper, 2008, “A comparison of techniques for estimating IDF values to generate lexical signatures for the web” ^[19], they introduced 3 quite different ways to estimate terms’ IDF and carefully examined their performances.

1.         Local universe which was a set of pages downloaded from 98 websites, starting from 1996 to September, 2007 in each month ^[19].

2.         Screen scraping Google web interface which was generated in January, 2008 ^[19].

3.         Google N-Gram (NG) which was distributed in 2006 ^[19].

They compared these 3 IDF estimation techniques and claimed that local universe based data as well as the screen scraping based data is similar compared to their baseline, Google N-Gram based data.

Besides listing the detail percentage of success and fail to retrieve a URL, they used the following 2 equations in paper ^[3] to evaluate the score of LSs: fair score and optimistic score.

  ^[3] 2-1

  ^[3] 2-2

R(i) shows the ith page’s rank returned by SE after sending the query, when it gets bigger value, the fair score will be lower, N is the total sample pages in their experiments which is 98 and is the average value.

  ^[3] 2-3

  ^[3] 2-4

In the optimistic score equation, S_opt is different from S_fair which is only determined by pages’ rank. is the average fair score value.

They set R_max = 100 which makes S_fair can always be positive if the desired page appears in first 100 results from SE. If R(o) > R_max, when the desired page does not appear in first 100 results, then simply set S_fair = 0 and S_opt = 0. The final results of scores were from 2 terms to 15 terms per query and scores ranged from 0.2 to 0.8. They also concluded the scores on one page since year 1996 to 2007 ranged from 0.1 to 0.6 ^[3]. More details and score curves in their paper are not included in this project report.

posted @ 2009-06-15 06:27 JosephQuinn 阅读(256) | 评论 (0) | 编辑 收藏

References

1.       Weiyi Meng, and Hai He.  Data Search Engine. In Encyclopedia of Computer Science and Engineering (Benjamin Wah, ed.), John Wiley & Sons, pp.826-834, January 2009.

2.       Thomas A. Phelps, Robert Wilensky, 2000. Robust Hyperlinks Cost Just Five Words Each. Technical Report: CSD-00-1091. Publisher: University of California at Berkeley.

3.       Martin Klein, Michael L. Nelson. 2008. Revisiting Lexical Signatures to (Re-)Discover Web Pages. Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries Pages: 371 – 382

4.       Seung-Taek, David M. Pennock, C. Lee Giles, Robert Krovetz, 2002. Analysis of Lexical Signatures for Finding Lost or Related Documents SIGIR' 02, August 11-15, 2002, Tampere, Finland.

5.       Seung-Taek, David M. Pennock, C. Lee Giles, Robert Krovetz, 2004. Analysis of Lexical Signatures for Improving Information Persistence on the World Wide Web. ACM Transactions on Information Systems, Vol. 22, No. 4, October 2004, Pages 540–572.

6.       M. Cutler, Y. Shih, W. Meng. 1997. Using the Structure of HTML Documents to Improve Retrieval. USENIX Symposium on Internet Technologies and Systems.

7.       M. Cutler, H. Deng, S. S. Maniccam, W. Meng. Tools with Artificial Intelligence, 1999. A new study on using HTML structures to improve retrieval. Proceedings. 11th IEEE International Conference on Volume , Issue , 1999 Page(s):406 – 409.

8.       J. Lu, Y. Shih, W. Meng and M. Cutler. 1996. Web-based Search Tool for Organization Retrieval. http://nexus.data.binghamton.edu/~yungming/webor.html

9.       Rada Mihalcea and Paul Tarau. 2004. TextRank: Bring Order into Texts. Proceedings of EMNLP 2004, pages 404–411, Barcelona, Spain.

10.    Rada Mihalcea. 2004. Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization. In Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), Geneva, Switzerland.

11.    Xiaojun Wang, Jianwu Yang. 2006. WordRank-Based Lexical Signatures for Finding Lost or Related Web Pages. APWeb 2006, LNCS 3841, pp. 843-849.

12.    Larry Page. 1998. The PageRank Citation Ranking: Bringing Order to the Web. Computer Networks and ISND Systems.

13.    Jon M. Kleinberg. 1999. Authoritative Sources in a HyperLinked Environment. Journal of the ACM, 46(5): 604-632.

14.    WordNet, http://wordnet.princeton.edu/

15.    C.Y.Lin and E.H.Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of Human Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada, May.

16.    Weiyi Meng, Clement Yu, and King-Lup Liu. 2002. Building Efficient and Effective Metasearch Engines. ACM Computing Surveys, Vol. 34, No. 1, March 2002, pp.48-89.

17.    Weiyi Meng, Zonghuan Wu, Clement Yu, and Zhuogang Li. 2001. A Highly-Scalable and Effective Method for Metasearch. ACM Transactions on Information Systems 19(3), pp.310-335, July 2001.

18.    Michael K. Bergman. 2001. White Paper: The Deep Web: Surfacing Hidden Value. BrightPlanet. Ann Arbor, MI: Scholarly Publishing Office, University of Michigan, University Library vol. 7, no. 1, August, 2001

19.    Martin Klein, Michael L. Nelson. 2008. A comparison of techniques for estimating IDF values to generate lexical signatures for the web. Workshop on Web Information and Data Management. Proceeding of the 10th ACM workshop on Web information and data management. Napa Valley, California, USA. SESSION: System issues. Pages 39-46.

20.    Crunch, http://www.psl.cs.columbia.edu/crunch/

posted @ 2009-06-15 06:20 JosephQuinn 阅读(248) | 评论 (0) | 编辑 收藏

2.1 Seung-Taek Park’s Study on Lexical Signature

Before referencing the related studies and works, the terminology “Lexical Signature” (LS) is quite necessary to be mentioned first. LS is simply considered as an equivalent term to “key words/terms/phrases” in chapter 1. There are many related works have given LS various descriptions. Thomas A. Phelps and Robert Wilensky made this definition: a relatively small set of such terms can effectively discriminate a given document from all the others in a large collection ^[2]. They also proposed a way to create LS that meets the desired criteria which is selecting the first few terms of the document that have the highest "term frequency-inverse document frequency" (TF-IDF) values ^[2]. Martin Klein and Michael L. Nelson introduced the LS as a small set of terms derived from a document that capture the “aboutness” of itself ^[3]. S. T. Park studied and analyzed Phelps and Wilensky’s theory, and he claimed that, LS had following characteristics by concluding from Phelps and Wilensky’s paper ^[4][5]:

(1) LSs should extract the desired document and only that document ^[5].

(2) LSs should be robust enough to find documents that have been slightly modified ^[5].

(3) New LSs should have minimal overlap with existing LSs ^[5].

(4) LSs should have minimal search engine dependency ^[5].

Seung-Park also raised his own perspective about LS to help the user finding similar or relevant documents:

(1) LSs should easily extract the desired document. When a search engine returns more than one document, the desired document should be the top-ranked documents ^[5].

(2) LSs should be useful enough to find relevant information when the precise documents being searched for are lost ^[5].

After all, S. T. Park’s studies on LS are very insightful and helpful in this project. If type “Lexical Signature” as a search query into Google, then the first 10 results are most likely going to have both of his 2 papers “Analysis of lexical signatures for finding lost or related documents” ^[4] and “Analysis of lexical signatures for improving information persistence on the www” ^[5].

S. T. Park conducted a large amount of experiments with TF, DF, TFIDF, PW, TF3DF2, TF4DF1, TFIDF3DF2, TFIDF4DF1 separately and combined them synthetically ^[4][5], then, compared the results from Yahoo, MSN and AltaVista all in histograms. Including unique result, 1^st-rank result and top 10 results ^[5], the success re-finding rate is more than 60% but less than 70% when take both 2 URLs match and 2 documents’ cosine value > 0.95 as a success re-finding into consideration. Thus, if only taking 2 URLs comparison as a measurement and having a success when they are matched, the success re-finding/re-locating rate would be probably lower.

Figure2.1

Figure2.2

Figure2.3 ^[5]

In this project, the LS’s definition follows S. T. Park’s theory: LSs are the key terms from the web page and can help to both identify the web page from others uniquely and retrieve the most relevant page effectively by search engines. Meanwhile, in experiments, LS cannot be simply considered as the unchanged terms (words) from the documents. Some necessary pre-procedures and transformations must be taken before starting to process the web pages/documents in the information retrieval ways, such as removing the stop words or transforming the words in different forms but close meanings into one unique term, like “lexica” and “lexical” to “lex”. Other than this, picking out only nouns and verbs or nouns and adjectives from the text is also feasible based on word form data base. These steps are implemented in Chapter 4 particularly by LUCENE and WORDNET, 2 open source Java projects well accepted in practical industry world.

posted @ 2009-06-15 04:49 JosephQuinn 阅读(215) | 评论 (0) | 编辑 收藏

1. Introduction

WWW shows unprecedented expansion and innumerable styles which has led us to an “information technology age” for over a decade. Thus, the ways of how to efficiently and effectively retrieve the web documents according to the specific needs raise the huge development in web search technology. Indeed, searching is the second most popular activity on the web, behind e-mail, and about 550 million web searches are performed every day ^[1]. Google and Yahoo are now leading the most advanced technology in the web search industry, by knowing some words, phrases, sentences, names, addresses, email addresses, telephone numbers and date, the related web pages can be efficiently searched up, which significantly changes the traditional ways of obtaining the information. People are becoming professionals in web information retrieval even most of them have no idea about information retrieval theory in their minds and they are struggling in finding more related and closed to their desired pages. One of the key challenges focuses on the input query. Generally speaking, it is about how to effectively get better results by acquiring the certain key words/phrases as search queries in the first step. The other one focuses on the order of words in the query, same words but in different order can leads to quite different result URLs, normally, the word appearing in the first shows more importance than the others, therefore the results are not trying to match the separated words in query at the same time equally. Other than this primitive query, there are also several query grammars provided by many search engines, such as double quotation marks, logical ‘and’, ‘or’ and ‘not’ operation, they assure the same weight are put equally on the words when these logical operators are applied. Meanwhile, given the same key words as a search query, different search engines return different results, the measurements of how ‘good’ and how ‘comprehensive’ the results are drawing the attentions in the related web research, meanwhile, the results quality is judged by the users and it is not completed objective. Therefore, the measurements must be designed as much objective and persuasive as possible. This leads to the initiative in this project, an appropriate test system is built upon the web search engines by analyzing their results. Then the analysis focuses on 2 aspects, the URL quality and content quality. URL quality can only be tested by comparing the user’s target URL and the result URLs returned by search engine, and a good URL quality is only determined by 2 URL’s matching or not. Content quality is more loose and unrestricted by comparing the target URL’s content and the result URL’s content. A good content quality means high similarity between those 2. It is critical that, before the above 2 methods being applied, the ways how to get result URLs from search engine and maximize the quality measurements is leading the main task in this project which is equal to getting a query best summarize the page itself. In the following report, SE stands for Search Engine and LS stands for Lexical Signature.

There are various ways to extract key terms from the pure text file in information retrieval theory, which can be applied on web pages. In this project, taking key terms from a web page as search query to SE and then comparing the results from SE with the previous page is considered as a way to measure SE. Develop such a measurement which needs to be convincible and reliable is the key part of this project and equals to the studies around re-find/re-locate a given HTML’s URL. Because a valid web page online has a URL associate with it, there is an opportunity that the URL can be located by SE after extracting key terms from its text content into a query. It is not always the correct way because SE also takes links information into ranking consideration such as page rank algorithm. But it can show a high success rate in re-finding/re-locating the target URL by only focusing on the page itself, disregarding the global links structure. This re-finding/re-locating unique designated URL process excludes the subjective inferences and offers a good practice in the SE measurements.

In this report, the related researches and experiments such as removing the structural HTML tags, extracting text from HTML, download document frequency for each term from Google and count term frequency are practiced and tested, then, the query is constructed and returned URL results from search engine among various methods are compared. Chapter 2 lists related works and studies on the text processing methods for a single web page/document. Chapter 3 introduces the data set, search engine selection, HTML parsing and text extraction, the terms in term frequency order, document frequency order and graph-based rank order, query sending, result pages comparisons and success retrieval evaluations. Chapter 4 describes the detailed experiments setup, different term order’s algorithms and URL comparisons which are introduced in Chapter 3. All related results are recorded and shown in the histograms, followed by the comparisons and analysis upon the differences. Chapter 5 makes the conclusion, limitations and some potential improvements on theories and experiments in this project.

posted @ 2009-06-15 04:48 JosephQuinn 阅读(265) | 评论 (0) | 编辑 收藏

Abstract

Extensive pages along with their URLs are taken as samples, a query which can best summarize the page itself is constructed and sent to the search engine, the samples’ URLs are compared with the returned URLs, if there is a match between them or their content, consider the query as a lexical signature query or strong query. Assume that the page and its URL in surface web are supposed to be found by general search engine leads to the search engine’s quality measurement. By sending the strong query to different search engines, the qualities are derived. It will be a good source for the measurement only if the query extraction and processing targeted on web pages are well designed and implemented. This process is called find a lexical signature query for a given web page.

 

Keywords: lexical signature, query, search engine, Google, Yahoo, HTML tags, term frequency, document frequency, graph-based ranking algorithm, word rank, sentence rank.

posted @ 2009-06-15 04:46 JosephQuinn 阅读(135) | 评论 (0) | 编辑 收藏

Catalog

     摘要: Normal 0 7.8 pt 0 2 false false false EN-US ZH-CN X-NONE MicrosoftInternetExplorer4 ...  阅读全文

posted @ 2009-06-15 04:11 JosephQuinn 阅读(173) | 评论 (0) | 编辑 收藏

JDBC: why 'Class.forName' at first

First, here is the following basic code:

Here is a trivial question, why Class.forName in the first place.
Now, let's take a look at class com.mysql.jdbc.Driver source file:

Take a close look in class Driver's static block:

It ensures the new created Driver object is registered every time after Driver class is created or Driver instance is created.

Class.forName(driver); make sure the code in static block run at first which means a new Driver class object is created during the compile time.

For better understanding, here is a sample program which have a static block in the class

 

The output from class Main is

Class A loaded

Now, change the above code a little bit in public static void main function: only initiate class A a=null.

 

There is no output at this time. This is because only with a variable name 'a' doesn't change the fact that no new object from class 'A' is created and the static block doesn't
run neither.

Change the code again:


The outputs are:
Class A loaded
create a instance of A
create a instance of A

Clearly, with new A(), static block only runs once(The basic concept of static) and constructor of A runs twice as 2 objects are creates.

posted @ 2009-06-15 00:44 JosephQuinn 阅读(349) | 评论 (0) | 编辑 收藏