Displaying implicit associations among items in loosely-structured data sets

• US 6,629,097 B1
• Filed: 04/14/2000
• Issued: 09/30/2003
• Est. Priority Date: 04/28/1999
• Status: Expired due to Fees

• Current Assignee: Douglas K. Keith
• Original Assignee: Douglas K. Keith
• Inventors: Keith, Douglas K.
• Primary Examiner(s): Metjahic, Safet
• Assistant Examiner(s): Nguyen, Merilyn P.
• Application Number:
  US09/550,409
• Time in Patent Office: 1,264 Days
• Field of Search: 707/104, 707/103, 707/513, 707/2, 707/3, 707/5, 707/10, 707/1, 707/6, 707/7, 707/9, 345/356
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A system for discerning and displaying relational structure and conceptual similarities among items in a target group of data items. Root terms are extracted from descriptions of the data items, and are used to generate similarity measures among all data items in the group. The invention uses a combination of mathematical operations to transform the similarity measures into Euclidean coordinates such that all data items and all root terms can be simultaneously plotted as visual objects in a computer-simulated three-dimensional space. Interpoint distances between data objects and root term objects correspond to the measures of associative similarity between those points. Three-dimensional graphics and movement simulation allow the data display to be presented and viewed from an unlimited number of perspectives. Users can access detailed information about displayed data items, including hyperlinks and URL links which serve to connect the user immediately to the original data sources represented by is objects in the visual plot. The invention can be applied across a broad range of circumstances in which surfacing of the implicit conceptual and relational structure among a group of data items is desired.

Claims

1. A method for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

normalizing the contingency matrix to produce a normalized matrix;

subjecting the normalized matrix to singular value decomposition to produce singular values;

transforming the singular values to produce the coordinates for concepts and entities in a simulated three-dimensional space;

adjusting the range of said coordinates so that said space fits in a display frame; and

displaying concepts and entities together as objects in said space.

2. A method for extracting inherent or implicit conceptual relationships or semantic associations that exist among items in a data set, and for representing said associations in a simulated three-dimensional space, said method comprising the steps of:

organizing said items into a plurality of entity data pairs, each entity data pair comprising a label of an entity and a textual or symbolic entity description;

creating a set of modified entity descriptions by pruning irrelevant terms from each textual or symbolic entity description and reducing each remaining term to a linguistic root form;

extracting concepts from said set of modified entity descriptions, each concept comprising a root term associated with at least two modified entity descriptions;

producing a similarity matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing a binary indication of whether the corresponding concept is present in the corresponding modified entity description;

quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space; and

displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the multi-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

3. The method of claim 2 further comprising the step of:

supporting a user'"'"'s selection of a method modification selected from the group consisting of;

selecting a term or type of term that may not be extracted as a concept;

selecting a minimum number or percentage of modified entity descriptions within which a term must occur to be extracted as a concept,

selecting a minimum number or percentage of modified entity descriptions within which a term must occur to be displayed as a concept,

selecting threshold criteria for the selection and/or display of concepts, including a frequency of use of a given root term in a given language, and alternate representations of root terms provided by a thesaurus or other word corpus,

using a statistical or mathematical procedure or algorithm which can generate chi-square or Euclidean distances between all items in two sets of variables such that all items in both sets can be simultaneously displayed in a dual-scaled plot,

specifying a density of the concept and entity display by applying an alternative statistical technique, selected from the group consisting of cluster analysis or principal components analysis to a result map for the purpose of collapsing data items in the map to produce a less dense and more abstract view of the entire field of data, and

selecting concepts and/or entities from the concept and entity display to be retained in subsequent samplings of a target data set.

4. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of claim 2.

5. A method for operating a server computer, said server computer having a computer-readable medium having stored thereon sequences of instructions which may be executed by a processor, said method comprising the step of:

serving to a client computer having said processor the sequences of instructions that cause said processor to perform the steps of claim 2.

6. A business method comprising the steps of:

displaying to a user in a visual display implicit associations among a plurality of items in a loosely-structured data set in accordance with the method of claim 2; and

generating business income by charging for a use of said visual display using a technique selected from the group consisting of;

obtaining the identity of said user,

posting an advertisement of an advertiser within or in proximity to said visual display and recording within a computer memory a charge to said advertiser for said use, and

charging said user a subscription or license fee in exchange for granting access to said method at a certain frequency or over a period of time.

7. An apparatus for extracting inherent or implicit conceptual relationships or semantic associations that exist among items in a data set, and for representing said associations in a simulated three-dimensional space, said apparatus comprising:

means for organizing said items into a plurality of entity data pairs, each entity data pair comprising a label of an entity and a textual or symbolic entity description;

means for creating a set of modified entity descriptions by pruning irrelevant terms from each textual or symbolic entity description and reducing each remaining term to a linguistic root form;

means for extracting concepts from said set of modified entity descriptions, each concept comprising a root term that is associated with at least two modified entity descriptions;

means for producing a similarity matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing a binary indication of whether the corresponding concept is present in the corresponding modified entity description;

means for quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising correspondence analysis, in combination with a matrix transformation operation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space; and

means for displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the multi-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

8. A system for identifying and displaying inherent semantic constructs in entities in a data set with each entity comprising a web site, wherein the contents of the web pages that comprise said web site providing a description for said web site, with said constructs being held in common by more than one entity, in a manner such that the strength of semantic association between all entities and identified constructs are represented as visually-observable distances between data points in an n-dimensional Euclidean space, said system comprising:

a network of computers, said network comprising interconnected server computers and client computers, some of said server computers storing and serving web pages having contents;

means for producing a data set residing in said network, said data set comprising a plurality of data pairs, each data pair comprising a label for a web page and a textual or symbolic description of the web page;

means for creating a set of modified web site descriptions by pruning irrelevant terms from web page descriptions and reducing each remaining term to a linguistic root form;

means for extracting concepts from said modified web site descriptions residing in said network, each concept comprising a root term that is associated with more than one web site description;

means for producing a similarity matrix residing in said network, wherein each web page is represented as a column and each concept is represented as a row or vise versa, with the element at the intersection of each such column and row containing a binary indication of whether the concept is found in the corresponding web page modified description;

means for quantifying the associative structure of the data set by subjecting the similarity matrix to means for dual-scaling, in combination with auxiliary matrix transformation operations, thereby producing the coordinates of each concept and each web page in a multi-dimensional Euclidean space; and

means for displaying the concepts as one type of virtual object and the web sites as a second type of virtual object with each object located at the appropriate coordinates in the multi-dimensional space, said means for displaying residing in a client computer, wherein the relative distances among the concept objects reflect the degree to which the concepts are associated with one another, wherein the relative distances among the entity objects reflect the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each web site object reflects the degree to which the content of each web page is associated with each concept.

9. The system of claim 8 wherein said data set is a formal or informal, structured or unstructured data set from which entity identifiers and associated descriptions can be extracted and said data set is selected from the group consisting of:

a result of a search of the World Wide Web produced by a search engine,

a result of a query of a database, and

a result of a search of a Help system.

10. The system of claim 8 wherein said means for producing is a search engine.

11. The system of claim 8 further comprising means for viewing a web page associated with a concept.

12. A computer-implemented method for displaying implicit associations among a plurality of items in a data set comprising:

processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

quantifying conceptually-based associative relationships among said plurality of items and said plurality of concepts; and

representing said relationships within a simulated three-dimensional visual space;

wherein said quantifying step comprises

producing a similarity matrix wherein each item is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of the degree to which the corresponding concept is present in the corresponding modified entity description, and

quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space; and

wherein said representing step comprises displaying the concepts as one type of virtual object and the items as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of item objects reflects the degree to which the items are associated with one another and wherein the relative distance between each concept object and each item object reflects the degree to which each item is associated with each concept.

13. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of claim 12.

14. A computer-implemented system for displaying implicit associations among a plurality of items in a data set comprising:

means for processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

means for extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

means for quantifying conceptually-based associative relationships among said plurality of items and said plurality of concepts; and

means for representing said relationships within a simulated three-dimensional visual space;

wherein said means for quantifying comprises

means for producing a similarity matrix wherein each item is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description, and

means for quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space; and

wherein said means for representing comprises means for displaying the concepts as one type of virtual object and the items as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of item objects reflects the degree to which the items are associated with one another and wherein the relative distance between each concept object and each item object reflects the degree to which each item is associated with each concept.

15. A computer-implemented method for displaying implicit associations among a plurality of items in a data set comprising:

processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

quantifying conceptually-based associative relationships among said plurality of items and said plurality of concepts by means of a dual-scaling algorithm; and

representing said relationships within a simulated three-dimensional visual space;

wherein said representing step comprises displaying the concepts as one type of virtual object and the items as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of item objects reflects the degree to which the items are associated with one another and wherein the relative distance between each concept object and each item object reflects the degree to which each item is associated with each concept.

16. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of claim 15.

17. A computer-implemented system for displaying implicit associations among a plurality of items in a data set comprising:

means for processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

means for extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

means for quantifying conceptually-based associative relationships among said plurality of items and said plurality of concepts by means of a dual-scaling algorithm; and

means for representing said relationships within a simulated three-dimensional visual space;

wherein said means for representing comprises means for displaying the concepts as one type of virtual object and the items as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of item objects reflects the degree to which the items are associated with one another and wherein the relative distance between each concept object and each item object reflects the degree to which each item is associated with each concept.

18. A computer-implemented method for displaying implicit associations among a plurality of items in a data set comprising:

processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

quantifying conceptually-based associative relationships among said plurality of items; and

representing said relationships within a simulated three-dimensional visual space;

wherein said quantifying step comprises

producing a similarity matrix wherein each item is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description, and

quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space.

19. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of claim 18.

20. A computer-implemented system for displaying implicit associations among a plurality of items in a data set comprising:

means for processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

means for extracting implicit or inherent conceptual information from said plurality of item descriptions to produce a plurality of concepts;

means for quantifying conceptually-based associative relationships among said plurality of items; and

means for representing said relationships within a simulated three-dimensional visual space;

wherein said means for quantifying comprises

means for producing a similarity matrix wherein each item is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of the degree to which the corresponding concept is present in the corresponding modified entity description, and

means for quantifying the associative structure of the data set by subjecting the similarity matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a multi-dimensional Euclidean space.

21. A method for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

normalizing the contingency matrix to produce a normalized matrix;

subjecting the normalized matrix to singular value decomposition to produce singular values;

transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

adjusting the range of said coordinates so that said space fits in a display frame; and

displaying concepts and entities as objects in said space;

wherein said assembling step comprises producing a contingency matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description;

wherein said subjecting step and transforming step comprise quantifying the associative structure of the data set by subjecting the contingency matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a three-dimensional Euclidean space; and

wherein said displaying step comprises displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

22. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of claim 21.

23. A computer-readable medium having stored thereon sequences of instructions which when executed by a processor cause the processor to perform the steps of:

acquiring a data set comprised of items;

organizing the items into a plurality of data pairs, each data pair comprising an entity and a description of the entity;

creating a set of modified entity descriptions by pruning irrelevant terms from entity description lists and reducing each remaining term to a linguistic root form;

extracting concepts from said set of modified entity descriptions, each concept comprising a root term that is associated with more than one modified entity description;

producing a contingency matrix wherein each entity is a column and each concept is a row or vise versa, with the element of each such column and row containing an indication of whether the concept is associated with the corresponding modified entity description;

quantifying the associative structure of the data set by manipulating the contingency matrix as follows;

collapsing identical row profiles and combining the concept terms associated with each row profile into a single complex term, and

subjecting the contingency matrix to singular value decomposition and another mathematical operation to produce an n-dimensional representation of the contingency matrix in Euclidean space;

scaling said raw coordinates to produce coordinates of each concept and each entity usable in a given three-dimensional display space; and

displaying each concept as a concept object and each entity as an entity object on said monitor or projection device with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distances among the concept objects reflect the degree to which the concepts are associated with one another, wherein the relative distances among the entity objects reflect the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

24. A system for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

means for assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

means for normalizing the contingency matrix to produce a normalized matrix;

means for subjecting the normalized matrix to singular value decomposition to produce singular values;

means for transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

means for adjusting the range of said coordinates so that said space fits in a display frame; and

means for displaying concepts and entities as objects in said space;

wherein said means for assembling comprises means for producing a contingency matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description;

wherein said means for subjecting and means for transforming comprise means for quantifying the associative structure of the data set that is operative to subject the contingency matrix to means for dual-scaling, in combination with means for matrix transformation, thereby producing a set of coordinates for each concept and each entity in a three-dimensional Euclidean space; and

wherein said means for displaying comprises means for displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

25. A method for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

normalizing the contingency matrix to produce a normalized matrix;

subjecting the normalized matrix to singular value decomposition to produce singular values;

transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

adjusting the range of said coordinates so that said space fits in a display frame; and

displaying concepts and entities as objects in said space;

wherein said displaying step comprises displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

26. A system for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

means for assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

means for normalizing the contingency matrix to produce a normalized matrix;

means for subjecting the normalized matrix to singular value decomposition to produce singular values;

means for transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

means for adjusting the range of said coordinates so that said space fits in a display frame; and

means for displaying concepts and entities as objects in said space;

wherein said means for displaying comprises means for displaying the concepts as one type of virtual object and the entities as a second type of virtual object with each object located at the appropriate coordinates in the three-dimensional space, wherein the relative distance between each pair of concept objects reflects the degree to which the concepts are associated with one another, wherein the relative distance between each pair of entity objects reflects the degree to which the entities are associated with one another and wherein the relative distance between each concept object and each entity object reflects the degree to which each entity is associated with each concept.

27. A method for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

normalizing the contingency matrix to produce a normalized matrix;

subjecting the normalized matrix to singular value decomposition to produce singular values;

transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

adjusting the range of said coordinates so that said space fits in a display frame; and

displaying concepts and entities as objects in said space;

wherein said assembling step comprises producing a contingency matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description; and

wherein said subjecting step and transforming step comprise quantifying the associative structure of the data set by subjecting the contingency matrix to procedures comprising dual-scaling, in combination with a matrix transformation, thereby producing a set of coordinates for each concept and each entity in a three-dimensional Euclidean space.

28. A system for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

means for assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

means for normalizing the contingency matrix to produce a normalized matrix;

means for subjecting the normalized matrix to singular value decomposition to produce singular values;

means for transforming the singular values to produce the coordinates for concept and entities in a simulated three-dimensional space;

means for adjusting the range of said coordinates so that said space fits in a display frame; and

means for displaying concepts and entities as objects in said space;

wherein said means for assembling comprises means for producing a contingency matrix wherein each entity is represented as a column in said matrix and each concept is represented as a row, or vise versa, with the element at the intersection of each said column and each said row containing an indication of whether the corresponding concept is present in the corresponding modified entity description; and

wherein said means for subjecting and means for transforming comprise means for quantifying the associative structure of the data set that is operative to subject the contingency matrix to means for dual-scaling, in combination with means for matrix transformation, thereby producing a set of coordinates for each concept and each entity in a three-dimensional Euclidean space.

29. A computer-implemented method for displaying implicit associations among a plurality of items in a data set comprising the steps of:

processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form unlinked name-description pairs;

extracting implicit or inherent conceptual information from said plurality of item descriptions;

quantifying conceptually-based associative relationships among said plurality of items by means of a dual-scaling algorithm; and

representing said relationships within a simulated three-dimensional visual space that contains representation of each item;

wherein the representation of each item is hyperlinked to the item.

30. A computer-implemented system for displaying implicit associations among a plurality of items in a data set comprising the steps of:

means for processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

means for extracting implicit or inherent conceptual information from said plurality of item descriptions to produce unlinked concepts that comprise a word or group of words that is associated with at least two of the items;

means for quantifying conceptually-based associative relationships among said plurality of items by means of a dual-scaling algorithm; and

means for representing said relationships within a simulated three-dimensional visual space.

31. A computer-implemented method for displaying implicit associations among a plurality of items in a data set to a user, said method comprising:

a step for processing each item in said plurality of items to produce a plurality of item names and a plurality of item descriptions which form name-description pairs;

a step for extracting implicit or inherent conceptual information from said plurality of item descriptions to produce concepts that are not connected to one another by navigable links;

a step for quantifying conceptually-based associative relationships among said plurality of items and concepts by means of a dual-scaling algorithm; and

a step for representing said relationships within a simulated three-dimensional visual space in which the degree to which the concepts and the items are related to one another is indicated to the user by Euclidian distances between the concepts and the items and in which the concepts and the items are not connected or organized in a hierarchical, taxonomic structure.

32. A method for facilitating recognition and understanding of relationships or associations among a set of data entities comprising:

assembling a contingency matrix that reflects the occurrence within descriptions of the entities of concepts identified in descriptions of the entities;

normalizing the contingency matrix to produce a normalized matrix;

subjecting the normalized matrix to singular value decomposition to produce singular values;

transforming the singular values to produce the coordinates for concepts and entities in a simulated three-dimensional space;

adjusting the range of said coordinates so that said space fits in a display frame; and

displaying concepts and entities as objects in said space in which the degree to which the concepts and the items are related to one another is indicated to the user by Euclidian distances between each two objects.