U.S. patent application number 11/399857 was filed with the patent office on 2007-06-21 for mechanism for managing facts in a fact repository.
Invention is credited to Jonathan Betz, Andrew Hogue.
Application Number | 20070143317 11/399857 |
Document ID | / |
Family ID | 37309560 |
Filed Date | 2007-06-21 |
United States Patent
Application |
20070143317 |
Kind Code |
A1 |
Hogue; Andrew ; et
al. |
June 21, 2007 |
Mechanism for managing facts in a fact repository
Abstract
Methods and systems for processing facts with one or more
janitors. Facts are extracted from documents on the Internet or
other sources. Facts can be any data or series of data in the
documents including an attribute and a file. The data can be in the
form of text, graphics, or multimedia content. Janitors transform
facts responsive to inferring a certain condition associated with
facts. The condition can be related to one or more of an attribute,
a value, or an object of a fact being analyzed. For example,
janitors can perform normalization, remove or merge similar or
duplicate facts, segregate multiple values of a fact, and the like.
An administrator can select which janitors are applied to facts and
in which order.
Inventors: |
Hogue; Andrew; (Ho Ho Kus,
NJ) ; Betz; Jonathan; (Summit, NJ) |
Correspondence
Address: |
GOOGLE / FENWICK
SILICON VALLEY CENTER
801 CALIFORNIA ST.
MOUNTAIN VIEW
CA
94041
US
|
Family ID: |
37309560 |
Appl. No.: |
11/399857 |
Filed: |
April 7, 2006 |
Related U.S. Patent Documents
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Application
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Patent Number |
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11024784 |
Dec 30, 2004 |
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11399857 |
Apr 7, 2006 |
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11142853 |
May 31, 2005 |
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11399857 |
Apr 7, 2006 |
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11341069 |
Jan 27, 2006 |
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11399857 |
Apr 7, 2006 |
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11356838 |
Feb 17, 2006 |
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11399857 |
Apr 7, 2006 |
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11356765 |
Feb 17, 2006 |
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11399857 |
Apr 7, 2006 |
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Current U.S.
Class: |
1/1 ; 707/999.1;
707/E17.108; 707/E17.124 |
Current CPC
Class: |
G06F 16/84 20190101;
G06F 16/93 20190101; G06F 16/951 20190101; G06F 16/353 20190101;
Y10S 707/962 20130101 |
Class at
Publication: |
707/100 |
International
Class: |
G06F 7/00 20060101
G06F007/00 |
Claims
1. A computer-implemented method for processing facts extracted
from web-based-documents, comprising: extracting a fact from a
plurality of web-based documents stored on document hosts, the fact
comprising an attribute and a value, at least two of the web-based
documents presenting the fact in different formats; applying two or
more janitors to the fact, each janitor inferring one or more
conditions associated with the fact from at least one of the
attribute and value, and responsive to inferring the one or more
conditions, each of the two or more janitors transforming the fact
in accordance with a different predetermined constraint for the
condition by adjusting at least one of the attribute and value; and
storing the transformed fact in a fact repository.
2. The method of claim 1, wherein the applying two or more janitors
comprises: applying a first and a second janitor in a predetermined
order to infer a first condition and a second condition,
respectively, wherein the second janitor is able to detect the
second condition responsive to applying the first janitor.
3. A computer-implemented method for processing facts extracted
from web-based-documents, comprising: extracting a fact from an
unstructured web-based document, the fact containing an attribute
and a value; inferring a condition associated with the fact from at
least one of the attribute and value; responsive to inferring the
condition, transforming the fact to a predetermined constraint for
the condition by adjusting at least one of the attribute and value;
and storing the transformed fact in a fact repository.
4. The method of claim 3, wherein: inferring the condition
comprises inferring a type for the fact, and transforming the fact
comprises transforming the fact to a predetermined constraint for
the fact type.
5. The method of claim 3, further comprising: receiving another
fact, wherein inferring the condition comprises inferring that the
facts have the same condition, and wherein transforming the fact
comprises comparing the facts.
6. The method of claim 3, further comprising: receiving another
fact containing another attribute and another value, wherein
inferring the condition comprises detecting that at least one of
the attributes or the values are similar, wherein transforming the
fact comprises merging the facts.
7. The method of claim 3, wherein: transforming the fact comprises
reformatting the value according to the predetermined
constraint.
8. The method of claim 3, wherein: inferring the condition
comprises inferring that the value includes two or more independent
values, and transforming the fact comprises generating two or more
new facts, each one of the two or more new facts having one of the
two or more independent values.
9. The method of claim 3, wherein: inferring the condition
comprises inferring the condition based on an object associated
with the fact, the object being common to both the fact and at
least one other fact.
10. The method of claim 3, further comprising: retrieving the fact
from storage in the fact repository.
11. The method of claim 3, further comprising: receiving the fact
during extraction from the web-based document.
12. The method of claim 3, wherein the web-based document is
encoded in Hypertext Markup Language, and the fact is extracted
from an HTML table in the document.
13. A computer-implemented method for processing facts extracted
from documents, comprising: extracting a fact from an unstructured
web-based document, the fact containing an attribute and a value;
applying a collection of janitors in a predetermined order to
process the fact, each janitor configured to infer a different
condition associated with a fact from at least one of the attribute
and value, and responsive to the janitor inferring the specific
condition, the janitor transforming the fact to a different
predetermined constraint for the condition by adjusting at least
one of the attribute and value, and responsive to the janitor
failing to infer the specific condition, the janitor discontinuing
processing of the fact; and storing the fact in a fact repository
of the computer.
14. A computer program product stored on a computer readable medium
and configured to perform a method for processing facts extracted
from unstructured web-based documents and stored in a repository,
comprising: extracting a fact containing an attribute and a value,
the fact extracted from an unstructured web-based document;
inferring a condition associated with the fact from at least one of
the attribute and value; responsive to inferring the condition,
transforming the fact to a predetermined constraint for the
condition by adjusting at least one of the attribute and value; and
storing the facts in the fact repository.
15. The computer program product of claim 14, wherein: inferring
the condition comprises inferring a type for the fact, and
transforming the fact comprises transforming the fact to a
predetermined constraint for the fact type.
16. The computer program product of claim 14, further comprising:
receiving another fact, wherein inferring the condition comprises
inferring that the facts have the same condition, and wherein
transforming the fact comprises normalizing the facts.
17. The computer program product of claim 14, further comprising:
receiving another fact containing another attribute and another
value, wherein inferring the condition comprises detecting that at
least one of the attributes or the values are similar, wherein
transforming the fact comprises merging the facts.
18. The computer program product of claim 14, wherein: transforming
the fact comprises reformatting the value according to the
predetermined constraint.
19. The computer program product of claim 14, wherein: inferring
the condition comprises inferring that the value includes two or
more independent values, and transforming the fact comprises
generating two or more new facts, each one of the two or more new
facts having one of the two or more independent values.
20. The computer program product of claim 14, wherein: inferring
the condition comprises inferring the condition based on an object
associated with the fact, the object used as a common indexer for
both the fact and at least one other fact.
21. The computer program product of claim 14, further comprising:
retrieving the fact from storage in the fact repository.
22. The computer program product of claim 14, further comprising:
receiving the fact during extraction from the document.
23. The computer program product of claim 14, wherein the web-based
document is encoded in Hypertext Markup Language, and the fact is
extracted from a table as indicated within the encoding.
24. A system for processing facts extracted from documents,
comprising: an extractor to extract facts, the facts each
containing an attribute and a value, the facts extracted from a
plurality of unstructured web-based documents; two or more janitors
configured to receive a fact, each of the two or more janitors
configured to operate in a predetermined order and to infer a
different specific condition associated with the fact from at least
one of the attribute and value, the two or more janitors configured
to transform the fact to a predetermined constraint for the
condition by adjusting at least one of the attribute and value, and
store the fact.
25. The system of claim 24, further comprising: a script module, in
communication with the one or more janitors, the script to describe
an order for the one or more janitors to process the fact.
26. A method performed by a software janitor in a data processing
system, comprising: receiving a first fact containing an attribute
and a value, the value having a first format when it was been
extracted from an unstructured web-based document; receiving a
plurality of existing facts, the plurality of facts each containing
an attribute and a value, each value having been previously
extracted from an unstructured web-based document, and at least one
of the values having had a second format different from the first
format at the time the existing fact was extracted from the
web-based document; and comparing the value of the first fact to
values of the existing facts to determine whether the first fact
should be stored in a fact repository of the data processing
system.
27. The method of claim 26, wherein comparing to determine whether
the first fact should be stored in the fact repository comprises:
not storing the fact in the fact repository if it duplicates a
threshold number of the existing facts.
28. The method of claim 26, wherein comparing to determine whether
the first fact should be stored in the fact repository comprises
storing the fact in the fact repository if it is corroborated by a
threshold number of the existing facts.
29. The method of claim 26, wherein comparing to determine whether
the first fact should be stored in the fact repository comprises
refraining from storing the fact in the fact repository if it is
not corroborated by a threshold number of the existing facts.
30. The method of claim 26, further comprising: normalizing the
first fact and the at least one existing fact prior to comparing
the first fact and the existing facts.
31. The method of claim 26, wherein comparing the first fact and
the existing facts further comprises being able to compare facts in
the first and second formats.
32. The method of claim 26, wherein receiving the plurality of
existing facts comprises receiving the plurality of existing facts
from the fact repository.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part the following
applications, all of which are incorporated by reference herein:
[0002] U.S. application Ser. No. 11/024,784, entitled
"Supplementing Search Results with Information of Interest", filed
on Dec. 30, 2004, by Jonathan T. Betz; [0003] U.S. application Ser.
No. 11/142,853, entitled "Learning Facts from Semi-Structured
Text", filed on May 31, 2005, by Shubin Zhao, Jonathan T. Betz;
[0004] U.S. application Ser. No. 11/341,069, entitled "Object
Categorization for Information Extraction", filed on Jan. 27, 2006,
by Jonathan T. Betz; [0005] U.S. application Ser. No. 11/356,838,
entitled "Modular Architecture for Entity Normalization", filed
Feb. 17, 2006, by Jonathan T. Betz, Farhan Shamsi; and [0006] U.S.
application Ser. No. 11/356,765, entitled "Attribute Entropy as a
Signal in Object Normalization", filed Feb. 17, 2006, by Jonathan
T. Betz, Vivek Menezes;
[0007] This application is related to the following applications,
all of which are incorporated by reference herein: [0008] U.S.
application Ser. No. 11/366,162, entitled "Generating Structured
Information," filed Mar. 1, 2006, by Egon Pasztor and Daniel Egnor;
[0009] U.S. application Ser. No. 11/357,748, entitled "Support for
Object Search", filed Feb. 17, 2006, by Alex Kehlenbeck, Andrew W.
Hogue; [0010] U.S. application Ser. No. 11/342,290, entitled "Data
Object Visualization", filed on Jan. 27, 2006, by Andrew W. Hogue,
David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C. Reynar, David
Alpert; [0011] U.S. application Ser. No. 11/342,293, entitled "Data
Object Visualization Using Maps", filed on Jan. 27, 2006, by Andrew
W. Hogue, David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C.
Reynar, David Alpert; [0012] U.S. application Ser. No. 11/356,679,
entitled "Query Language", filed Feb. 17, 2006, by Andrew W. Hogue,
Doug Rohde; [0013] U.S. application Ser. No. 11/356,837, entitled
"Automatic Object Reference Identification and Linking in a
Browseable Fact Repository", filed Feb. 17, 2006, by Andrew W.
Hogue; [0014] U.S. application Ser. No. 11/356,851, entitled
"Browseable Fact Repository", filed Feb. 17, 2006, by Andrew W.
Hogue, Jonathan T. Betz; [0015] U.S. application Ser. No.
11/356,842, entitled "ID Persistence Through Normalization", filed
Feb. 17, 2006, by Jonathan T. Betz, Andrew W. Hogue; [0016] U.S.
application Ser. No. 11/356,728, entitled "Annotation Framework",
filed Feb. 17, 2006, by Tom Ritchford, Jonathan T. Betz; [0017]
U.S. application Ser. No. 11/341,907, entitled "Designating Data
Objects for Analysis", filed on Jan. 27, 2006, by Andrew W. Hogue,
David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C. Reynar, David
Alpert; [0018] U.S. application Ser. No. 11/342,277, entitled "Data
Object Visualization Using Graphs", filed on Jan. 27, 2006, by
Andrew W. Hogue, David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey
C. Reynar, David Alpert; [0019] U.S. application Ser. No. ______,
entitled "Entity Normalization Via Name Normalization", filed on
Mar. 31, 2006, by Jonathan T. Betz, Attorney Docket No.
24207-11047; [0020] U.S. application Ser. No. ______, entitled
"Determining Document Subject by Using Title and Anchor Text of
Related Documents", filed on Mar. 31, 2006, by Shubin Zhao,
Attorney Docket No. 24207-11049; [0021] U.S. application Ser. No.
______, entitled "Unsupervised Extraction of Facts", filed on Mar.
31, 2006, by Jonathan T. Betz and Shubin Zhao, Attorney Docket No.
24207-11056; [0022] U.S. application Ser. No. ______, entitled
"Anchor Text Summarization for Corroboration", filed on Mar. 31,
2006, by Jonathan T. Betz and Shubin Zhao, Attorney Docket No.
24207-11046; and
BACKGROUND OF THE INVENTION
[0023] 1. Field of the Invention
[0024] The present invention relates generally to database
management, and more particularly, to managing data extracted from
the World Wide Web.
[0025] 2. Background of the Invention
[0026] Data sources often present information in a manner that is
not easily accessible by a user. For example, when the user queries
web pages through a search engine, the user is burdened with
reviewing individual search results for pertinent information. In
other words, the information must be manually synthesized across
several web pages.
[0027] Data stored on the web and similar hyperlinked networks has
no set format and has no set content. Thus, data from the web or
similar networks is often referred to as unstructured data because
it is not received in a specific format and the documents contents
are not necessarily identified as structured fields. Extraction and
processing of data from unstructured sources, such as the World
Wide Web presents unique challenges. Extraction of data from the
Web is especially challenging due to the wide variety of topics
covered and the almost infinite number of authors that are
providing that information. In addition, not all information on the
World Wide Web is factually accurate. In fact, just the opposite is
true. It must be assumed that at least some of the data obtained
from the Web is not true, is incomplete, or is outdated.
[0028] Conventional techniques for harvesting data from sources
such as web pages also are limited by the variety of styles used to
present information. The design of web pages using Hyper Text
Markup Language, or HTML, is a creative process. Information can be
presented in text paragraphs, tables, or across separate web pages
of a domain. Furthermore, information such as a date can be
presented in different formats such a "Dec. 2, 1981", "Dec. 2,
1981", and "12 Dec. 1981." Moreover, similar information harvested
from different sources can cause data duplication.
[0029] For these reasons, what is needed is a method and system for
processing facts extracted from web-based documents to transform to
predetermined constraints.
SUMMARY
[0030] The present invention provides methods and systems for using
a janitor to process facts extracted from the Word Wide Web. In one
embodiment, janitors are software programs that transform facts
into more useful data and/or provide functions to clean up and
corroborate facts. Janitors can also process facts to detect and
process duplicates. Janitors can transform facts responsive to
inferring a certain condition associated with facts. Generally, a
fact is information, data, or a series of data that can be
represented as an attribute and a value. Facts can be in the form
of text, graphics, or multimedia content. For example, a web page
can list a series of presidents in a first column of a table and
list their dates of births in another column. In one embodiment,
facts are extracted from documents on the World Wide Web for
storage in a fact repository. One or more janitors transform facts
in accordance with constraints designed to improve the quality of
facts. In one embodiment, facts can be processed as they are
extracted from documents. In another embodiment, facts can be
retrieved from the fact repository and processed after storage.
[0031] The condition can be related to one or more of an attribute,
a value, or an object of a fact being analyzed. For example,
janitors can perform normalization, remove or merge similar or
duplicate facts, segregate multiple values of a fact, synthesize
new facts from old, and the like. In one embodiment, an
administrator can select which janitors are applied to facts. The
administrator can choose to apply several janitors.
[0032] Advantageously, janitors improve the quality of facts
extracted from the World Wide Web and stored in a fact repository.
The improved facts are more useful and reliable to users.
[0033] The features and advantages described herein are not all
inclusive, and, in particular, many additional features and
advantages will be apparent to one skilled in the art in view of
the drawings, specifications, and claims. Moreover, it should be
noted that the language used in the specification has been
principally selected for readability and instructional purposes and
may not have been selected to circumscribe the claimed
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] The teachings of the present invention can be readily
understood by considering the following detailed description in
conjunction with the accompanying drawings. Like reference numerals
are used for like elements in the accompanying drawings.
[0035] FIG. 1 is a block diagram of a system for gathering facts
according to one embodiment of the present invention.
[0036] FIGS. 2(a)-(e) illustrate example data structures for facts
within a fact repository.
[0037] FIGS. 3(a)-(b) illustrate exemplary data paths for fact
processing according to one embodiment of the present
invention.
[0038] FIG. 4 is a flow chart illustrating a method for processing
facts according to one embodiment of the present invention.
[0039] FIG. 5 is a flow chart illustrating a method for processing
facts according to another embodiment of the present invention.
[0040] FIG. 6 is a flow chart illustrating a method for
transforming facts based on a condition according to one embodiment
of the present invention.
[0041] The figures depict embodiments of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following discussion that alternative
embodiments of the structures and methods illustrated herein may be
employed without departing from the principles of the invention
described herein.
DETAILED DESCRIPTION OF EMBODIMENTS
[0042] Methods and systems for processing facts with janitors are
described. Facts are extracted from documents on the Internet or
other sources. Generally, facts are information, data, or a series
of data that can be represented in a logical form of an attribute
and a value. Facts can be in the form of text, graphics, or
multimedia content. For example, a web page can list a series of
presidents in a first column of a table and list their dates of
births in another column. Janitors are used to transform facts into
more useful data (e.g., to clean-up facts).
Exemplary Systems
[0043] FIG. 1 is a block diagram illustrating a system 100 for
managing facts according to one embodiment of the present
invention. System 100 comprises document hosts 102, object
requestor 152, and data processing system 106. The components are
communicatively coupled through a network 104 (e.g., a data network
such as the Internet, a telephone network, etc.). At a high level,
system 100 can gather and organize facts, and then retrieve facts
in accordance with queries. For example, facts can be gathered from
a set of web pages related to baseball players, and then presented
in response to a query term such as "baseball", "sports", etc.
[0044] Document host 102 comprises one more hosts that store and
provide access to documents. Document host 102 can be implemented
in a computing device (e.g., personal computer, a workstation,
mini-computer, or mainframe, or a PDA) including a processor and
operating system. Document host 102 can communicate over network
104 via networking protocols (e.g., TCP/IP), and be configured to
use application and presentation protocols (e.g., HTTP, HTML, SOAP,
D-HTML, Java). A document comprises facts represented by any data
that are discernable by a machine including any combination of
text, graphics, multimedia content, etc. A document (e.g., an
e-mail, a web page, a file, news group posting, a blog, or a web
advertisement) may be encoded in various formats such as a markup
language (e.g., HTML), an interpreted language (e.g., JavaScript),
an application-specific format (e.g., DOC format for Microsoft
Word, or PDF format for Adobe Reader), or any other computer
readable or executable format. A document can include references to
other documents or other embedded information (e.g., hyperlinks). A
document stored in a document host 102 may be accessed by a Uniform
Resource Locator (URL), or Web address, or any other appropriate
form of identification and/or location. The documents stored by
document host 102 are typically held in a file directory, a
database, or other data repository. A document from which a
particular fact may be extracted is a source document (or "source")
of that particular fact. In other words, a source of a fact
includes that fact (or a synonymous fact) within its contents.
[0045] Data processing system 106 includes one or more importers
108, one or more janitors 110 with a controller 111, a build engine
112, a service engine 114, and a fact repository 115. Each of the
components can be implemented as software modules (or programs)
executed by a processor 116.
[0046] Importers 108 can include one or more modules for different
types of documents (e.g., an HTML importer, a PDF importer, etc.).
Importers 108 processes documents received from document hosts 102
by parsing the data content of documents to identify facts, and
extracting the identified facts from the documents. Importers 108
also determine the subject or subjects with which the facts are
associated, and stores the facts in fact repository 115 as
individual objects of data.
[0047] Janitors 110 can be self-contained software modules, or a
software architecture with a functionality module that can be
customized for a particular function. Janitors 110 manage facts by
processing various combinations of objects, attributes, or values,
according to janitor rules. Janitors 110 can include one or more
modules that each perform a different data management function. An
administrator can configure controller 111 (or a script) to call
janitors 110 based on a specific ordering. For example, if only
dates are extracted from documents, janitors 110 that specifically
operate on dates can be used for processing date facts.
[0048] In one embodiment, janitors 110 infer a condition of a fact
and, in response, transform an attribute and/or value of the fact
in accordance with a predetermined constraint. Each janitor 110 can
be configured to infer a certain condition of the fact. The fact is
transformed to meet predetermined constraints. Generally, janitors
110 can perform functions such as data cleansing, object merging,
fact merging, fact induction, and the like, as described in more
detail below. For example, data cleansing can remove useless facts
that have a low frequency of use. Object merging can combine
duplicate objects that appear to represent the same entity. Fact
merging can combine duplicate facts that have different formats.
Fact induction can imply new facts from existing facts, such as
implying that a capitalized name appearing before a comma and a
state name is a city name. Some janitors 110 describe desired
characteristics of a fact, such as a format or categorization of
the attribute and/or value. One janitor 110 can normalize attribute
names and values, and delete duplicate and near-duplicate facts so
that an object does not have redundant information. For example, we
might find on one page that Britney Spears' birthday is "Dec. 2,
1981" while on another page that her date of birth is "Dec. 2,
1981." Birthday and Date of Birth can be rewritten as Birthdate by
one janitor 110 and then another janitor 110 can recognize that
Dec. 2, 1981 and Dec. 2, 1981 are different forms of the same date.
Janitor 110 transforms the dates to a preferred form. There are
numerous rules that can be implemented, a particular set of which
depends on a particular implementation. Specific rules are
described in more detail below. Various embodiments of janitors 110
and methods operating therein are described in more detail
below.
[0049] Referring again to system 100, build engine 112 builds and
manages repository 115. Service engine 114 is an interface for
querying repository 115. Service engine 114 processes queries,
scores matching objects, and returns them to the caller. Service
engine 114 is also used by janitors 110.
[0050] Fact repository 115 comprises a storage element such a RAM
or ROM device in combination with software such as a file system or
a database manager. Fact repository 115 stores the facts extracted
from the documents. The facts can be stored as a list, a file
system, or database data. Exemplary data structures for storing
facts in fact repository 215 are described in more detail below
with respect to FIGS. 2(a)-(e).
[0051] Object requesters 152, 154 are entities that request objects
from fact repository 115. Object requesters 152, 154 may be
understood as clients of the system 106, and can be implemented in
any computer device or architecture. As shown in FIG. 1, a first
object requester 152 is located remotely from system 106, while a
second object requester 154 is located in data processing system
106. For example, in a computer system hosting a blog, the blog may
include a reference to an object whose facts are in fact repository
115. An object requester 152, such as a browser displaying the
blog, will access data processing system 106 so that the
information of the facts associated with the object can be
displayed as part of the blog web page. As a second example,
janitors 120 or other entities considered to be part of data
processing system 106 can function as object requester 154,
requesting the facts of objects from fact repository 115.
[0052] Memory 107 includes importers 108, janitors 110, build
engine 112, service engine 114, and requester 154, each of which
are preferably implemented as instructions stored in memory 107 and
executable by processor 126. Memory 107 also includes fact
repository 115. Fact repository 115 can be stored in a memory of
one or more computer systems or in a type of memory such as a disk.
FIG. 1 also includes a computer readable medium 128 containing, for
example, at least one of importers 108, janitors 110, build engine
112, service engine 114, requester 154, and at least some portions
of repository 115. FIG. 1 also includes one or more input/output
devices 120 that allow data to be input and output to and from data
processing system 106. It will be understood that data processing
system 106 preferably also includes standard software components
such as operating systems and the like and further preferably
includes standard hardware components not shown in the figure for
clarity of example.
Data Structures
[0053] FIGS. 2(a)-(e) show example data structures for the facts as
stored. As shown in FIG. 2(a), each fact 204 includes a unique
identifier for that fact, such as a fact ID 210. Each fact 204
includes at least an attribute 212 and a value 214. For example, a
fact associated with an object representing George Washington may
include an attribute of "date of birth" and a value of "Feb. 22,
1732." In one embodiment, all facts are stored as alphanumeric
characters since they are extracted from web pages. In another
embodiment, facts also can store binary data values. Other
embodiments, however, may store fact values as mixed types, or in
encoded formats.
[0054] As described above, each fact is associated with an object
ID 209 that identifies the object that the fact describes. Thus,
each fact that is associated with a same entity (such as George
Washington), has the same object ID 209. In one embodiment, objects
are not stored as separate data entities in memory. In this
embodiment, the facts associated with an object contain the same
object ID, but no physical object exists. In another embodiment,
objects are stored as data entities in memory, and include
references (for example, pointers or IDs) to the facts associated
with the object. The logical data structure of a fact can take
various forms; in general, a fact is represented by a tuple that
includes a fact ID, an attribute, a value, and an object ID. The
storage implementation of a fact can be in any underlying physical
data structure.
[0055] FIG. 2(b) shows an example of facts having respective fact
IDs of 10, 20, and 30 in repository 215. Facts 10 and 20 are
associated with an object identified by object ID "1." Fact 10 has
an attribute of "Name" and a value of "China." Fact 20 has an
attribute of "Category" and a value of "Country." Thus, the object
identified by object ID "1" has a name fact 205 with a value of
"China" and a category fact 206 with a value of "Country." Fact 30
208 has an attribute of "Property" and a value of ""Bill Clinton
was the 42nd President of the United States from 1993 to 2001."
Thus, the object identified by object ID "2" has a property fact
with a fact ID of 30 and a value of "Bill Clinton was the 42nd
President of the United States from 1993 to 2001." In the
illustrated embodiment, each fact has one attribute and one value.
The number of facts associated with an object is not limited; thus
while only two facts are shown for the "China" object, in practice
there may be dozens, even hundreds of facts associated with a given
object. Also, the value fields of a fact need not be limited in
size or content. For example, a fact about the economy of "China"
with an attribute of "Economy" could have a value including several
paragraphs of text, numbers, or perhaps even tables of figures.
This content can be formatted, for example, in a markup language.
For example, a fact having an attribute "original html" might have
a value of the original html text taken from the source web
page.
[0056] Also, while the illustration of FIG. 2(b) shows the explicit
coding of object ID, fact ID, attribute, and value, in practice the
content of the fact can be implicitly coded as well (e.g., the
first field being the object ID, the second field being the fact
ID, the third field being the attribute, and the fourth field being
the value). Other fields include but are not limited to: the
language used to state the fact (English, etc.), how important the
fact is, the source of the fact, a confidence value for the fact,
and so on.
[0057] FIG. 2(c) shows an example object reference table 210 that
is used in some embodiments. Not all embodiments include an object
reference table. The object reference table 210 functions to
efficiently maintain the associations between object IDs and fact
IDs. In the absence of an object reference table 210, it is also
possible to find all facts for a given object ID by querying the
repository to find all facts with a particular object ID. While
FIGS. 2(b) and 2(c) illustrate the object reference table 210 with
explicit coding of object and fact IDs, the table also may contain
just the ID values themselves in column or pair-wise
arrangements.
[0058] FIG. 2(d) shows an example of a data structure for facts
within repository 215, according to some embodiments of the
invention showing an extended format of facts. In this example, the
fields include an object reference link 216 to another object. The
object reference link 216 can be an object ID of another object in
the repository 215, or a reference to the location (e.g., table
row) for the object in the object reference table 210. The object
reference link 416 allows facts to have as values other objects.
For example, for an object "United States," there may be a fact
with the attribute of "president" and the value of "George W.
Bush," with "George W. Bush" being an object having its own facts
in repository 215. In some embodiments, the value field 214 stores
the name of the linked object and the link 216 stores the object
identifier of the linked object. Thus, this "president" fact would
include the value 214 of "George W. Bush", and object reference
link 416 that contains the object ID for the "George W. Bush"
object. In some other embodiments, facts 204 do not include a link
field 216 because the value 214 of a fact 204 may store a link to
another object.
[0059] Each fact 204 also may include one or more metrics 218. A
metric provides an indication of quality of the fact. In some
embodiments, the metrics include a confidence level and an
importance level. The confidence level indicates the likelihood
that the fact is correct. The importance level indicates the
relevance of the fact to the object, compared to other facts for
the same object. The importance level may optionally be viewed as a
measure of how vital a fact is to an understanding of the entity or
concept represented by the object.
[0060] Each fact 204 includes a list of one or more sources 220
that include the fact and from which the fact was extracted. Each
source may be identified by a Uniform Resource Locator (URL), or
Web address, or any other appropriate form of identification and/or
location, such as a unique document identifier.
[0061] The facts illustrated in FIG. 2(d) include an agent field
222 that identifies which importer 108 extracted the fact. For
example, importers 108 may be a specialized importer that extracts
facts from a specific source (e.g., the pages of a particular web
site, or family of web sites) or type of source (e.g., web pages
that present factual information in tabular form), or an importer
208 that extracts facts from free text in documents throughout the
Web, and so forth.
[0062] Some embodiments include one or more specialized facts, such
as a name fact 207 and a property fact 208. A name fact 207 is a
fact that conveys a name for the entity or concept represented by
the object ID. A name fact 207 includes an attribute 224 of "name"
and a value, which is the name of the object. For example, for an
object representing the country Spain, a name fact would have the
value "Spain." A name fact 207, being a special instance of a
general fact 204, includes the same fields as any other fact 204;
it has an attribute, a value, a fact ID, metrics, sources, etc. The
attribute 224 of a name fact 207 indicates that the fact is a name
fact, and the value is the actual name. The name may be a string of
characters. An object ID may have one or more associated name
facts, as many entities or concepts can have more than one name.
For example, an object ID representing Spain may have associated
name facts conveying the country's common name "Spain" and the
official name "Kingdom of Spain." As another example, an object ID
representing the U.S. Patent and Trademark Office may have
associated name facts conveying the agency's acronyms "PTO" and
"USPTO" as well as the official name "United States Patent and
Trademark Office." If an object does have more than one associated
name fact, one of the name facts may be designated as a primary
name and other name facts may be designated as secondary names,
either implicitly or explicitly.
[0063] A property fact 208 is a fact that conveys a statement about
the entity or concept represented by the object ID. Property facts
are generally used for summary information about an object. A
property fact 208, being a special instance of a general fact 404,
also includes the same parameters (such as attribute, value, fact
ID, etc.) as other facts 404. The attribute field 426 of a property
fact 408 indicates that the fact is a property fact (e.g.,
attribute is "property") and the value is a string of text that
conveys the statement of interest. For example, for the object ID
representing Bill Clinton, the value of a property fact may be the
text string "Bill Clinton was the 42nd President of the United
States from 1993 to 2001." Some object IDs may have one or more
associated property facts while other objects may have no
associated property facts. It should be appreciated that the data
structures shown in FIGS. 2(a)-(d) and described above are merely
exemplary. The data structure of the repository 215 may take on
other forms. Other fields may be included in facts and some of the
fields described above may be omitted. Additionally, each object ID
may have additional special facts aside from name facts and
property facts, such as facts conveying a type or category (for
example, person, place, movie, actor, organization, etc.) for
categorizing the entity or concept represented by the object ID. In
some embodiments, an object's name(s) and/or properties may be
represented by special records that have a different format than
the general facts records 204.
[0064] As described previously, a collection of facts is associated
with an object ID of an object. An object may become a null or
empty object when facts are disassociated from the object. A null
object can arise in a number of different ways. One type of null
object is an object that has had all of its facts (including name
facts) removed, leaving no facts associated with its object ID.
Another type of null object is an object that has all of its
associated facts other than name facts removed, leaving only its
name fact(s). Alternatively, the object may be a null object only
if all of its associated name facts are removed. A null object
represents an entity or concept for which the data processing
system 206 has no factual information and, as far as the data
processing system 106 is concerned, does not exist. In some
embodiments, facts of a null object may be left in the repository
215, but have their object ID values cleared (or have their
importance set to a negative value). However, the facts of the null
object are treated as if they were removed from the repository 215.
In some other embodiments, facts of null objects are physically
removed from repository 215.
[0065] FIG. 2(e) is a block diagram illustrating an alternate data
structure 290 for facts and objects in accordance with preferred
embodiments of the invention. In this data structure, an object 290
contains an object ID 292 and references or points to facts 294.
Each fact includes a fact ID 295, an attribute 297, and a value
299. In this embodiment, an object 290 actually exists in memory
207.
[0066] It should be appreciated that the components of document
host and data processing system 406 can be distributed over
multiple computers. For example, repository 215 can have components
deployed over multiple servers. For convenience, however, the
components of data processing system 206 are discussed as though
they were implemented on a single computer.
Exemplary Data Paths
[0067] FIGS. 3(a)-(b) show alternative data paths for fact
processing. As shown in FIG. 3(a), janitors 10 process facts as
they are extracted from documents on document hosts 102 on the
World Wide Web. Data stored on the web and similar hyperlinked
networks has no set format and has no set content. Thus, data from
the web or similar networks is often referred to as unstructured
data. The processed facts are then stored in fact repository 115.
This embodiment may be ideal for janitors 110 that operate on one
fact at a time to perform functions such as normalization. Some
janitors such as those in FIG. 3(a) may also access facts already
stored in the repository to process a newly extracted fact. For
example, a janitor may compare the value of a new fact to values of
facts that have been previously extracted, stored in the repository
(and possibly indexed).
[0068] In FIG. 3(b), janitors 110 access facts from fact repository
115, after the facts are extracted from document hosts 102.
Janitors 110 can thus be configured to process multiple facts
previously stored in the repository 115. This embodiment may be
ideal for janitors 110 that operate on several facts at a time to
perform functions such as merging, although it can also be used to
"clean up," formats, spelling, etc of facts already in the
repository.
[0069] Is will be understood that some systems contain a
combination of the types of janitors shown in FIGS. 3(a) and 3(b),
so that janitors process facts when the facts are initially placed
in the repository and also post-processes facts after the facts
have initially been placed in the repository.
[0070] FIG. 4 is a flow chart illustrating a method 400 of
processing facts during extraction, according to one embodiment of
the present invention. Optionally, an administrator (which can be a
human being or automated software) configures 410 janitors 110
according to an implementation of facts using a script or other
controller. In one embodiment, a specific algorithm, or ordering,
of janitors 110 is configured for a specific implementation. For
example, janitors 110 performing normalization or other types of
clean-up operations can be applied to facts early in an order.
Subsequently, janitors 110 making inductions based on two or more
facts can be applied. If janitors 110 making inductions were run
first, they would not be as efficient, because similar facts may
not be compatible until transformed to a common format. In another
embodiment, a predetermined, ordered set of janitors 110 can be
provided by controller 111.
[0071] Importers 108 extract 420 facts from documents stored on
document hosts 102. Generally, the extraction process analyzes
documents for indicators of facts such as attribute value pairs.
For example, a table is encoded using specific tags in HTML (e.g.,
<td>). Importers 108 can identify the table and determine
whether column headers or row headers are appropriate attributes,
and further, whether corresponding cells are appropriate values.
Importers 108 can also be directed to documents known to contain
facts under a known template. Fact repository 115 stores 420
facts.
[0072] Individual janitors 110 process 430 facts as described below
with respect to FIG. 6. More than one janitor 110 can process a
fact. Additionally, one janitor 110 can process an object,
associated with multiple facts or can compare the facts associated
with multiple objects (e.g., during object merging and/or duplicate
detection). After processing, the fact is stored 440 in fact
repository 115.
[0073] FIG. 5 is a flow chart illustrating a method 500 of
processing facts already stored on fact repository 115. An
administrator configures 510 janitors as described. However, there
may be differences in the number and/or type of janitors 110
applied to facts since facts are processed from the fact repository
115 rather than during extraction as above. Importers 108 extract
520 facts from documents stored on document hosts 102 and store 530
the facts in fact repository 115. Janitors process 540 facts after
extraction. However, after being processed by janitors 110, facts
can be stored under a different organization. For example, similar
facts can be grouped under a single object.
[0074] FIG. 6 is a flow chart illustrating a method 600 of
transforming facts with an inferred condition according to one
embodiment of the present invention. A script or other controller
receives 610 a fact (or more than one fact) containing an attribute
and a value. The script or controller selects 620 a janitor to
process the fact. As described, janitors 110 can be applied
sequentially or according to a specific algorithm. In addition, a
subset of all available janitors 110 can be used for processing
certain facts.
[0075] A janitor 110 infers 630 a condition associated with the
fact from the attribute and/or value. In one embodiment, inferences
can be made from multiple facts associated with an object.
Conditions of attributes and/or values can be birthdates, numerical
values, names, cities, etc. A janitor 110 detecting fact for a Date
of Birth and a fact for a Social Security Number, may infer that
the facts concern a person. Because the fact concerns a person, the
janitor 110 can apply specific constraints associated with persons
such as the format of a person's name, or associate the fact with
other person facts. In some embodiments, the janitor can also add a
new fact explicitly indicating that the associated object
represents a "person." Subsequently, additional janitors 110
configured to operate on persons can examine the fact to make
additional inferences and adjustments. Thus, a janitor 110 may not
perform any operation on the fact if the appropriate condition
cannot be inferred. Facts typically require inferences since they
are not specially formatted for fact repository 115 as is data that
is generated for a particular database.
[0076] The janitor 110 transforms 640 the fact to a predetermined
constraint by adjusting the attribute and/or value. For example,
the name of an attribute or format of a value can be changed as
discussed above. If the fact has needs to be processed by more
janitors in the configured order, the process repeats at the step
selecting 620 a janitor.
[0077] The above paragraphs provide some general discussion and
examples of janitors. The paragraphs that follow provide some
specific examples of janitors. Different embodiments of the present
invention may include some, all, or none of these example janitors.
For the purpose of clarity, only a few types of janitors 110 have
been described below. However, one of ordinary skill in the art
will recognize that other types of janitors 110 are possible in
addition to those described below.
[0078] In some embodiments, some janitors 110 reduce information in
fact repository 115. A singleton-attribute janitor 110 identifies
attributes which should be unique per object, and eliminates all
but one instance of that attribute on any given object. For
example, a person should only have one date of birth. A blacklist
janitor 110 reads in a list of patterns, and deletes any fact that
matches a pattern. For example, blacklist janitor 110 can be used
to remove curse words. A string-cleanup janitor 110 trims unuseful
characters, such as @, #, %, or !, from the beginning or end of
attributes. A name-group-threshold-match janitor 110 merges
duplicate objects if they share a certain number of attributes,
based on their entropy. An entropy is calculated for each value as
described in further detail in U.S. application Ser. No.
11/356,765. Objects having similar facts can be merged if
associated entropy values fall within an entropy threshold. The
name-group-threshold-match janitor 110 is described in further
detail in U.S. application Ser. No. 11/356,765. A
near-duplicate-fact merger janitor 110 identifies duplicate facts
within an object.
[0079] Thus, some janitors compare a first fact to a plurality of
existing facts. The existing facts can be obtained from the
repository or from any other appropriate source. In some janitors,
a fact is compared to existing facts to determine whether the new
fact should be stored in the fact repository. In one janitor, if
the fact duplicates a threshold number of existing facts, the fact
is not stored in the fact repository. In another janitor, if the
fact is corroborated by a threshold number of existing facts, the
fact is stored in the fact repository. In another janitor, if the
fact is not corroborated by a threshold number of existing facts,
the fact is not stored in the fact repository. Because the facts
extracted from the world-wide web are from unstructured data, the
facts can have many formats when they re initially extracted and
some of the facts that are compared by the janitors may not have
the same format. For example, dates can be in MMDDYY format, DDMMYY
format, in formats where months are spelled out ("December"), and
so on. Some janitors know about various formats, such as various
date formats, and take those formats into account when comparing
facts to facts in the repository. In some embodiments, the facts
are normalized before they are stored in the repository. In some
embodiments, a janitor may require that another janitor runs first
in order to normalize formatting of the facts to be compared. Any
of these situations allows a comparing janitor to compare facts
that had different formats when they were extracted.
[0080] One set of janitors 110 is applied to delete certain facts.
A persisted-id-fact-deleter janitor 110 deletes any fact from a
previous repository that should no longer be kept as described in
further detail in U.S. application Ser. No. 11/356,842. A
stuttering-fact-deleter janitor 110 removes any fact whose
attribute and value are the same. A reference-redirect-collapser
janitor 110 collapses value links that point to objects that have
been merged. An invalid-fact-deleter janitor 110 removes any fact
that fail some basic validity checks (e.g., the value is empty). A
suspicious-fact-deleter janitor 110 removes facts with lengthy
attributes (e.g., 3 words) and repeat information that appears
elsewhere in the object. These facts can result from extraction
problems. An invalid-language-deleter janitor 110 removes any fact
in certain languages. This janitor 110 can be used to segregate
facts by language. A legal-constraint janitor 110 enforces
constraints on objects for legal purposes. For example, certain
document can be limited as to how many facts should be extracted.
An unlicensed-fact-finder janitor 110 removes any facts marked as
being `internal only` for legal or other reasons. A
small-object-deleter janitor 110 removes any object with too few
facts. A dangling-reference-deletion janitor 110 removes any fact
with a value link that points at a non-existent object. An object
can be missing when removed by another janitor 110. A
name-references-resolver janitor 110 identifies references to other
objects in facts and creates search links to the other objects.
[0081] One set of janitors 110 can characterize preferred formats
such as canonical forms. A place-cannonicalizer janitor 110
rewrites place names into canonical form. For example, the value
"Trenton, N.J." can be rewritten to "Trenton, N.J." A
date-canonicalizer janitor 110 rewrites dates into a canonical
form. For example, the date "2006-02-16" is rewritten to "16 Feb.
2006." A measurement-cleanup janitor 110 rewrites measurements to a
canonical form. For example, the measurements "5'4''" or "5 ft. 4
in." can be rewritten to "5' 4"." An attribute-cannonicalizer
janitor 110 rewrites attributes. For example, "birthday",
"birthdate", and "birth date" can be rewritten to "date of birth."
An article-value-normalizer janitor 110 rewrites values with
articles to a readable format. For example, the value "Foo, The"
can be rewritten to "The Foo."
[0082] Other janitors 110 can be implemented as well. A
type-identifier janitor 110 assigns type values to objects based on
a subset of janitors 110. For example, every fact with a "date of
birth" attribute is assigned a type value of "person." A born-died
cleanup janitor 110 splits facts associated with birth and death
dates into several facts. For example, the fact "Born: 14 Jul. 1960
in Scranton, Pa." can be split into a fact for date of birth and
another fact for place of birth. A near-duplicate-fact-merger
janitor 110 combines duplicate facts. A value-dereferencer janitor
110 identifies a fact having a value which is a link to another
object, and updates a display value of the fact to be the name of
the object.
[0083] The order in which the steps of the methods of the present
invention are performed is purely illustrative in nature. The steps
can be performed in any order or in parallel, unless otherwise
indicated by the present disclosure. The methods of the present
invention may be performed in hardware, firmware, software, or any
combination thereof operating on a single computer or multiple
computers of any type. Software embodying the present invention may
comprise computer instructions in any form (e.g., source code,
object code, interpreted code, etc.) stored in any
computer-readable storage medium (e.g., a ROM, a RAM, a magnetic
media, a compact disc, a DVD, etc.). Such software may also be in
the form of an electrical data signal embodied in a carrier wave
propagating on a conductive medium or in the form of light pulses
that propagate through an optical fiber.
[0084] While particular embodiments of the present invention have
been shown and described, it will be apparent to those skilled in
the art that changes and modifications may be made without
departing from this invention in its broader aspect and, therefore,
the appended claims are to encompass within their scope all such
changes and modifications, as fall within the true spirit of this
invention.
[0085] In the above description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the invention. It will be apparent,
however, to one skilled in the art that the invention can be
practiced without these specific details. In other instances,
structures and devices are shown in block diagram form in order to
avoid obscuring the invention.
[0086] Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification are not necessarily all referring to the same
embodiment.
[0087] Some portions of the detailed description are presented in
terms of algorithms and symbolic representations of operations on
data bits within a computer memory. These algorithmic descriptions
and representations are the means used by those skilled in the data
processing arts to most effectively convey the substance of their
work to others skilled in the art. An algorithm is here, and
generally, conceived to be a self-consistent sequence of steps
leading to a desired result. The steps are those requiring physical
manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to these
signals as bits, values, elements, symbols, characters, terms,
numbers, or the like.
[0088] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the discussion, it is appreciated that throughout the description,
discussions utilizing terms such as "processing" or "computing" or
"calculating" or "determining" or "displaying" or the like, refer
to the action and processes of a computer system, or similar
electronic computing device, that manipulates and transforms data
represented as physical (electronic) quantities within the computer
system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices.
[0089] The present invention also relates to an apparatus for
performing the operations herein. This apparatus can be specially
constructed for the required purposes, or it can comprise a
general-purpose computer selectively activated or reconfigured by a
computer program stored in the computer. Such a computer program
can be stored in a computer readable storage medium, such as, but
is not limited to, any type of disk including floppy disks, optical
disks, CD-ROMs, and magnetic-optical disks, read-only memories
(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or
optical cards, or any type of media suitable for storing electronic
instructions, and each coupled to a computer system bus.
[0090] The algorithms and modules presented herein are not
inherently related to any particular computer or other apparatus.
Various general-purpose systems can be used with programs in
accordance with the teachings herein, or it may prove convenient to
construct more specialized apparatuses to perform the method steps.
The required structure for a variety of these systems will appear
from the description below. In addition, the present invention is
not described with reference to any particular programming
language. It will be appreciated that a variety of programming
languages can be used to implement the teachings of the invention
as described herein. Furthermore, as will be apparent to one of
ordinary skill in the relevant art, the modules, features,
attributes, methodologies, and other aspects of the invention can
be implemented as software, hardware, firmware or any combination
of the three. Of course, wherever a component of the present
invention is implemented as software, the component can be
implemented as a standalone program, as part of a larger program,
as a plurality of separate programs, as a statically or dynamically
linked library, as a kernel loadable module, as a device driver,
and/or in every and any other way known now or in the future to
those of skill in the art of computer programming. Additionally,
the present invention is in no way limited to implementation in any
specific operating system or environment.
[0091] It will be understood by those skilled in the relevant art
that the above-described implementations are merely exemplary, and
many changes can be made without departing from the true spirit and
scope of the present invention. Therefore, it is intended by the
appended claims to cover all such changes and modifications that
come within the true spirit and scope of this invention.
* * * * *