WO2002010957A2 - Computer method and apparatus for determining content types of web pages - Google Patents
Computer method and apparatus for determining content types of web pages Download PDFInfo
- Publication number
- WO2002010957A2 WO2002010957A2 PCT/US2001/022430 US0122430W WO0210957A2 WO 2002010957 A2 WO2002010957 A2 WO 2002010957A2 US 0122430 W US0122430 W US 0122430W WO 0210957 A2 WO0210957 A2 WO 0210957A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- web page
- content type
- examining
- potential content
- false
- Prior art date
Links
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/953—Organization of data
- Y10S707/959—Network
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99935—Query augmenting and refining, e.g. inexact access
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99936—Pattern matching access
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99937—Sorting
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99943—Generating database or data structure, e.g. via user interface
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99944—Object-oriented database structure
- Y10S707/99945—Object-oriented database structure processing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99948—Application of database or data structure, e.g. distributed, multimedia, or image
Definitions
- a global computer network e.g., the Internet
- a global computer network is formed of a plurality of computers coupled to a communication line for communicating with each other.
- Each computer is referred to as a network node.
- Some nodes serve as information bearing sites while other nodes provide connectivity between end users and the information bearing sites.
- the explosive growth of the Internet makes it an essential component of every business, organization and institution strategy, and leads to massive amounts of information being placed in the public domain for people to read and explore.
- the type of information available ranges from information about companies and their products, services, activities, people and partners, to information about conferences, seminars, and exhibitions, to news sites, to information about universities, schools, colleges, museums and hospitals, to information about government organizations, their purpose, activities and people.
- the Internet became the venue of choice for every organization for providing pertinent, detailed and timely information about themselves, their cause, services and activities.
- the Internet essentially is nothing more than the network infrastructure that connects geographically dispersed computer systems. Every such computer system may contain publicly available (shareable) data that are available to users connected to this network. However, until the early 1990's there was no uniform way or standard conventions for accessing this data. The users had to use a variety of techniques to connect to remote computers (e.g. telnet, ftp, etc) using passwords that were usually site-specific, and they had to know the exact directory and file name that contained the information they were looking for.
- remote computers e.g. telnet, ftp, etc
- the World Wide Web was created in an effort to simplify and facilitate access to publicly available information from computer systems connected to the Internet.
- a set of conventions and standards were developed that enabled users to access every Web site (computer system connected to the Web) in the same uniform way, without the need to use special passwords or techniques.
- Web browsers became available that let users navigate easily through Web sites by simply clicking hyperlinks (words or sentences connected to some Web resource).
- Web Domain Web domain is an Internet address that provides connection to a Web server
- URL stands for Uniform Resource Locator. Generally, URLs have three parts: the first part describes the protocol used to access the content pointed to by the URL, the second contains the directory in which the content is located, and the third contains the file that stores the content:
- ⁇ protocol> ⁇ domain> ⁇ directory> ⁇ file>
- the ⁇ protocol> part may be missing. In that case, modern Web browsers access the URL as if the http:// prefix was used.
- the ⁇ f ⁇ le> part may be missing. In that case, the convention calls for the file "index.html" to be fetched. For example, the following are legal variations of the previous example
- Web page is the content associated with a URL.
- this content is static text, which is stored into a text file indicated by the URL.
- the content contains multi-media elements (e.g. images, audio, video, etc) as well as non-static text or other elements (e.g. news tickers, frames, scripts, streaming graphics, etc).
- multi-media elements e.g. images, audio, video, etc
- non-static text or other elements e.g. news tickers, frames, scripts, streaming graphics, etc.
- more than one files form a Web page, however, there is only one file that is associated with the URL and which initiates or guides the Web page generation.
- Web Browser Web browser is a software program that allows users to access the content stored in Web sites. Modern Web browsers can also create content "on the fly", according to instructions received from a Web site. This concept is commonly referred to as “dynamic page generation”. In addition, browsers can commonly send information back to the Web site, thus enabling two-way communication of the user and the Web site.
- Every Web site publishes its content packaged in one or more Web pages.
- a Web page contains a combination of text and multimedia elements (audio, video, pictures, graphics, etc) and has relatively small and finite size.
- text and multimedia elements audio, video, pictures, graphics, etc
- HTML code forms the infrastructure of the page, and which may dynamically download or produce its contents on the fly.
- the purpose of this invention is to automatically identify and classify the contents of a Web page among some specific types, by assigning a confidence level to each type. For example, given the following list of potential content types:
- the present invention method includes the steps of: providing a predefined set of potential content types; for each potential content type, running tests having test results which enable quantitative evaluation of at least some contents of the subject Web page being of the potential content type; mathematically combining the test results; and based on the combined test results, assigning a respective probability, for each potential content type, that some contents of that type exists on the subject Web page.
- Apparatus embodying the present invention thus includes a predefined set of potential content types and a test module utilizing the predefined set.
- the test module employs a plurality of processor-executed tests having test results which enable, for each potential content type, quantitative evaluation of at least some contents of the subject Web page being of the potential content type.
- the test module For each potential content type, the test module (i) runs at least a subset of the tests, (ii) combines the test results and (iii) for each potential content type, assigns a respective probability that at least some contents of that type exists on the subject Web page.
- the set of potential content types includes one or more of the following: organization description, organization history, organization mission, organization products/services, organization members, organization contact information, management team information, job opportunities, press releases, • calendar of events/activities, biographical data, articles/news with information about people, articles/news with information about organizations, and • employee roster.
- the step of combining includes producing a respective confidence level for each potential content type, that at least some content of the subject Web page is of the potential content type.
- a Bayesian network is used to combine the test results.
- the Bayesian network is trained using a training set of Web pages with respective known content types such that statistics on the test results are collected on the training set of Web pages.
- the tests involve: (i) determining whether a predefined piece of data or keyword appears in the page (e.g., people names, telephone numbers, etc.), (ii) examining syntax or grammar or text properties (e.g., number of passive sentences, number of sentences without a verb, percentage of verbs in past tense, etc.),
- storage means receives and stores indications of the assigned probabilities of each content type per Web page as determined by the test module.
- the storage means thus provides a cross reference between a Web page and respective content types of contents found on that Web page.
- Fig. 1 is an overview of the preparation phase for the present invention.
- Fig. 2 is a dataflow diagram for the training phase of the present invention.
- Fig. 3 is an overview of the classification phase.
- Fig. 4 is a block diagram of a preferred computer embodiment of the present invention.
- Every Web page is simply a container of information. There are no restrictions or standard conventions about the type of information it contains, its style, or its format. Therefore it is very difficult for computer programs to automatically extract information from a random Web page, since there are no rules or standards that could help them locate the information, or simply determine if the information exists at all.
- the present invention helps solve the second part of this problem, namely to determine what kind of information a given Web page contains. Once the content type of a Web page is known, specialized techniques can be used to locate and extract this content (e.g. see Inventions 5 and 6 as disclosed in the related Provisional Application No. 60/221,750 filed on July 31, 2000 for a "Computer Database Method and Apparatus").
- these rules may become very complicated, hard to understand, and are error prone, b) they are very rigid, in the sense that a rule either fires or not. hi other words, no "partial credit" is given when a rule is only partially satisfied, c) the end result of implementing these rules is simply a classification, but without any measure of the confidence level associated with this classification, d) the rules are developed based mostly on intuition and simple observations, without associating any kind of statistical confidence or measure with each rale.
- the present invention circumvents all these problems by replacing the rule-based approach with a series of tests, statistical training, and mathematical combination of the test results to produce a list of the potential content types and an accurate measure of the confidence level associated with each type.
- a preparation phase the user defines the set of content types that the invention must recognize within Web pages, and prepares tests that provide evidence about one or more of these types.
- a training phase the user runs all the tests on a set of Web pages with known content types. Then, the results of the tests are used to calculate statistical conditional probabilities of the fonn P(Test result
- the resulting table with probabilities can then be used for classification.
- the user runs the tests prepared in the preparation phase on a subject Web page with unknown content types and collects the test results. Then, the user combines the test results using the probabilities from the training phase and calculates a confidence level for each of the potential content types, as they have been identified during the preparation phase.
- the current invention uses the following steps:
- a preparation phase 24 is illustrated.
- the first task in identifying the contents of a Web page is, naturally, to decide what kind of content types 10 a user is interested in. For example, one user may need to identify pages that contain company-related information, hi that case, the set 10 of potential content types may be the following:
- the potential content types 10 may be the following: ⁇ Conference Title, Conf. Description, Conf. Keywords, Conf. Dates, Conf.
- the next step in preparation phase 24 is to prepare tests 15 that provide some evidence whether a page contains some of these content types 10 or not. For example, the following tests may be used to provide evidence about whether a page contains a press release or not:
- Test 1 Page contains company keywords, for example: Inc., Corp., Ltd., etc.
- Test 4 Page contains phone numbers or addresses Outcome: True or False ...etc.
- Test 5 The number of paragraphs in the page is in one of the following ranges:
- Test 6 The number of words in the page is in one of the folio wing ranges:
- Test 7 The number of external links in the page is: 0, 1, 2, or more (outcome "M") Outcome: 0, 1, 2, or M (the corresponding outcome).
- the tests 15 may be anything that helps differentiate between two or more of the given types 10.
- some possible types of tests 15 are the following: check if an easily recognizable piece of data (a predefined term or keyword) appears in the page (e.g. people names, telephone numbers, etc.) measure syntax or grammar or text properties (e.g. number of passive sentences, number of sentences without a verb, percentage of verbs in past tense, etc)
- the particular kind of tests 15 to develop and use depends of course on the task in hand, i.e. the kind of page content that the user is interested in identifying.
- the next step is to measure the statistical connection between every possible test outcome and the target page content types 10.
- a training set 23 of pages with known content types is collected. For example, if the target content types 10 are the following:
- the accuracy of the classification that is achieved by this invention increases as the training set 23 becomes larger and more representative of the "real world".
- the ideal training set 23 is a random sample of a few hundred to a few thousand samples (the actual number depends on the number of target types, and how easily they are distinguishable from each other).
- the actual training phase/module 50 consists of the following steps as illustrated in Fig. 2: a) run all tests 15 on all samples 23 b) collect the test results 22, and calculate the conditional probabilities 23 for each result to appear given each target type 20, i.e. P(test result
- test results 22 and the conditional probabilities 27 connected with each result provide evidence about the possibility that the page contains each one of the target content types 20. But a tool is still needed to combine and weight all these pieces of evidence, and produce the final conclusion.
- the mathematical tool that is used by the present invention is based on the concept of Bayesian Networks and is illustrated in Fig. 3 (discussed later). Bayesian Networks have emerged during the last decade as a powerful decision-making technique. It is a statistical algorithm that can combine the outcome of several tests in order to chain probabilities and produce an optimal decision based on the given test results.
- Bayesian Networks come in many forms, however their basic building block is Bayes' theorem:
- Bayesian Networks One of the simplest types of Bayesian Networks is the Na ⁇ ve Bayesian Network.
- the Na ⁇ ve Bayesian Network is based on the assumption that the tests are conditionally independent which simplifies considerably the calculations.
- the formula that calculates the probability for some hypothesis given some test results is the following:
- H X , H 2 ,... , H ⁇ are all the possible values of the hypothesis
- T x , T 2 , ... , T N are the test results from tests 1, 2, ..., N respectively.
- each Bayesian Network is capable to detect the existence of one type of contents, based on the test results.
- the output of each Bayesian Network is a probability, or confidence level, that the given page contains that type of content. For example, to distinguish between the following types:
- Bayesian Networks are used in the same way, and the end result is an array of values that correspond to confidence levels about the existence of the target content types. Referring to Fig. 3, a subject Web page 34 is received as input to Bayesian
- the Bayesian Network module 52 applies the predefined tests 15 from the preparation phase 24 (Fig. 1).
- the test results 36 for the subject Web page 34 are combined using a Bayesian Network 38 as described above.
- the Bayesian Network 38 calculates a confidence level 32 for each candidate content type 10 (Fig. 1). That is, the Bayesian Network 38 outputs an indication (i.e., the probability) of each type 10 of content being detected on the subject Web page 34.
- a digital processor 59 receives input at 14 from input devices (e.g., keyboard, mouse, etc.), a software program, another computer (e.g., over a communications line, the Internet, within an intranet, etc.) and the like.
- the digital processor 59 provides as output 16, indications of the types of contents detected on given input Web page. Preferably confidence level per detected content type is also output.
- the output 16 is provided to output devices (e.g., a display monitor, printer, etc.), software programs, another computer (coupled to processor 59 across a communications link) and the like.
- the page content types determined by computer system 12 for respective Web pages are output to a database system 31 for storage therein.
- the database 31 receives and stores the indications of page content types correlated to (or in a cross-referenced manner with) indications of respective Web pages.
- a database 31 or index of Web pages and corresponding page content type is formed by the present invention method and apparatus.
- digital processor 59 stores or has loaded into its memory the invention software 18.
- processor 59 executes invention software 18 to implement the present invention as discussed above in Figs. 1-3.
- software routine 18 is formed of a training member/module 50, a Bayesian Network module 52 and a test module 54.
- test module 54 performs step A (preparation) above, while training module 50 performs step B (training) above with the support of test module 54.
- training module 50 applies the tests 15 of step A above to training set 23 of Web pages with known content types.
- Next training module 50 calculates conditional probabilities 27 for all combinations of test outcomes and hypothesis values.
- the Bayesian Network module 52 implements step C (classification) above as previously discussed in conjunction with Fig. 3.
- the example Bayesian Network identifies if the page contains press release content. Therefore the content types of interest are simply: ⁇ PRESS_RELEASE, OTHER ⁇ In order to identify between these two target types, one Bayesian Network with the following hypothesis is sufficient:
- Test 1 The page has a title. Outcome: True or False.
- Test 2 The page has a copyright statement. Outcome: True or False.
- Test 3 The page has a navigation map. Outcome: True or False.
- Test 4 The page has a line with the word "Contact” followed by at least 1 phone number within the next 10 lines. Outcome: True or False.
- Test 5 The page has a text line that contains the string "Press Release” or "for immediate release”.
- Test 6 The first sentence of the first paragraph has a date. Outcome: True or False.
- Test 7 The first sentence of the first paragraph is preceded by a header line. If this header contains a company name, then outcome is
- Test 8 First sentence of first-paragraph contains a company name. Outcome: True or False.
- Test 9 First sentence of first paragraph contains the word "announce” or a form of it.
- Test 10 First sentence of first paragraph contains "NYSE:” or "NASDAQ:”. Outcome: True or False.
- Test 11 There is a line or sentence starting with "For further information” or "For more information” (if this line contains a company name, then outcome is "Company") Outcome: True, False, or Company.
- Test 12 There is a text line starting with "About” (if this line contains a company name, then outcome is "Company”). Outcome: True, False, or Company.
- Test 13 The page has a text line containing one of the headers defined in "Stuff Headers” list (see below). Outcome: True or False.
- Test 14 The page has a text line containing one of the headers defined in "Board Headers” list (see below). Outcome: True or False.
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2001273522A AU2001273522A1 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining content types of web pages |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US22175000P | 2000-07-31 | 2000-07-31 | |
US60/221,750 | 2000-07-31 |
Publications (3)
Publication Number | Publication Date |
---|---|
WO2002010957A2 true WO2002010957A2 (en) | 2002-02-07 |
WO2002010957A8 WO2002010957A8 (en) | 2002-07-25 |
WO2002010957A3 WO2002010957A3 (en) | 2003-04-10 |
Family
ID=22829204
Family Applications (5)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2001/022381 WO2002010955A2 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining content owner of a website |
PCT/US2001/022385 WO2002010956A2 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining site type of a web site |
PCT/US2001/022430 WO2002010957A2 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining content types of web pages |
PCT/US2001/022426 WO2002010982A2 (en) | 2000-07-31 | 2001-07-17 | Computer system for collecting information from web sites |
PCT/US2001/023343 WO2002010960A2 (en) | 2000-07-31 | 2001-07-25 | Computer method and apparatus for extracting data from web pages |
Family Applications Before (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2001/022381 WO2002010955A2 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining content owner of a website |
PCT/US2001/022385 WO2002010956A2 (en) | 2000-07-31 | 2001-07-17 | Computer method and apparatus for determining site type of a web site |
Family Applications After (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2001/022426 WO2002010982A2 (en) | 2000-07-31 | 2001-07-17 | Computer system for collecting information from web sites |
PCT/US2001/023343 WO2002010960A2 (en) | 2000-07-31 | 2001-07-25 | Computer method and apparatus for extracting data from web pages |
Country Status (3)
Country | Link |
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US (7) | US6618717B1 (en) |
AU (5) | AU2001278938A1 (en) |
WO (5) | WO2002010955A2 (en) |
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