WO2002010955A2 - Computer method and apparatus for determining content owner of a website - Google Patents

Computer method and apparatus for determining content owner of a website Download PDF

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Publication number
WO2002010955A2
WO2002010955A2 PCT/US2001/022381 US0122381W WO0210955A2 WO 2002010955 A2 WO2002010955 A2 WO 2002010955A2 US 0122381 W US0122381 W US 0122381W WO 0210955 A2 WO0210955 A2 WO 0210955A2
Authority
WO
WIPO (PCT)
Prior art keywords
name
candidate
content owner
web site
determining whether
Prior art date
Application number
PCT/US2001/022381
Other languages
English (en)
French (fr)
Other versions
WO2002010955A3 (en
Inventor
Kosmas Karadimitriou
Jonathan Stern
Michel Decary
Jeremy W. Rothman-Shore
Original Assignee
Eliyon Technologies Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eliyon Technologies Corporation filed Critical Eliyon Technologies Corporation
Priority to AU2001273513A priority Critical patent/AU2001273513A1/en
Publication of WO2002010955A2 publication Critical patent/WO2002010955A2/en
Publication of WO2002010955A3 publication Critical patent/WO2002010955A3/en

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Classifications

    • 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
    • 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 ofthe 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 has become 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 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.
  • 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 is an Internet address that provides connection to a Web server (a computer system connected to the Internet that allows remote access to some of its contents).
  • 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 domain directory in which the content is located, and the third contains the file that stores the content:
  • the ⁇ protocol> part may be missing, hi that case, modern Web browsers access the URL as if the http:// prefix was used.
  • the ⁇ file> part maybe missing. In that case, the convention calls for the file "index.html" to be fetched.
  • 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 file forms 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 ofthe user • and the Web site.
  • Web site is closely linlced to the concept of Web domain, however they have some important differences.
  • Web site is a more abstract term, which refers to a collection of Web pages usually put together, owned, and maintained by a single entity (which can be company, organization, institution, person, etc).
  • a Web site resides on a particular Web domain, however there are exceptions when the content of a site is distributed among several domains. In general, every Web site has at least one domain, however the opposite is not always true (there are domains with no corresponding Web site).
  • a Web site can be viewed as a book where each chapter provides information about a different but related topic and where it is assumed that the reader will correlate information from one page to the other so that there is no need to repeat the same information on every page.
  • a company Web site may contain pages dedicated to the company's mission, history, products, services, management, contact information, news, etc. The company name might not even be mentioned when talking about products or its management and is assumed to be conveyed by the fact that the present/currently displayed page is part ofthe company Web site.
  • the content published in a Web site can vary considerably in both its type and format: it may be news, information, fiction, statements, etc. given as text, pictures, video, audio, graphics, etc.
  • the Web site content owner is the legal entity (individual or institution, company, organization, etc) which publishes and is legally responsible for this content.
  • Identifying the content owner allows tagging the information with its source, in order to filter it, estimate its value and accuracy, or use it according to attributes associated with this source.
  • very often information collected from a Web site is incomplete if the site owner is unknown.
  • the main purpose ofthe present invention is to automatically identify the content owner of a Web site, given a set of Web pages from that site. Statistical algorithms are used to make inferences based on an array of tests and candidate strings that are extracted from the given pages. The whole process is completely automated, so that it can run without human supervision or labor. In this way, the present invention method coupled with a Web robot (see invention 4 as disclosed in the related Provisional Application No. 60/221,750) is capable of producing the complete list of ⁇ Web domain, legal owner ⁇ for all existing domains in the Web, in a totally automated fashion.
  • a method of identifying content owner of a Web site comprises the computer-implemented steps of: (i) collecting candidate names from a subject Web site; (ii) for each candidate name, running tests having test results which enable quantitative evaluation ofthe candidate name being content owner name ofthe subject Web site; and (iii) mathematically combining the test results into an indication of content owner name.
  • the step of running tests includes employing tests that enable statistical evaluation of each candidate name being the content owner name.
  • the step of combining the test results includes using a Bayesian network.
  • the Bayesian network is trained using a training set of Web sites with respective known content owner names such that statistics on the test results are collected on the training set of Web sites.
  • the invention method may further comprise the step of providing a confidence level ofthe indicated content owner name.
  • the step of collecting candidate names includes using software robots, Web crawlers, Web spiders and/or agents.
  • the step of collecting candidate names includes collecting noun phrases that are capitalized and in particular have a generic term that is indicative of an organization. For example, the terms (or the abbreviations thereof) “agency”, “association”, “bank”, “club”, “company”, “corporation”, “council”, “enterprise”, “foundation”, “hospital”, “incorporated”, “labs”, “limited”, “partners”, “ventures” and the like are commonly used to indicate an organization.
  • the invention collects noun phrases that include such terms and uses the collected noun phrases as candidate names.
  • the step of combining the test results to identify the content owner name includes determining when no said content owner name exists at the subject Web site.
  • the step of running tests enables statistical evaluation ofthe candidate name being the content owner name; and the step of determining no content owner name exists at the subject Web site is based on no candidate name having a probability within a predetermined threshold.
  • the threshold is statistically predefined by a desired ratio of false positives to false negatives.
  • computer apparatus for identifying the content owner of a Web site comprises: means for collecting candidate names from a subject Web site; and a test module responsive to the means for collecting and including a plurality of processor-executed tests having test results which enable quantitative (e.g., statistical) evaluation ofthe candidate name being content owner name ofthe subject Web site. For each candidate name, the test module runs the tests and combines the test results into an indication of content owner name or an indication that no content owner name exists at the subject Web site.
  • the test module preferably employs a Bayesian network to mathematically combine the test results.
  • a training member trains the Bayesian network using a training set of Web sites with known content owner names.
  • the training module collects statistics on the test results from the training set of Web sites.
  • the test module further provides a confidence level ofthe indicated content owner name.
  • the means for collecting candidate names includes software robots, Web crawlers, Web spiders and/or agents.
  • the method and apparatus includes storing determined indications of content owner names in a manner and means that enables cross referencing with indications of respective Web sites. To that end, a database (or similar storage means) containing indications of content owner name correlated with a respective Web site is formed.
  • Fig. 1 is an overview ofthe process employed by the present invention for calculating test statistics from a training set of Web sites with known content owner names.
  • Fig. 2 is an overview ofthe process of combining the test statistics with the test results from a subject Web site in order to calculate the confidence level for each candidate string being the content owner's name for the subject Web site in a preferred embodiment ofthe invention.
  • Fig. 3 is a block diagram of a preferred computer embodiment ofthe present invention.
  • Fig. 4 is a schematic overview of a computer network environment for deploying the software embodiment of Fig. 3. DETAJJ ED DESCRIPTION OF THE INVENTION
  • Every Web site belongs to a legal entity (individual or institution, company, organization, etc.) that is responsible for its content.
  • the content owner is obvious to a human reader, e.g. in company Web sites the company name very often appears in the home page.
  • the name ofthe content owner does not have a standard format, position, or header, and sometimes it does not even appear in the home page at all.
  • the content owner name often does not even appear in text form, but it is embedded in a picture with the company logo. Text in pictures is readable by humans, but not easily readable by computers, especially when the logo has been creatively designed. For all these reasons, it is not straightforward for a computer program to identify the content owner's name from a Web site.
  • Fig. 2 The approach that the present invention employs for solving this problem involves the following steps as illustrated in Fig. 2: a) collect 26 as many candidate names 40 as possible from the Web site b) run 28 a series of tests (T ; )on each candidate name 40 c) combine 30 the test results 36 of each candidate name 40 into a score d) decide 34 which one is the winning candidate name based on the scores
  • Each test result 36 provides some evidence about whether the candidate name 40 is the Web site's 42 content owner name or not.
  • Statistics 32 are used to quantify the amount of evidence each test result 36 provides. These statistics 32 are in the form of conditional probabilities, for example, when a candidate name returns result X from the test T j5 then there is 78% probability that this candidate name is the actual content owner name.
  • the statistics 32 used in quantifying the evidence have been calculated during a "training phase" from a suitable training sample 50.
  • the preferred embodiment ofthe present invention uses a Bayesian Network 30, however, other decision making, pattern recognition, or classification techniques can also be used (e.g. decision tree, neural network, rule-based expert system, etc).
  • Illustrated in Fig. 3 is a computer system 12 for implementing the present invention.
  • a digital processor 29 receives input at 14 from input devices (e.g., keyboard, mouse, etc.), a software program, another computer (e.g., over a commuriications line, the Internet, within an intranet, etc.) and the like.
  • the digital processor provides as output, indications of content owner names at 16 to output devices (e.g., a display monitor, printer, etc.), software program, another computer (coupled to processor 29 across a communications link) and the like, i the preferred embodiment, the content owner names determined by computer system 12 for respective Web sites are output to a database system for storage therein, a particular, the database receives and stores the indications of owner names correlated to (or in a cross-referenced manner with) indications of respective Web sites.
  • a database or index of Web sites and corresponding content owner names is formed by the present invention method and apparatus.
  • computer 100 is connected to a keyboard 102 and mouse 104 for receiving input and a display 106 for presentation of content.
  • Computer 100 is also linked to a network 110 having access to Web server 120.
  • the invention software 18 can be stored on disk and loaded into memory to be executed by a processor on a variety of computers, including computer 100 and Web server 120.
  • Network 110 can be part ofthe Internet, the worldwide collection of computers, networks and gateways that use the TCP/IP suite of protocols to communicate with one another.
  • the Internet provides a backbone of high-speed data communication lines between major nodes and host computers, consisting of thousands of commercial, government, educational, and other computer systems, that route data and messages.
  • Web server 120 is connected to the network 110 and can host computer software 18 ofthe present invention.
  • digital processor 29 stores and executes the invention program or software routine 18 which is formed of a training member 20, a candidate name collector 22 and a test module 24.
  • the collector 22 performs step a) above, while the test module 24 perfo ⁇ ns steps b) through d) above with the support of training member 20.
  • training member 20 calculates test statistics 32 ofthe tests T ; of step b) above, as applied to training set 50 of Web sites with known content owner names. These test statistics 32 provide quantitative measures ofthe significance of each possible test result in the process of identifying the Web site's content owner name.
  • Each of these program elements 20, 22, 24 are further discussed below in conjunction with Figs. 1 and 2.
  • collector 22 In order for the collector 22 (in step a) above) to collect all candidate names for the Web site's 42 content owner, a set of fairly loose criteria is used. These mainly involve collecting every noun phrase (succession of nouns and adjectives) that is capitalized, and or contains some keywords, such as "rnc", "Corporation”, “Association”, etc. Thus, collector 22 collects as candidate names 40 (in Fig.
  • noun phrases having a generic term that is indicative of an entity or organization for example, noun phrases having the term “agency”, “association'V'associates”, “bank”, “club”, “company”, “corporation”, “council”, “enterprise”, “foundation”, “group”, “hospital”, “incorporated”, “labs'V'laboratories”, “limited”, “partners”, “venture”, etc.
  • step 26 the invention method does not impose any strict criteria, so as not to lose any candidate name 40.
  • the subsequent tests T "filter out” any obviously bad candidates as discussed next.
  • step b) above the test module 24 runs a set of tests Ti on each candidate name 40 collected by collector 22, and records the results 36 ofthe tests.
  • Each test offers some evidence about whether the candidate name 40 is the name ofthe content owner ofthe subject Web site 42 or not.
  • These tests T may include the following:
  • candidate name alias refers to a sub-string, acronym, abbreviation or similar form ofthe candidate name, e.g. "Corex”, “Corex Tech.”, “CorTech”, “Corex Tech. Corp.”, or “CTC” are all aliases of "Corex Technologies Corporation)
  • determining whether the candidate name contains a weak organization keyword e.g. Systems, Business, International, etc.
  • a training set 50 of many hundred Web sites S is used.
  • the correct content owner's name 52 for each sample Web site S is known.
  • Candidate strings 54 are collected from each site S j and the tests T j are run on each candidate string 54.
  • probabilities are calculated that connect the test outcomes 56 with the given hypotheses, that is, P(Test
  • Hypothesis) the probability ofthe particular test result given the particular hypothesis.
  • Bayesian Networks have emerged during the last decade as a powerful decision-making technique.
  • a Bayesian Network is a statistical algorithm that combines 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 ofthe 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, hi Na ⁇ ve Bayesian Networks, the formula that calculates the probability for some hypothesis given some test results is the following:
  • H l H 2 ,:.,H K are all possible values ofthe hypothesis
  • T ,T ,...,T are the test results from tests 1 , 2, ... , N respectively
  • the above formula and the test results 36 for some candidate 40 are used to calculate the probability (confidence level 38) that the candidate name 40 satisfies each hypothesis (in the present invention, the hypothesis for each candidate name 40 has two values "True, the candidate string is the Web site's 42 content owner name" and "False, it is not”). Having a probability/confidence level 58 assigned to each candidate name 40, it is straightforward then to choose the candidate name 40 with the highest probability of being the Web site's 42 content owner name.
  • a threshold value for the probabilities/confidence level 38 is also useful to detect sites for which no name can be found, e.g. if no candidate name 40 was assigned probability higher than 0.80, then one can conclude that the real content owner's name does not exist in the Web site 42.
  • the threshold level can be calculated statistically based on the desired ratio of false positives and false negatives (false positives are cases that this invention produces a name but the Web site does not actually contain the content owner's name; false negatives are cases that the invention produces no name even though the content owner's name appears in the Web site).
PCT/US2001/022381 2000-07-31 2001-07-17 Computer method and apparatus for determining content owner of a website WO2002010955A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2001273513A AU2001273513A1 (en) 2000-07-31 2001-07-17 Computer method and apparatus for determining content owner of a website

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 (2)

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WO2002010955A3 WO2002010955A3 (en) 2003-08-07

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PCT/US2001/022385 WO2002010956A2 (en) 2000-07-31 2001-07-17 Computer method and apparatus for determining site type of a web site
PCT/US2001/022381 WO2002010955A2 (en) 2000-07-31 2001-07-17 Computer method and apparatus for determining content owner of a website
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

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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

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