CA2556362A1 - Arranging concept clusters in thematic neighborhood relationships in a two-dimensional display - Google Patents

Arranging concept clusters in thematic neighborhood relationships in a two-dimensional display Download PDF

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CA2556362A1
CA2556362A1 CA002556362A CA2556362A CA2556362A1 CA 2556362 A1 CA2556362 A1 CA 2556362A1 CA 002556362 A CA002556362 A CA 002556362A CA 2556362 A CA2556362 A CA 2556362A CA 2556362 A1 CA2556362 A1 CA 2556362A1
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spine
best fit
candidate
concept
clusters
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CA2556362C (en
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Lynne Marie Evans
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Attenex Corp
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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
    • G06F16/355—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
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G06F16/24578—Query processing with adaptation to user needs using ranking
    • 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
    • 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
    • G06F16/3331—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
    • G06F16/358—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
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/20—Drawing from basic elements, e.g. lines or circles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/20—Drawing from basic elements, e.g. lines or circles
    • G06T11/206—Drawing of charts or graphs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/60—Editing figures and text; Combining figures or text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/416—Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K999/00—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS dummy group
    • H05K999/99—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS dummy group dummy group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99933—Query processing, i.e. searching
    • Y10S707/99935—Query augmenting and refining, e.g. inexact access

Abstract

A set of clusters (50) is selected from a concept space. The concept space includes clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47). A theme in each of a plurality of the clusters (50) is identified. Each theme includes at least one such concept (53) ranked within the cluster (50). Unique candidate spines (55) is logically formed. Each candidate spine (55) includes clusters (50) commonly sharing at least one such concept (54). The clusters (50) are assigned to one such candidate spine (55) having a substantially best fit. Each such sufficiently unique best fit candidate spine (56) is identified and placed in a visual display space (43). Each non-identified best fit candidate spine (56) is placed in the visual display space (43) relative to an anchor cluster (60) on one such identified best fit candidate spine (56).

Claims (38)

1. A system (34) for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:
a set of clusters (50) selected from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
a theme generator (41) to identify a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50); and a spine placer (42), comprising:
a candidate spine selector to logically form a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54);
a candidate spine assigner to assign one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
a best fit candidate spine placer to identify each such best fit candidate spine (56) sufficiently unique from each other such best fit candidate spine (56) and to place the identified best fit candidate spine (56) in a visual display space (43); and a remaining candidate spine placer to place each non-identified best fit candidate spine (56) in the visual display space (43) relative to an anchor cluster (60) on one such identified best fit candidate spine (56).
2. A system (34) according to Claim 1, further comprising:
a concept scorer to determine a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and a concept ranker to rank the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
3. A system (34) according to Claim 1, further comprising:
a concept evaluator to evaluate each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
4. A system (34) according to Claim 3, wherein the acceptance criteria comprises at least one of being contained in a seed theme of a cluster (50) and being contained in a predetermined minimum of the documents (49).
5. A system (34) according to Claim 1, further comprising:
a candidate spine evaluator to evaluate such candidate spine (55) against an acceptance criteria.
6. A system (34) according to Claim 5, wherein the acceptance criteria comprises the at least one such concept (54) being contained in at least one of a plurality of the plurality of clusters (50) and within a predetermined maximum of the plurality of clusters (50).
7. A system (34) according to Claim 1, further comprising:
a spine fit evaluator to determine a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and a spine fit selector to select the spine fit comprising a maximum spine fit as the substantially best fit.
8. A system (34) according to Claim 7, wherein the spine fit is calculated in accordance to an equation:
where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
9. A system (34) according to Claim 1, wherein each such best fit candidate spine (56) containing only one such cluster (50) is discarded.
10. A system (34) according to Claim 1, further comprising:
a spine concept score vector (57) generated for each such best fit candidate spine (56); and~

a similarity evaluator to evaluate a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
11. A system (34) according to Claim 10, further comprising:
a concept score aggregator to aggregate a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and a concept score normalizer to normalize each aggregated concept score (51).
12. A system (34) according to Claim 10, wherein the similarity is calculated as a cosine over the spine concept score vectors (57).
13. A system (34) according to Claim 1, further comprising:
a similarity identifier to determine a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56), and to identify the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
14. A system (34) according to Claim 13, wherein the similarity is calculated as a cosine over the anchor cluster (60) candidate and one such cluster (50) in the spine (56).
15. A system (34) according to Claim 13, wherein the non-identified best fit candidate spine (56) is placed in the visual display space (43) along a vector originating from the anchor cluster (60).
16. A system (34) according to Claim 15, wherein the placement of the non-identified best fit candidate spine (56) is adjusted if overlapping with at least one other cluster (50) already placed.
17. A system (34) according to Claim 15, wherein the non-identified best fit candidate spine (56) is labeled as containing at least one anchor cluster candidate (50) following placement.
18 18. A system (34) according to Claim 15, wherein the non-identified best fit candidate spine (56) is placed along a vector originating from the anchor cluster (60) with an angle comprising at least one of , where 0<=.sigma.<II.
19. A method (100) for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:
selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);
identifying (110) a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);
logically forming (120) a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;
identifying (140) each such best fit candidate spine (56) sufficiently unique from each other such best fit candidate spine (56) and placing the identified best fit candidate spine (56) in a visual display space (43); and placing (160) each non-identified best fit candidate spine (56) in the visual display space (43) relative to an anchor cluster (60) on one such identified best fit candidate spine (56).
20. A method (100) according to Claim 19, further comprising:
determining a cumulative score (51) for one or more of the concepts (53) for each of the plurality of clusters (50); and ranking the concepts (53) by the cumulative score (51) in at least one of descending and ascending order.
21. A method (100) according to Claim 19, further comprising:

evaluating each of the plurality of concepts (53) against an acceptance criteria to qualify as the theme of the cluster (50).
22. A method (100) according to Claim 21, wherein the acceptance criteria comprises at least one of being contained in a seed theme of a cluster (50),and being contained in a predetermined minimum of the documents (49).
23. A method (100) according to Claim 19, further comprising:

evaluating such candidate spine (55) against an acceptance criteria.
24. A method (100) according to Claim 23, wherein the acceptance criteria comprises the at least one such concept (54) being contained in at least one of a plurality of the plurality of clusters (50) and within a predetermined maximum of the plurality of clusters (50).
25. A method (100) according to Claim 19, further comprising:

determining a spine fit between the concept (54) in each such cluster (50) and the at least one theme commonly shared by the clusters (50) in each of the candidate spines (55); and selecting the spine fit comprising a maximum spine fit as the substantially best fit.
26. A method (100) according to Claim 25, wherein the spine fit is calculated in accordance to an equation:

where popularity is defined as a number of clusters (50) containing each such concept (54) in the candidate spine (55), rank is defined as a rank of the candidate spine concept (54), and scale is defined as a bias factor.
27. A method (100) according to Claim 19, further comprising:

discarding each such best fit candidate spine (56) containing only one such cluster (50).
28. A method (100) according to Claim 19, further comprising:

generating a spine concept score vector (57) for each such best fit candidate spine (56); and evaluating a similarity between the best fit candidate spine (56) and each other such other such best fit candidate spine (56).
29. A method (100) according to Claim 28, further comprising:

aggregating a concept score (51) for each such concept (54) contained in each cluster (50) in the best fit candidate spine (56); and normalizing each aggregated concept score (51).
30. A method (100) according to Claim 28, further comprising:

calculating the similarity as a cosine over the spine concept score vectors (57).
31. A method (100) according to Claim 19, further comprising:

determining a similarity between at least one anchor cluster candidate (50) and at least one such cluster (50) in a non-identified best fit candidate spine (56); and identifying the at least one such anchor cluster (60) candidate with acceptable similarity as the anchor cluster (60).
32. A method (100) according to Claim 31, further comprising:

calculating the similarity as a cosine over the anchor cluster (60) candidate and one such cluster (50) in the spine (56).
33. A method (100) according to Claim 31, further comprising:

placing the non-identified best fit candidate spine (56) in the visual display space (43) along a vector originating from the anchor cluster (60).
34. A method (100) according to Claim 33, further comprising:

adjusting placement of the non-identified best fit candidate spine (56) if overlapping with at least one other cluster (50) already placed.
35. A method (100) according to Claim 33, further comprising:

labeling the non-identified best fit candidate spine (56) as containing at least one anchor cluster candidate (50) following placement.
36. A method (100) according to Claim 33, further comprising:

placing the non-identified best fit candidate spine (56) along a vector originating from the anchor cluster (60) with an angle comprising at least one of; .sigma.~ ,where 0 <=.sigma. < II.
37. A computer-readable storage medium holding code for performing the method (100) according to Claim 19.
38. An apparatus for arranging concept clusters (53) in thematic neighborhood relationships in a two-dimensional visual display space (43), comprising:

means for selecting a set of clusters (50) from a concept space comprising a multiplicity of clusters (50) with concepts (53) visualizing document content (49) based on extracted concepts (47);

means for identifying a theme in each of a plurality of the clusters (50), each theme comprising at least one such concept (53) ranked within the cluster (50);

means for logically forming a plurality of unique candidate spines (55) comprising clusters (50) commonly sharing at least one such concept (54) and means for assigning one or more of the clusters (50) to one such candidate spine (55) having a substantially best fit;

means for identifying each such best fit candidate spine (56) sufficiently unique from each other such best fit candidate spine (56) and means for placing the identified best fit candidate spine (56) in a visual display space (43); and means for placing each non-identified best fit candidate spine (56) in the visual display space (43) relative to an anchor cluster (60) on one such identified best fit candidate spine (56).
CA2556362A 2004-02-13 2005-02-11 Arranging concept clusters in thematic neighborhood relationships in a two-dimensional display Expired - Fee Related CA2556362C (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US10/778,416 US7191175B2 (en) 2004-02-13 2004-02-13 System and method for arranging concept clusters in thematic neighborhood relationships in a two-dimensional visual display space
US10/778,416 2004-02-13
PCT/US2005/004241 WO2005081139A1 (en) 2004-02-13 2005-02-11 Arranging concept clusters in thematic neighborhood relationships in a two-dimensional display

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CA2556362C CA2556362C (en) 2012-10-02

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EP (1) EP1721267A1 (en)
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