U.S. patent application number 14/673833 was filed with the patent office on 2015-12-03 for online marketplace for automatically extracted data.
The applicant listed for this patent is STRAGENT, LLC. Invention is credited to Jonathan Monsarrat.
Application Number | 20150347360 14/673833 |
Document ID | / |
Family ID | 39189933 |
Filed Date | 2015-12-03 |
United States Patent
Application |
20150347360 |
Kind Code |
A1 |
Monsarrat; Jonathan |
December 3, 2015 |
ONLINE MARKETPLACE FOR AUTOMATICALLY EXTRACTED DATA
Abstract
A system for automatically locating and data-typing information
originating from many Web pages, and then collecting that
information in a database. The database is then made available via
an online data marketplace which allows users from different
organizations to buy and sell related data, associated
advertisements, and access to the communities of end-users who may
also view advertisements and make purchases.
Inventors: |
Monsarrat; Jonathan;
(Cambridge, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STRAGENT, LLC |
Longview |
TX |
US |
|
|
Family ID: |
39189933 |
Appl. No.: |
14/673833 |
Filed: |
March 30, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13691612 |
Nov 30, 2012 |
8996594 |
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14673833 |
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13172771 |
Jun 29, 2011 |
8352419 |
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13691612 |
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12620573 |
Nov 17, 2009 |
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13172771 |
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11521072 |
Sep 14, 2006 |
7647351 |
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12620573 |
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Current U.S.
Class: |
715/760 |
Current CPC
Class: |
G06F 40/169 20200101;
G06F 40/14 20200101; G06F 16/24573 20190101; G06Q 50/01 20130101;
G06Q 30/0282 20130101; G06Q 30/06 20130101; G06F 3/0484 20130101;
G06Q 30/0217 20130101; G06F 16/22 20190101; G06Q 30/0601
20130101 |
International
Class: |
G06F 17/22 20060101
G06F017/22; G06Q 30/06 20060101 G06Q030/06; G06F 17/24 20060101
G06F017/24; G06F 3/0484 20060101 G06F003/0484; G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for extracting information from a collection of source
pages, comprising: identify a schema for a domain that defines data
relationships and data types expected for source pages in a given
domain; for a specific source page, creating a template associated
with the source page; allowing a user to identify a region using
the source page; and for the identified region, using user input to
determine a data type and inter-relationship to other data.
2. A method as in claim 1 further comprising: accepting user input
identifying the highlighted region; examining the schema; and
displaying a list of likely data types.
3. A method as in claim 1 additionally comprising: for a plurality
of origin pages in the domain; matching the template to the source
page to identify data elements in the source page that match the
annotated data in the template; and storing data elements in a
database associated with the domain, based on the schema.
Description
RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 13/691,612 filed on Nov. 30, 2012, which is a
continuation of U.S. patent application Ser. No. 13/172,771 filed
on Jun. 29, 2011, now U.S. Pat. No. 8,352,419, which is a
continuation of U.S. patent application Ser. No. 12/620,573 filed
on Nov. 17, 2009, which is a continuation of U.S. patent
application Ser. No. 11/521,072 filed Sep. 14, 2006, now issued
under U.S. Pat. No. 7,647,351.
BACKGROUND OF THE INVENTION
[0002] The World Wide Web contains billions of pages of freely
available information, such as airplane arrival times, baseball
statistics, and product descriptions. However, much of that
information is embedded in running prose intended for reading by
humans. A human is best equipped, for example, for locating the
information on a Web page, giving it a data type (whether "1938" is
a calendar year, the price of a product, or an airline flight
number), and relating it to other data ("this picture located here
depicts that product located there"). This manual process is
time-intensive and error-prone.
[0003] There are current two ways to extract data automatically
from a Web page, a process which is called "Web scraping". First,
every Web page contains hidden mark-ups for formatting, such as
boldface and italics. Theoretically, these mark-ups can help a
computer algorithm locate information on a page. Unfortunately,
every Web site has a different look and feel, so each Web page
needs its own custom algorithm. Writing a custom algorithm is
time-intensive, but possible on a small scale, such as a price
comparison website which gathers product information from a dozen
sources. But there is no efficient way to scale this approach up to
thousands or millions of Web sites, which would require thousands
or millions of custom algorithms to be written.
[0004] The second method requires the owner and developer of each
Web site to add hidden mark-ups that specifically designate
information and its data type. The preferred technology for this is
XML. Unfortunately, nearly all Web sites are not built this way,
and there are no standardized terms for XML usage. It is a little
like saying that if only everyone would speak Esperanto, there
would be no translation problems. This is true in theory, but
hopelessly impractical.
[0005] Once data has been collected, there are no good mechanisms
for disseminating it. Every Web site that publishes information
stands alone. Each publisher writes its own content, sells its own
ads, and manages its own online community. Web publishers such as
Amazon.com that include others book reviews, and such as The Boston
Globe that include others' newswire stories, require their
partner's active participation to integrate their databases
together. This function is also quite difficult to scale up to
millions of potential partners and the trillions of possible
bilateral partnerships between those potential partners. The matter
becomes even more complicated when advertisements, which can come
from thousands of sources, need to be associated with data and
presented to the end-users who read the publisher's Web site.
Finally, there is currently no easy way for the online communities
of various Web sites to profit from each other's knowledge, forming
a "meta-community" which could, for example, automatically share
movie reviews and ratings across thousands of movie fan Web
communities.
SUMMARY OF THE INVENTION
[0006] There exists a need for a low-cost, highly-automated method
for "scraping" information from the World Wide Web, forming
partnerships to trade this data, and presenting it to readers
alongside advertisements from any source.
[0007] Briefly, the present invention provides a system for
automatically locating and data-typing information from thousands
of Web pages, and then collecting that information in a central
database. The database is then made available via an online data
marketplace which allows users from thousands of different
organizations to buy and sell related data, associated
advertisements, and access to the communities of end-users who may
also view advertisements and make purchases. These innovations may
be used together or separately.
[0008] Web pages contain running text, in English or some other
language, which is designed to be read by humans. Thus, extracting
the data embedded in that text, data type information and context
would seem to be a difficult problem for a computer algorithm.
However, some automation is possible because many Web pages can be
grouped as similar in appearance and format. For example, every
book description Web page on Amazon.com looks the same as every
other. If a human locates and types information on one Amazon.com
Web page, then a computer may be able to locate and type data on
all of the millions of similar-looking Web pages on Amazon.com. The
challenges are then: [0009] (a) What is the best user interface for
a human to identify for a computer which element of a Web page
contains the desired information, and the information's data type
and relation to other data? [0010] (b) What is the most flexible
way to store and communicate this knowledge? [0011] (c) How can a
computer generalize from one Web page to extracting information
from millions of similar looking Web pages, even if they do not a
match precisely? [0012] (d) In what ways can the need for human
involvement be minimized, and probable errors be identified
automatically for review? [0013] (e) What is the best user
interface to report errors to a human and have them step in to fix
the situation? [0014] (f) What modifications are required to target
specific vertical markets?
[0015] These problems are solved with a method according to a
preferred embodiment of the invention in the following way: [0016]
(a) Using the mouse and a Web browser, a human interacts with a
program (such as running on an application server) and highlights
information on a page and right-clicks to bring up a
dynamically-generated menu to permit the user to select the data
type. [0017] (b) Information as to data type is then stored
directly into a copy of the Web page by the server. [0018] (c) Web
pages typically include not only prose but also text formatting
markup tags (such as <b> that cause text to be displayed in
boldface). The server can match an element on a template to an
element on a source Web page to another by defining a set of
"contextual clues" that characterize an element's location in the
context of its Web page. Then the server makes a map of these
features, which can be used later to navigate around the Web page.
[0019] (d) Natural language algorithms using word frequency
statistics can also he used to characterize extracted data, and
thus provide suggestions to the human user for rapid information
location and data typing. These word frequency statistics can also
be used to evaluate the result of automated extraction for likely
correctness. [0020] (e) An interface similar to the debuggers used
for computer programming languages can be used to report the
results of data typing. [0021] (f) For specific vertical markets,
the data may be extracted as lines of text that require further
processing (e.g. extracting the time-of-day from a text string such
as "Hours of Operation: Monday to Friday, 8 am to 5 pm, except
Holidays"). Specially written parsing algorithms can be used,
because the vocabulary in such a domain is limited (e.g., to
determining time-of-day ranges).
[0022] Once data has been collected, a further mechanism can be
employed so that the data can be freely traded and published. A
database suitable for storing information scraped from Web sites,
in one embodiment, differs from standard databases in several ways:
[0023] (a) The Web page that is the source for the data may change
regularly, requiring a moderator to configure an information flow
rather than store static data 1006; [0024] (b) Data may be sourced
from numerous Web pages, which should be assembled 506; [0025] (c)
Users of the database, e.g., a publisher of a Web site, may have a
community that will contribute numeric ratings, and prose
commentary and the like to the data 1004; managing this centrally
so that the opinions of differing communities can be shared is
another desirable feature 1006; [0026] (d) Publishers of Web
information may often want to associate advertisements with the
data, in as targeted a way as possible, to achieve the highest
level of accuracy. Targeting advertisements towards information
scraped from Web site may require special algorithms 1010; and
finally [0027] (e) Web scraping algorithms may occasionally gather
the wrong information, requiring a technique to automatically
identify and reject this information 507.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] The foregoing and other features, and advantages of the
invention will be apparent from the following more particular
description of preferred embodiments of the invention, as
illustrated in the accompanying drawings in which like reference
characters refer to the same parts throughout the different
views.
[0029] The foregoing will be apparent from the following more
particular description of example embodiments of the invention, as
illustrated in the accompanying drawings in which like reference
characters refer to the same parts throughout the different views.
The drawings are not necessarily to scale, emphasis instead being
placed upon illustrating embodiments of the present invention.
[0030] FIG. 1 is a high level diagram of a data processing
environment in which the invention may be implemented.
[0031] FIG. 2 illustrates a data schema that defines data typing
and data inter-Relationships.
[0032] FIG. 3a is a copy of a Web page with data.
[0033] FIG. 3b is a sequence of steps for setting up a Web page to
be "scraped".
[0034] FIG. 3c is a template: a copy of Web page with data marked
up.
[0035] FIG. 4 illustrates a Web page that has been set up with
marks.
[0036] FIG. 5a illustrates a sequence of steps for "Web scraping":
gathering data from Web Sites.
[0037] FIG. 5b is a visual representation of Web scraping in
action.
[0038] FIG. 6 illustrates example contextual clues and navigational
steps to provide clues for navigating through a Web page.
[0039] FIG. 7 is a conceptual diagram illustrating how the
processes match elements on a template to elements on a source Web
page.
[0040] FIG. 8 illustrates a sequence of steps for how elements are
located on the source Web page.
[0041] FIG. 9 illustrates an example page where the locations of
elements containing the desired information have been
identified.
[0042] FIG. 10 illustrates an online marketplace for information
scraped from Web sites and a "meta-community".
[0043] FIG. 11 is an example of a personalized Web page of
activities embedded in a Web publisher's own Web site.
DETAILED DESCRIPTION OF THE INVENTION
[0044] A description of preferred embodiments of the invention
follows.
Overview
[0045] This preferred embodiment is in the arts & entertainment
industry. Arts and entertainment events are typically listed across
thousands of Web sites. Gathering, trading, and publishing this
information is of substantial value to Web Publishers 111
Advertisers 108, and the Online Community 112 for each of the
published Web sites.
[0046] FIG. 1 shows an overview of a data processing environment
which the invention ma be used. First, the Set Up Expert 100
characterizes the data domain of the data to be gathered from the
Web, using a Data Schema 113. For example, if the data domain is
automobiles then the Data Schema 113 would specify that cars have a
make, model, and year of manufacture. Having built the Data Schema
113, the Set Up Expert 100 uses the Set Up System 101 to browse to
a Web page and mark the location of information, creating a
template. This may be repeated across thousands of Web sites, but
one template will usually suffice for a single page, and an entire
group of Web pages that have similar look and feel, for all time
throughout their changes and updates. A Web server then uses this
configuration for Daily Web "Scraping" 103, a term which means
reading source Web pages and extracting information using the
appropriate template.
[0047] The extracted information is stored in a Database 104. This
Database 104 feeds data into a Publishing System 110 which can be
used by each of several Web Publishers 111 to provide information
to their own Online Community 112, of which there is one for every
Web publisher. The Database 104 is itself fed by an Online Data
Market 105, which allows Buyers 106 and Sellers 107 to freely trade
primary and auxiliary information relating to data flows that come
from Web site, effectively creating a meta-community from
potentially thousands of different online communities. An Ad System
109 allows Advertisers 108 to register advertisements with the
system, which are matched with information in the Online Data
Market 105. This matching presents advertisements to the Online
Community 112 that are relevant to their interests and thus more
likely to stimulate Advertisers 108 to spend money.
Setting Up a Web Page to be "Scraped"
[0048] Because the data domain is Arts and Entertainment event
listings, the Set Up Expert 100 characterizes this data domain by
creating a Data Schema 113. A typical way to do this would be using
the database language SQL, or as class definitions in Java. FIG. 2
shows an example Data Schema 113, the Data Schema for Arts &
Entertainment Event Listings 200, which defines for each data
class, its data type, and its data inter-relationships. For
example, each Activity 202 has a Venue 201 and an Organizer 203.
Every Venue 201 has an address. Error-checking information is
included in the schema. For example, addresses should not be more
than 50 words in length. This error-checking information can be
manually set up or computed using statistics from known
examples.
[0049] FIGS. 3a, 3b and 3c illustrate the manual set up that is
required to gather information from a Web site. First the Set Up
Expert 100 identifies target Web sites that are relevant to the
data domain. In this preferred embodiment, the data domain is Arts
and Entertainment events, so the Set Up Expert 100 would target
museum, concert hall, student club, festival organizer, and similar
Web sites. Such sites may contain event calendars with relevant
information embedded within. Once a few target Web sites have been
identified, a statistical algorithm can identify others on the
Internet through word-frequency and word-location matching. The end
result is a group of target Web sites from which information can be
drawn. For example, in New England, there are 3,000 Web sites that
list activities and events. These Web sites, which change
day-to-day, list 100,000 New England activities and events each
month.
[0050] Each Web site can have dozens, thousands, or potentially
millions of Web pages. Each Web page with a unique look and feel
requires a template to he manually set up. However, most Web pages
belong to a group of similar-looking Web pages. A group like this
requires only one representative Web page to be manually set up as
a template. In this example, the Set Up Expert 100 identifies the
Bayside Expo Center as a major venue for conferences in the Boston,
Mass. area. The Bayside Expo Center has a website at a well known
.com address. One Web page on that website is a calendar of
activities happening at the Bayside Expo Center.
[0051] In step 301, The Set Up Expert 100 directs the Set Up System
101 to make a copy of the calendar of events of the Bayside Expo
Center, resulting in a Copy of Web Page With Data 300. The Copy of
Web Page With Data 300 is simply a copy of the Hyper Text Markup
Language (HTML) of the original Web page.
[0052] In this example, The Copy of Web Page With Data 300 contains
information about the event, including its name, "The World of
Wheels" 319, its time span, "January 6-January 8" 320, and its
organizer, "Championship Auto Shows" 321. We also know that the
event takes place at the Venue for this website, The Bayside Expo
Center, The Set Up Expert 100 wants to teach the system how to
automatically scrape this information from the page and all other
Web pages in the group of similar-looking pages, which comprise the
entire calendar of the Bayside Expo Center.
[0053] In step 301, The Copy of Web Page With Data 300 is displayed
in a Web browser on which is running a Java applet. As shown in
FIG. 3a, Set Up Expert 100 uses the mouse to highlight items on the
page. First, the user assigns a type to the entire page, by
highlight the "entire page" element 310 at the top of the page and
right-clicking with the mouse. A dynamically generated drop-down
menu 312 appears listing the data types in the Schema 200. The user
selects Venue 201 from the list, because this Web site is owned by
The Bayside Expo Center, which is a venue. Then the user highlights
the entire Activity 314, and right-clicks with the mouse.
[0054] This time the drop-down menu 312, which is dynamically
generated, makes some guesses about the data type that is most
appropriate for the element that was just highlighted. Since the
page itself is a Venue 201, and the Data Schema for Arts &
Entertainment Event Listings 200 says that every Activity 202 has a
Venue 201, one of the elements of the drop-down menu will be
Activity 316, which the user selects, In this way the dynamically
generated drop-down menu 312 is making it simpler and faster for
the user to identify data types, by automatically suggesting what
seems most relevant. Word frequency statistics can be used in the
creation of such suggestions. For example, if the user highlights a
10 digit number with dashes that is most likely a phone number, the
drop-down menu would place "Phone Number" at the top of the
dynamically generated drop-down menu.
[0055] In step 302, the Set Up Expert 100 highlights "World of
Wheels" 319. Then in step 303, the user right-clicks, again
bringing up a dynamic drop-down menu. According to the Data Schema
for Arts and Entertainment Event Listings 200, each Activity 202 is
associated with a name, hours of operation, organizers, and other
data. These possibilities are listed in the dynamically created
drop-down menu, and the user selects "name" 322. Then in step 304,
the computer then places special annotations into the Copy of Web
Page With Data 300 to record these facts.
[0056] Similarly, in step 305, the Set Up Expert 100 associates
"January 6-January 8" 320 as the time span for the event, and
"Championship Auto Shows" 321 as an organizer 326 (see FIG. 3c).
This information is displayed in The Copy of Web Page With All Data
Marked Up 207. When the user is finished, in step 306, the Set Up
System 101 stores the Copy of Web Page With Data 300 as a template
for future use.
[0057] This Template Contains: [0058] The original Web page's HTML
full [0059] Annotations showing: [0060] The location of the element
on the Web page that contains the desired information [0061] The
data type of the information [0062] The relation between this
information and other data on this page or elsewhere
[0063] The drop-down menu 312 includes the item "anchor", which
allows the user to indicate that the highlighted text on the Web
page should never change. This annotation would also be stored as
an embedded tag in The Copy of Web Page With Data 300.
[0064] The drop-down menu 312 also includes the item "link", which
allows the user to indicate that a link on the Web page is
important. Any link the user clicks on is automatically read as
important, as well. The intention is that during the Web scraping
phase, if a Web page being read contains a link, the Web page being
linked to will also be scraped, using the appropriate template.
[0065] Finally, the user may also indicate that some text region of
the Web page is a list of blocks, and each block is treated as if
it were a separate Web page with its own template. For example, the
calendar of events at the Bayside Expo Center is one big list of
identically formatted event summaries, each of which links through
to an identically formatted event details page. A template from one
of the event detail pages will thus suffice to read information
from the rest.
[0066] FIG. 4 shows the resulting embedded markups in the Template:
A Copy of Web Page With Data Marked Up, in HTML Format 400. The
special annotations created by the Set Up System 101 are
highlighted. There is no difference between this and the Template:
A Copy of Web Page With Data Marked Up 307. It is the same HTML
page displayed differently--first in a Web browser and then in raw
text format.
"Web Scraping": Gathering Data from Web Sites
[0067] Once the Set tip Expert 100 has marked up several or
possibly thousands of Web sites, FIG. 5a illustrates how data is
gathered.
[0068] Web scraping is run as a batch job on Daily Web Scraping 103
that can be repeated monthly, daily, hourly, or more frequently.
Different data domains will tend to change more or less frequently,
requiring more or less frequent Web scraping. An event calendar,
for example, may be updated daily, but a Web page with stock market
fluctuations may change every minute.
[0069] The starting point in Step 500 is to gather all the
templates from the Database 104 that are associated with a
permanent URL. A permanent URL, for example, would be the home page
of the Bayside Expo Center events calendar, which resides at a
known URL and will never be located elsewhere. Other templates,
those without a permanent URL, are accessed through the
user-identified links on Web pages already being processed.
[0070] Then in Step 501, all the templates with permanent URLs are
sent for processing, Step 502. The first step in processing, Step
503, is to use the URL to fetch a source Web page in real-time from
the Internet. This source page is fully up-to-date with whatever
information the Web publisher owning that Web page has got
currently posted on their website. Then the server applies the
template to the source Web page, matching the elements of the
template to the elements of the Web page, and extracting the
desired information, its data type, and its inter-relationship to
other data. Exactly how this is done is described in the next
section. For example, the Bayside Expo Center events page would be
loaded and compared with the appropriate template. The big list of
events would be discovered.
[0071] Then in Step 504, if the source Web page contains any lists,
those lists are now processed. For example, a list 530 was found on
the event calendar page of the Bayside Expo Center in FIG. 5b. A
list is a series of blocks 509, each on one line, each of which is
processed against a template just as Web pages are processed
against templates in 503. In this case, the Bayside Expo Center has
a series of brief event descriptions which link into pages with
detailed descriptions, such as the "World of Wheels" page shown in
300. Each of these brief event descriptions is scraped for
information.
[0072] The last step in processing a template against a URL is Step
505, to handle any links that were discovered in the list. Each of
the blocks 509 on the Bayside Expo Center event calendar list has a
link, as noted in the previous paragraph. Each link is associated
with the template for scraping the Web page that is linked to. As
one example, there is a link 550 to the "Boston Home Show" event
page. The Web Scraper 103 proceeds to load the page linked to, the
"World of Wheels" page. The template 307 derived from the "World of
Wheels" event page 307 is compared against the "Boston Home Show"
event page, a comparison is made, and data is extracted 560. The
extracted data is as then stored with their data types (Venue 201,
Activity 202, etc.).
[0073] To summarize, the entire Web site can be read when the Set
Up Expert 100 has only set up two pages, the Bayside Expo Center
events calendar page and the World of Wheels event details page.
From this rapid manual labor, the Daily Web Scraping 103 can now
proceed automatically and read every events page on the entire
website, both that day and every day in the future.
[0074] Finally, after all the pages and the pages they link to have
been read and processed, in Step 506, the data that has been
gathered is post-processed to connect data together, resolve
conflicts, and report possible errors. Then in Step 507, using the
Set Up System 101, the Set Up Expert 100 corrects any remaining
errors and resolves any remaining conflicts. The resulting data may
resemble A Visual Representation of Web Scraping in Action 508.
How Information is Located on the Web Page
[0075] Given a template, such as Template: A Copy of Web Page With
Data Marked Up, 307, and a page to read, such as the "Boston Home
Show" page on the Bayside Expo Center (see FIG. 9), how can the
computer locate and data-type fields such as Title: "Boston Home
Show", Hours: "January 13-January 15", Organizer: "Pat Hoey
Productions", as shown in A Visual Representation of Web Scraping
in Action 508? Since the data-type is embedded in the template 307,
the problem can be distilled down to location. Once we have matched
every element in the template indicating desired information with
the corresponding element in the source Web page, the data typing
and data inter-relationships are simply given from the template's
element.
[0076] FIG. 5 illustrates the contextual clues needed to locate
information on a Web page. In Many Locations Exist on the Source
Web page 600, there are nine locations identified, all HTML tags,
white space, or running text such as "Boston Home Show". The trick
is to identify which location on the source Web page ("Boston Home
Show") matches up with the highlighted location on the template
("World of Wheels").
[0077] Every location has contextual clues, such as which tag
surrounds or precedes it, as shown in Contextual Clues Helping
Specify a Location 601. In addition, two adjacent locations will
have a relationship to each other, as illustrated in Adjacency
Relationships In-Between Neighboring Elements 602. This information
helps identify matches between elements on the template and
elements on the source Web page, even though we cannot rely on the
source Web pages associated with a template to have identical
formats today and for all time. The text is likely to vary
significantly, and the tags and general structure of the source Web
page may change slightly too.
[0078] FIG. 7 shows the approach to matching up the elements of the
template with the corresponding elements of the source Web page.
The algorithm for matching locations between a template and a
source Web page begins with the matches that are highest
confidence, which become "anchors". Those anchors give further
contextual clues to place down other locations in-between known
anchors.
[0079] FIG. 8 is a formal description of the algorithm for locating
information on a source Web page using a template. In step 800, a
range is defined between the start and end points of the two Web
pages being matched. In step 801, every known template element F is
examined, and every possible location of that element E on the
source Web page is examined, to find all the E-and-F match ups in
which we have very high confidence. As shown in step 802, this is
done using the above described contextual clues and adjacency
relationships as a scoring system and using a weighted least
squares algorithm. In step 803, if no high-confidence matches are
found, the algorithm recursively backtracks and may signal a human
for assistance.
[0080] In step 804, we choose the highest confidence match is
chosen and in step 805 this becomes an anchor point, splitting the
START-to-END region into two regions: START-to-ANCHOR, and
ANCHOR-to-END. This transforms the problem into smaller regions
where all of the neighboring locations to ANCHOR can now be located
by returning to step 801.
[0081] Although this would seem to be a slow algorithm, since it
involves all combinations of E and F, in practice there are
typically several unique or very high confidence matches which can
be located immediately, dividing the problem into small fragments.
One complexity is that since things may be added or deleted from a
Web page over time, a separate weighted least squares algorithm
evaluates the possibility that one of the elements of the template
simply does not exist in the source Web page, or exists but
something additional has been added.
Online Market for Data Scraped from Web Sites
[0082] Historically, online marketplaces have been created for
buying and selling antiques or trading stock over the Web. However,
trading the data scraped from Web sites presents new features.
Referring to FIG. 10, [0083] Web Publishers 1001 act as brokers for
buying and selling information for their respective Online
Communities 1002 [0084] Not only are Web Publishers 1002 charged
monetarily for buying and rewarded monetarily for selling; their
Online Communities 1002 may bear costs or reap rewards as well. How
best to managing these flows is an issue. [0085] Information
generated by Online Communities 1002 should be policed for
accidental or malicious error [0086] The information that is to be
traded is of a form never traded before: [0087] Event experts who
sell reviews, photographs [0088] Communities who share their
ratings (each community's ratings can be weighted when combined)
[0089] Moderators who choose a stream of events, like a DJ chooses
which music to play [0090] Access to advertisers and access to
communities [0091] Event experts who use category tags to label an
event for easy reference [0092] Data scraped from the Web is not
static; it is a flow that is frequently changing [0093] Finally,
Advertisements can be targeted to differing communities based on
their differing statistics, increasing the effectiveness of ads and
therefore how much advertisers will pay.
[0094] What is happening is similar to podcasting. Audio broadcasts
have traditionally been expensive and complex to produce, and were
dominated by large corporations through radio stations. The
Internet made it possible for hobbyists to inexpensively produce
their own audio shows, leading to a boom in creativity and content.
In a similar way, although online communities have existed for over
a decade, for the first time, through the Online Data Market 1000,
an entire community can act together to "broadcast" information to
other communities. Online communities become lightweight and
inexpensively created and managed. This paradigm explicitly
includes a commercial buy and sell model, fostering incentives and
creating one huge meta-community for any data domain.
[0095] In previous sections of this description of a preferred
embodiment, a regular daily scraping of thousands of arts &
entertainment Web sites has been set up, creating an ever-changing
data flow of arts & entertainment activity listings.
[0096] Now, in Step 1005, a Web Publisher 1001 configure this
stream of activities, choosing which portion of the whole will
appear on his or her Web site for his or her Online Community 1002.
The first way this can be done is through performing a query to the
Database 104 and saving that under a name. This query is then
optimized so that updates are selected as new information is added
to the Database 104 by the Daily Web Scraping 103. This query may
be based on keywords, or on category tags. A category tag is text
word such as "Over-18", "Handicapped-Access" or "Free" that can be
applied to an event explicitly in an attempt to categorize it A
statistical matching algorithm is used to automatically apply
category tags based on the text of a source Web page, starting from
a seed of user-applied tags.
[0097] In Step 1005, Web Publisher 1001 has now configured a
personalized Web page on the Publishing System 110 which can be
accessed from his or her own Web site by link or by including it as
a frame or table inside one of the Web Publisher's 1005 own Web
pages. FIG. 11 shows an example of this, where the activity
listings from Visual Representation of Web Scraping in Action 508
have been inserted into a Web Publisher's 1001 Web page. This
personalized Web page will fill in automatically with activity
data. This stream of information can run freely from the database
to the online community, or each event can be moderated
individually for approval before being presented to the online
community.
[0098] Then, in Step 1004, the Online Community 1002 adds content
such as reviews, photographs, interviews, and ratings. This content
may be free or it may be compensated for by the Web Publisher
1005.
[0099] Then, in Step 1006, the Web Publisher 1001 configures rules
for how the content created in Step 1004 by the Online Community
1002 is to be sold, if at all. The community's reviews in plain
text, and photographs with captions can be bought and sold.
[0100] The key problem of selling content created by a community is
that the overall quality of volunteers is usually amateurish and
not very good. However, in Step 1007, the Online Data Market 1000
can help the Web Publisher 1001 moderate the content and separate
the good from the bad by assigning a utility score to the content
that members of the Online Community 1002 are contributing. Based
on these utility scores, the Web Publisher 1001 can approve content
for sale through the Online Data Market 1000, or manually intervene
to remove accidentally or maliciously erroneous content.
[0101] In Step 1007, different types of content require different
utility scoring algorithms. The quality of the submission can be
automatically judged based on (a) statistics' involving the words
in the plain text and photograph captions; (b) how often a Web
visitor clicks on the content; (c) how long a Web visitor spends
looking at the content; and (d) explicit ratings given by Web
visitors. Some users may be trusted and have immediate permission
to sell information into the Online Data Market 105 on behalf of
the online community.
[0102] Then in Step 1008, a different Web Publisher 1003 wants to
draw information from the Online Data Market 1000 for its own Web
Community 1004. This may be a selling--the Web Publisher 1003 may
charge to publish any listing. Or, the data may be valuable enough
that the Web Publisher 1003 is buying it from Web Publisher 1001.
Web Publisher 1003 configures the system to determine which
communities information will be drawn from, what prices are
reasonable to pay, and whether content will be sparse or deeply
filled in. Web Publisher 1003 can also outsource the entire
moderation of the event stream through the Online Data Market 1000.
This would be similar to one DJ selling a playlist of music to
another DJ every day.
[0103] Based on demand and that configuration, in Step 1009 the
Online Data Market 1000 determines the appropriate prices and
handles the transition of money. In this case, instead of trading
purely for money, Web publishers 1001, 1003 accrue "points",
similar to how airlines use "air miles". Although these points can
be redeemed for cash, they can also be used to provide services for
an online community. For example, if Bugaboo Creek Steakhouse has
an advertisement with a coupon good for $15 off a meal, the
publisher 1001 may spend points to purchase 250 of these coupons
and present them to his or her online community. Creating
incentives for the community to provide content, the Web publisher
can take a cut and then finance the original incentives by sales
into the Online Data Market 1000.
[0104] Additionally, in Step 1010, algorithms can select and
suggest content for the end-user based on their explicit tastes
(ratings) and their implicit tastes as demonstrated by their
browsing history and the community they have chosen to join. These
algorithms can select for the most relevant content and serve to
sort lists of events with the ones most likely to be of interest on
the top. Additionally, advertisements can be selected by an
algorithm that matches ads with the end-users most likely to click
on them.
[0105] Finally, in Step 1011, Ratings that are contributed by the
Online Community 1006 need to be combined with the ratings from
other communities. This is done using a weighted scoring system
that is balanced from what the end-users tastes seem to be, as
recorded by the history of browsing events.
[0106] In addition to this, a Publishing System 110 allows any Web
publisher to manage the online community, and annotate events and
activities with additional expert content, such as reviews,
ratings, and photography. An Advertising System 109 allows
advertisers to post their own ads and configure the system with
hints about which events and category tags would be most relevant
to the ad. This information is then used when determining which ads
to show to end-users.
[0107] While this invention has been particularly shown and
described with references to preferred embodiments thereof, it will
be understood by those skilled in the art that various changes in
form and details may be made therein without departing from the
scope of the invention encompassed by the appended claims.
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