CA2610208A1 - Learning facts from semi-structured text - Google Patents
Learning facts from semi-structured text Download PDFInfo
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- CA2610208A1 CA2610208A1 CA002610208A CA2610208A CA2610208A1 CA 2610208 A1 CA2610208 A1 CA 2610208A1 CA 002610208 A CA002610208 A CA 002610208A CA 2610208 A CA2610208 A CA 2610208A CA 2610208 A1 CA2610208 A1 CA 2610208A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/93—Document management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/353—Clustering; Classification into predefined classes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/80—Information retrieval; Database structures therefor; File system structures therefor of semi-structured data, e.g. markup language structured data such as SGML, XML or HTML
- G06F16/84—Mapping; Conversion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/953—Organization of data
- Y10S707/962—Entity-attribute-value
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Machine Translation (AREA)
Abstract
A method and system of learning, or bootstrapping, facts from semi-structured text is described. Starting with a set of seed facts associated with an object, documents associated with the object are identified. The identified documents are checked to determine if each has at least a first predefined number of seed facts. If a document does have at least a first predefined number of seed facts, a contextual pattern associated with the seed facts is identified and other instances of content in the document matching the contextual pattern are identified. If the document includes at least a second predefined number of the other instances of content matching the contextual pattern, then facts may be extracted from the other instances.
Claims (19)
1. A computer-implemented method of learning facts, comprising:
accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
identifying in the document a contextual pattern associated with the respective seed facts in the document;
confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts;
and when the confirming is successful, extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
identifying in the document a contextual pattern associated with the respective seed facts in the document;
confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts;
and when the confirming is successful, extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
2. The method of claim 1, further comprising repeating the extracting and merging operations for one or more instances of content matching the contextual pattern in the document.
3. The method of claim 1, wherein the extracted and merged fact comprises an attribute-value pair that is distinct from all other attribute-value pairs of the object.
4. The method of claim 1, further comprising:
identifying an attribute-value pair in the document that matches a respective attribute-value pair of the object; and adding an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
identifying an attribute-value pair in the document that matches a respective attribute-value pair of the object; and adding an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
5. The method of claim 4, further comprising generating a confidence value for each attribute-value pair of the object that is based on the documents in the list of documents associated with the attribute-value pair.
6. The method of claim 4, further comprising generating a confidence value for each attribute-value pair of the object that corresponds to a number of documents in the list of documents associated with the attribute-value pair.
7. A system for learning facts, comprising:
one or more modules having instructions:
to access an object within a fact repository, wherein the object includes a name and one or more seed facts;
to identify a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
to identify in the document a contextual pattern associated with the respective seed facts in the document; and to confirm that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts; and to extract an extracted fact from a respective instance of content matching the contextual pattern and merge the extracted fact into the object.
one or more modules having instructions:
to access an object within a fact repository, wherein the object includes a name and one or more seed facts;
to identify a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
to identify in the document a contextual pattern associated with the respective seed facts in the document; and to confirm that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts; and to extract an extracted fact from a respective instance of content matching the contextual pattern and merge the extracted fact into the object.
8. The system of claim 7, wherein the one or more modules include instructions to repeatedly extract and merge attribute-value pairs from instances of content matching the contextual pattern in the document.
9. The system of claim 7, wherein the extracted and merged fact comprises an attribute-value pair that is distinct from all other attribute-value pairs of the object.
10. The system of claim 7, wherein the one or more modules include instructions to:
identify an attribute-value pair in the document that matches a respective attribute-value pair of the object; and add an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
identify an attribute-value pair in the document that matches a respective attribute-value pair of the object; and add an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
11. The system of claim 10, further comprising, instructions to generate a confidence value for each attribute-value pair of the object that is based on the documents in the list of documents associated with the attribute-value pair.
12. The system of claim 10, further comprising instructions to generate a confidence value for each attribute-value pair of the object that corresponds to a number of documents in the list of documents associated with the attribute-value pair.
13. A computer readable storage medium storing one or more programs for execution by a computer system, the one or more programs comprising instructions for:
accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
identifying in the document a contextual pattern associated with the respective seed facts in the document;
confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts;
and when the confirming is successful, extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
for each of the documents in the identified set:
identifying in the document a contextual pattern associated with the respective seed facts in the document;
confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts;
and when the confirming is successful, extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
14. The computer readable storage medium of claim 13, further comprising repeating the extracting and merging operations for one or mor. instances of content matching the contextual pattern in the document.
15. The computer readable storage medium of claim 13, wherein the extracted and merged fact comprises an attribute-value pair that is distinct from all other attribute-value pairs of the object.
16. The computer readable storage medium of claim 13, further comprising instructions for:
identifying an attribute-value pair in the document that matches a respective attribute-value pair of the object; and adding an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
identifying an attribute-value pair in the document that matches a respective attribute-value pair of the object; and adding an identifier of the document to a list of documents associated with the respective attribute-value pair of the object.
17 17. The computer readable storage medium of claim 16, further comprising instructions for generating a confidence value for each attribute-value pair of the object that is based on the documents in the list of documents associated with the attribute-value pair.
18. The computer readable storage medium of claim 16, further comprising instructions for generating a confidence value for each attribute-value pair of the object that corresponds to a number of documents in the list of documents associated with the attribute-value pair.
19. A system for learning facts, comprising:
means for accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
means for identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
means, for each of the documents in the identified set:
for identifying in the document a contextual pattern associated with the respective seed fact in the document;
for confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts; and when the confirming is successful, for extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
means for accessing an object within a fact repository, wherein the object includes a name and one or more seed facts;
means for identifying a set of documents having content and associated with the object name, each document in the set having at least a first predefined number of distinct seed facts in common with the object;
means, for each of the documents in the identified set:
for identifying in the document a contextual pattern associated with the respective seed fact in the document;
for confirming that the document includes at least a second predefined number of instances of content matching the contextual pattern in addition to the respective seed facts; and when the confirming is successful, for extracting an extracted fact from a respective instance of content matching the contextual pattern and merging the extracted fact into the object.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/142,853 US7769579B2 (en) | 2005-05-31 | 2005-05-31 | Learning facts from semi-structured text |
US11/142,853 | 2005-05-31 | ||
PCT/US2006/019807 WO2006132793A2 (en) | 2005-05-31 | 2006-05-18 | Learning facts from semi-structured text |
Publications (2)
Publication Number | Publication Date |
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CA2610208A1 true CA2610208A1 (en) | 2006-12-14 |
CA2610208C CA2610208C (en) | 2012-07-10 |
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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CA2610208A Active CA2610208C (en) | 2005-05-31 | 2006-05-18 | Learning facts from semi-structured text |
Country Status (5)
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US (4) | US7769579B2 (en) |
EP (1) | EP1891557A2 (en) |
CN (1) | CN101253498B (en) |
CA (1) | CA2610208C (en) |
WO (1) | WO2006132793A2 (en) |
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CA2610208C (en) | 2012-07-10 |
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CN101253498B (en) | 2010-12-08 |
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