WO2011017155A4 - Displaying relationships between concepts to provide classification suggestions via inclusion - Google Patents

Displaying relationships between concepts to provide classification suggestions via inclusion Download PDF

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Publication number
WO2011017155A4
WO2011017155A4 PCT/US2010/043516 US2010043516W WO2011017155A4 WO 2011017155 A4 WO2011017155 A4 WO 2011017155A4 US 2010043516 W US2010043516 W US 2010043516W WO 2011017155 A4 WO2011017155 A4 WO 2011017155A4
Authority
WO
WIPO (PCT)
Prior art keywords
concepts
uncoded
classification code
concept
cluster
Prior art date
Application number
PCT/US2010/043516
Other languages
French (fr)
Other versions
WO2011017155A1 (en
Inventor
William C. Knight
Nicholas I. Nussbaum
John W. Conwell
Original Assignee
Fti Technology Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fti Technology Llc filed Critical Fti Technology Llc
Priority to CA2773263A priority Critical patent/CA2773263C/en
Priority to EP10747718A priority patent/EP2460103A1/en
Publication of WO2011017155A1 publication Critical patent/WO2011017155A1/en
Publication of WO2011017155A4 publication Critical patent/WO2011017155A4/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • G06F16/287—Visualization; Browsing
    • 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
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3322—Query formulation using system suggestions
    • 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
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • 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
    • 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
    • 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
    • G06F16/355—Class or cluster creation or modification
    • 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
    • G06F16/358—Browsing; Visualisation therefor
    • 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
    • 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
    • G06F16/954—Navigation, e.g. using categorised browsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/046—Forward inferencing; Production systems
    • G06N5/047—Pattern matching networks; Rete networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Abstract

A system (10) and method (50) for displaying relationships between concepts (14c, 14d) to provide classification suggestions via inclusion is provided. A set of reference concepts (14d) each associated with a classification code is designated. One or more of the reference concepts (14d) are combined with a set of uncoded concepts (14c). Clusters of the uncoded concepts (14c) and the one or more reference concepts (14d) are generated. Relationships between the uncoded concepts (14c) and the one or more reference concepts (14d) in at least one cluster (173) are visually depicted as suggestions for classifying the uncoded concepts (14c) in that cluster (173).

Claims

AMENDED CLAIMS
received by the International Bureau on 01 February 2011 (01.02.2011) 1. A method (50) for displaying relationships between concepts (14c, 14d) to provide classification suggestions via inclusion, comprising the steps of:
designating a set of reference concepts (14d) each associated with a classification code, wherein each concept (14d) comprises nouns and noun phrases with common semantic meaning that are extracted from a set of documents (14a);
combining a subset of the reference concepts (14d) with a set of uncoded concepts (14c);
generating clusters (173) of the uncoded concepts (14c) and the reference concepts (14d) subset; and
visually depicting relationships between the uncoded concepts (14c) and one or more reference concepts (14d) in at least one cluster (173) as suggestions for classifying the uncoded concepts (14c) in that cluster (173), wherein the steps are performed by a suitably programmed computer.
2. A method (50) according to Claim 1, further comprising: determining a classification code for at least one of the uncoded concepts (14c) in the at least one cluster (173), comprising:
comparing the at least one uncoded concept (14c) to a neighborhood of reference concepts (14d) in the at least one cluster (173), wherein the neighborhood comprises a predetermined number of the reference concepts (14d) that have a closest distance to the uncoded concept (14c); and selecting the classification code based on the comparison of the at least one uncoded concept (14c) and reference concept neighborhood.
3. A method (50) according to Claim 1, further comprising:
automatically classifying the at least one uncoded concept (14c) by assigning the classification code to that uncoded concept (14c).
4. A method (50) according to Claim 3, further comprising:
identifying the documents (14a) associated with the classified uncoded concept (14c); and classifying the associated documents (14a) by assigning a classification code to each of the documents (14a).
5. A method (50) according to Claim 4, wherein the documents (14a) are identified using a matrix comprising a mapping of concepts and related documents (14a).
6. A method (50) according to Claim 2, further comprising:
providing the classification code as a suggestion for classifying the at least one uncoded concept (14c) in the at least one cluster (173).
7. A method (50) according to Claim 6, further comprising:
providing a confidence level for the classification code suggestion.
8. A method (50) according to Claim 7, wherein the classification code is provided when the confidence level exceeds a predetermined threshold.
9. A method (50) according to Claim 1, further comprising:
generating the set of reference concepts (14d), comprising at least one of:
identifying dissimilar uncoded concepts (14c) for a document review project and assigning a classification code to each of the dissimilar uncoded concepts (14c) for inclusion in the reference concepts (14d) set; and clustering uncoded concepts (14c) for a document review project, selecting one or more of the uncoded concepts (14c) in one or more of the clusters (173), and assigning a classification code to each of the selected uncoded concepts (14c) for inclusion in the reference concepts (14d) set.
10. A method (50) according to Claim 1 , wherein the subset of reference concepts (14d) is determined via at least one of predefined, arbitrary, or customized selection.
11. A system (10) for displaying relationships between concepts to provide classification suggestions via inclusion, comprising: a set of reference concepts (14d) each associated with a classification code, wherein each concept comprises nouns and noun phrases with common semantic meaning that are extracted from a set of documents (14a);
a clustering module (33) to combine a subset of the reference concepts (14d) with a set of uncoded concepts (14c) and to generate clusters (173) of the uncoded concepts (14c) and the reference concepts (14d) subset;
a display (36) to visually depict relationships between the uncoded concepts (14c) and one or more reference concepts (14d) in at least one cluster (173) as suggestions for classifying the uncoded concepts (14c) in that cluster (173); and
a processor to execute the clustering module.
12. A system (10) according to Claim 11 , further comprising: a classification module (34) to determine a classification code for at least one of the uncoded concepts (14c) in the at least one cluster (173), comprising:
a comparison module to compare the at least one uncoded concept (14c) to a neighborhood of reference concepts (14d) in the at least one cluster (173), wherein the neighborhood comprises a predetermined number of the reference concepts (14d) that have a closest distance to the uncoded concept (14c); and
a selection module to select the classification code based on the comparison of the at least one uncoded concept (14c) and reference concept neighborhood.
13. A system (10) according to Claim 11, wherein the classification module (34) automatically classifies the at least one uncoded concept (14c) by assigning the classification code to that uncoded concept (14c).
14. A system (10) according to Claim 13, wherein the classification module (34) identifies the documents (14a) associated with the classified uncoded concept (14c) and classifies the associated documents (14a) by assigning a classification code to each of the documents (14a).
25
15. A system (10) according to Claim 14, wherein the documents (14a) are identified using a matrix comprising a mapping of concepts and related documents (14a).
16. A system (10) according to Claim 12, wherein the classification module (34) provides the classification code as a suggestion for classifying the at least one uncoded concept (14c) in the at least one cluster (173).
17. A system (10) according to Claim 16, wherein the classification module (34) provides a confidence level with the classification code suggestion.
18. A system (10) according to Claim 17, wherein the classification code is provided when the confidence level exceeds a predetermined threshold.
19. A system (10) according to Claim 11, further comprising:
a reference module to generate the set of reference concepts (14d), comprising at least one of:
a reference similarity module to identify dissimilar uncoded concepts (14c) for a document review project and to assign a classification code to each of the dissimilar uncoded concepts (14c) for inclusion in the reference concepts (14d) set; and
a reference clustering module to cluster uncoded concepts (14c) for a document review project, to select one or more of the concepts in at least one cluster (173), and to assign a classification code to each of the selected concepts for inclusion in the reference concepts (14d) set.
20. A system (10) according to Claim 11, wherein the subset of reference concepts (14d) is determined via at least one of predefined, arbitrary, or customized selection.
26
PCT/US2010/043516 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via inclusion WO2011017155A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CA2773263A CA2773263C (en) 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via inclusion
EP10747718A EP2460103A1 (en) 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via inclusion

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US22921609P 2009-07-28 2009-07-28
US61/229,216 2009-07-28
US23649009P 2009-08-24 2009-08-24
US61/236,490 2009-08-24
US12/844,810 US8700627B2 (en) 2009-07-28 2010-07-27 System and method for displaying relationships between concepts to provide classification suggestions via inclusion
US12/844,810 2010-07-27

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WO2011017155A1 WO2011017155A1 (en) 2011-02-10
WO2011017155A4 true WO2011017155A4 (en) 2011-03-31

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PCT/US2010/043383 WO2011017098A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via nearest neighbor
PCT/US2010/043331 WO2011017080A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via injection
PCT/US2010/043290 WO2011017064A2 (en) 2009-07-28 2010-07-27 Providing a classification suggestion for electronically stored information
PCT/US2010/043452 WO2011017133A2 (en) 2009-07-28 2010-07-27 Providing a classification suggestion for concepts
PCT/US2010/043292 WO2011017065A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via inclusion
PCT/US2010/043453 WO2011017134A1 (en) 2009-07-28 2010-07-27 Displaying relationships between concepts to provide classification suggestions via injection
PCT/US2010/043506 WO2011017152A2 (en) 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via nearest neighbor
PCT/US2010/043516 WO2011017155A1 (en) 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via inclusion

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Application Number Title Priority Date Filing Date
PCT/US2010/043383 WO2011017098A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via nearest neighbor
PCT/US2010/043331 WO2011017080A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via injection
PCT/US2010/043290 WO2011017064A2 (en) 2009-07-28 2010-07-27 Providing a classification suggestion for electronically stored information
PCT/US2010/043452 WO2011017133A2 (en) 2009-07-28 2010-07-27 Providing a classification suggestion for concepts
PCT/US2010/043292 WO2011017065A1 (en) 2009-07-28 2010-07-27 Displaying relationships between electronically stored information to provide classification suggestions via inclusion
PCT/US2010/043453 WO2011017134A1 (en) 2009-07-28 2010-07-27 Displaying relationships between concepts to provide classification suggestions via injection
PCT/US2010/043506 WO2011017152A2 (en) 2009-07-28 2010-07-28 Displaying relationships between concepts to provide classification suggestions via nearest neighbor

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US (22) US8713018B2 (en)
EP (8) EP2460097A1 (en)
CA (8) CA2773317C (en)
WO (8) WO2011017098A1 (en)

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