Dynamic Taxonomy Process for Browsing and Retrieving Information in Large Heterogeneous Data Bases

• US 20110270878A1
• Filed: 07/11/2011
• Published: 11/03/2011
• Est. Priority Date: 12/16/1998
• Status: Active Grant

• Current Assignee: Giovanni Sacco
• Original Assignee: 21 srl
• Inventors: Sacco, Giovanni
• Granted Patent: US 8,650,171 B2
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/34 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957
• CPC Class Codes: Y10S 707/954 Relational
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99937 Sorting
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A process is disclosed for retrieving information in large heterogeneous data bases, wherein information retrieval through visual querying/browsing is supported by dynamic taxonomies; the process comprises the steps of: initially showing (F1) a complete taxonomy for the retrieval; refining (F2) the retrieval through a selection of subsets of interest, where the refining is performed by selecting concepts in the taxonomy and combining them through boolean operations; showing (F3) a reduced taxonomy for the selected set; and further refining (F4) the retrieval through an iterative execution of the refining and showing steps.

Claims

1. A method for retrieving information from databases, said databases being structured or unstructured, said databases being homogeneous or heterogeneous, wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items in said databases being classified under one or more concepts, said items and their classification being called an extension, said method comprising, given an initial current subset of interest:

using a computer for providing a reduced taxonomy for the current subset of interest;

using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations;

and

using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein;

said initial subset of interest includes all the items in the extension of the dynamic taxonomy or a subset of them;

said reduced taxonomy is derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified;

said step of pruning concepts includes eliminating from the taxonomy all the concepts under which no item in the current subset of interest is classified, or preventing said concepts from being displayed, or preventing said concepts from being selected in order to refine interest sets;

said step of providing a reduced taxonomy either reports only the concepts belonging to the reduced taxonomy or, for each such concept also reports how many items in the current interest set are classified under the concept;

in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension.

2. The method of claim 1, said method to construct and provide through a reduced taxonomy the relationships between any two concepts based on the classification by using the computer, a relationship between any two concepts existing if at least one item is classified (1) under a first concept or any descendants of the first concept, and (2) under a second concept, or any descendants of the second concept.

3. The method of claim 1 wherein each item is identified by a unique identifier, and, for each concept, there exists a deep classification that lists all items classified under said concept or any descendants of said concept in the taxonomy, and an optional shallow classification that lists all items directly classified under said concept, said deep classification and optional shallow classification being either stored or computed.

4. The method of claim 3, wherein boolean operations on concepts are implemented through corresponding set operations on the deep classification of said concepts.

5. The method of claim 3, wherein said step of providing a reduced taxonomy for the selected subset of interest comprises a testing operation such that a concept is provided in the reduced taxonomy if an intersection between the selected subset of interest and the deep classification of the concept is not empty, the testing operation being configured to optionally count a number of items in said intersection to show a user a number of items in the selected subset of interest that are also classified under the concept, said testing operation being also configured to be applied to the shallow classification, if used, to show the user a number of items in the selected subset of interest that are also directly classified under the concept, the number being useful when items can be classified at any level of abstraction in the taxonomy, said testing operation being also configured to be applied to a set including a single item, in order to compute a classification of said item, if not explicitly stored, said testing operation being also used to produce a reduced tree by testing and providing sons of a root and, on subsequent explosion of a concept, testing and providing sons of said concept.

6. The method of claim 3, wherein deep classifications are physically stored as uncompressed or compressed bit vectors, and a counting of items in a result of logic operations on bit vectors is performed through a constant table whose size is 2n, whose i-th element contains a number of bits at 1 in binary number i, and processing the uncompressed form of the bit vector n bits at a time, adding to a counter, for every group j of n bits, contents of the element of said constant table whose index in said table is equal to the binary value of said group j of n bits.

7. The method of claim 1, wherein said classification is implicitly stored as virtual concepts in external databases, said virtual concepts being concepts for which neither actual sons, that are actual values of a domain to be represented, nor an actual classification are stored, but instead are computed, said virtual concept being a simple virtual concept, which is described by four abstract operations:

given a virtual concept, retrieve all its sons;

given a virtual concept, retrieve its deep classification;

given a son of a virtual concept, retrieve its deep classification; and

given an item, find all the terminal concepts, descendants of the virtual concept, under which said item is classified;

said virtual concept being a derived virtual concept, which is a simple virtual concept with additional restrictions.

8. The method of claim 1, wherein said dynamic taxonomy is used to represent data represented by a single view on an external database, said items corresponding to tuples, rows, records, or objects in said view and, in order to identify an item, a candidate key of said view being used as item identifier or two abstract relations being kept for mapping system-generated item identifiers to and from a primary key of the view, a taxonomy for said view being able to be constructed by inserting concepts of interest for said view in the taxonomy, each concept being associated to a boolean clause, said boolean clause to reference any attribute of a tuple in said view and returning true if and only if said tuple must be classified under said concept, said concept being a real concept or a virtual concept, said virtual concept using said view for a synthesis of sons and extensions.

9. The method of claim 1, said method accounting for the popularity of items by representing a measure of popularity through a branch in the taxonomy, wherein said measure of popularity for an item is derived from the number of predefined actions, including access or purchase, applied to said item, or is supplied by an external source, including user or editorial reviews.

10. The method of claim 1, said method allowing the personalization of interaction by predefining a subset of interest for a specific user or a set of users, and having the system use said subset of interest instead of the universe for all interactions for said user or set of users.

11. The method of claim 1, said method allowing the restriction of access to a predefined subset of interest for a specific user or a set of users by using said predefined subset of interest instead of the universe for all the interactions of said specific user or a set of users.

12. The method of claim 1, said method allowing tracking of user interests by keeping track of all the concepts used to define each focus, or selected subset of items.

13. The method of claim 1, said method performing the computation of the reduced taxonomy for a set of items, said method working on a relation containing a pair of attributes, one of said attributes representing item identifiers, the other one of said attributes representing concept identifiers in such a way that if there is in such relation a tuple with a specific item identifier and a specific concept identifier then the item identified by said specific item identifier is classified under the concept identified by said specific concept identifier, said method comprising retrieving all the tuples containing an item identifier belonging to said set of items;

counting the number of tuples in said retrieved tuples for each concept identifier, and producing said reduced taxonomy, concepts in said reduced taxonomy being all the concept identifiers with a count larger than zero, and the ancestors of said concepts.

14. The method of claim 1, further comprising saving computed interest sets in order to avoid recomputation.

15. The method of claim 1, said method further comprising saving computed reduced taxonomies in order to avoid recomputation.

16. The method of claim 1, said method applied to the retrieval of real estate items, wherein:

said intension also comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature;

said real estate items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature.

17. The method of claim 1, said method applied to the retrieval of person or entity records for applications including matchmaking, social networks or human resource management wherein:

said intension also comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature; and

said person or entity records are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature.

18. The method of claim 1, said method applied to diagnostic applications such as, for example, medical diagnosis or malfunction diagnosis wherein:

said intension also comprises a set of features comprising symptoms, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature; and

said items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature.

22. The method of claim 1, said method applied to the retrieval of multimedia data items for applications such as image databases, multimedia databases or video-on-demand wherein:

said intension also comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature; and

said multimedia data items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature

23. The method of claim 1, wherein items in said extension also comprise numerical attributes, and for each concept in the taxonomy, numerical attributes are presented as numerical values computed by one or more aggregating functions from the values of the numerical attribute for all the items in the interest set that are classified under said concept, said aggregating functions being predefined or defined.

24. The method of claim 1, said method further comprising a geographic map, such geographic map being used to show the items in the selected subset of interest.

25. The method of claim 1, said method further comprising a geographic map, the area of such geographic map being modifiable by users, such area being used to refine the interest set by discarding from the interest set all the items whose geographic coordinates do not fall within such area.

26. The method of claim 1 where one or more concepts represent a tag cloud, said tag cloud having as descendants all or parts of the terms or phrases that are derived from the items, each tag cloud and each of descendants is used as a dynamic taxonomy concept to define a subset of interest possibly in combination with other clouds or concepts or querying methods, each tag cloud and each of descendants is used as a dynamic taxonomy concept to summarize a subset of interest.

27. The method of claim 1 where the current subset of interest is refined by intersecting it with the combination of one or more taxonomy concepts through Boolean operations or with the result of executing querying methods, said querying methods retrieving classified items according to different selection criteria, or with a combination of both, or by intersecting it with a set of items selected by using a computer.

28. The method of claim 1 where the initial subset of interest is specified by the combination of one or more taxonomy concepts through Boolean operations or by the result of executing querying methods, said querying methods retrieving classified items according to different selection criteria, or by a combination of both.

29. The method of claim 1 where the intension is organized as a hierarchy of concepts or as a directed acyclic graph of concepts, said directed acyclic graph allowing a concept to have multiple fathers;

30. The method of claim 1 where the intension is organized with the only restriction that given an ancestor concept and a descendant concept in the organization, the deep classification of the descendant concept is a subset of the deep classification of the ancestor concept or it is equal to the deep classification of the ancestor concept;

31. The method of claim 1 where the computation of each subset of interest includes a restriction on a specific location, said location being manually selected by using the computer or automatically selected by means of predefined preferences or geo-localization devices.

32. The method of claim 1, said method applied to the retrieval of items in digital libraries, wherein:

said intension also comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature;

said digital library items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature.

34. The method of claim 1 wherein the computation of each subset of interest includes a restriction on items belonging to a specific region, said region representing one or more locations, said region being manually selected by using the computer or automatically selected by means of predefined preferences or geo-localization devices or geo-localization software.

19. A method for retrieving real estate items, wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items being classified under one or more concepts, said items and their classification being called an extension, said method comprising, given an initial current subset of interest:

using a computer for providing a reduced taxonomy for the subset of interest;

using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations;

and

using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein;

said initial subset of interest includes all the items in the extension of the dynamic taxonomy, or a subset of them;

said reduced taxonomy being derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified;

said step of pruning concepts includes eliminating from the taxonomy all the concepts under which no item in the current subset of interest is classified, or preventing said concepts from being displayed, or preventing said concepts from being selected in order to refine interest sets;

said step of providing a reduced taxonomy either reports only the concepts belonging to the reduced taxonomy or, for each such concept, also reports how many items in the current interest set are classified under the concept;

said organization of concepts may include a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature;

said items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature;

in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension.

20. A method for retrieving items from electronic catalogs, for applications such as electronic commerce or electronic auctions, wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items in said electronic catalogs being classified under one or more concepts, said items and their classification being called an extension, said method comprising, given an initial current subset of interest:

using a computer for providing a reduced taxonomy for the subset of interest;

using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations;

and

using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein;

said initial subset of interest includes all the items in the extension of the dynamic taxonomy, or a subset of them;

said reduced taxonomy being derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified;

said step of pruning concepts includes eliminating from the taxonomy all the concepts under which no item in the current subset of interest is classified, or preventing said concepts from being displayed, or preventing said concepts from being selected in order to refine interest sets;

said step of providing a reduced taxonomy either reports only the concepts belonging to the reduced taxonomy or, for each such concept, also reports how many items in the current interest set are classified under the concept;

said organization of concepts for said electronic catalogs comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature;

said items in said electronic catalogs are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature;

in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension.

33. The method of claim 20 where said features include price.

21. A method for the retrieval of person or entity records for applications including matchmaking, social networks or human resource management, wherein retrieval is performed through visual queries on dynamic taxonomies, said dynamic taxonomies being an organization of concepts that ranges from a most general concept to a most specific concept, said concepts and their organization being called an intension, items being classified under one or more concepts, said items and their classification being called an extension, said method comprising, given an initial current subset of interest:

using a computer for providing a reduced taxonomy for the subset of interest;

using the computer for refining the current subset of interest of said reduced taxonomy with the combination of one or more taxonomy concepts through Boolean operations;

and

using the computer for iteratively repeating said steps of providing a reduced taxonomy for the current subset of interest to further refine said retrieval and of refining the current subset of interest, wherein;

said initial subset of interest includes all the items in the extension of the dynamic taxonomy, or a subset of them;

said reduced taxonomy being derived from said taxonomy by using the computer for pruning concepts under which no item in said current subset of interest is classified;

said step of pruning concepts includes eliminating from the taxonomy all the concepts under which no item in the current subset of interest is classified, or preventing said concepts from being displayed, or preventing said concepts from being selected in order to refine interest sets;

said step of providing a reduced taxonomy either reports only the concepts belonging to the reduced taxonomy or, for each such concept, also reports how many items in the current interest set are classified under the concept;

said organization of concepts comprises a set of features, each of said features being a descendant concept of the root concept of said organization, each of said features having as descendants in the taxonomy a set of concepts, each concept in said set of concepts representing either a single value or a set of values for said feature;

said items are classified, for each said feature, under zero or more concepts representing either a single value or a set of values for that feature;

in said extension, there exists at least one item such that said item is classified under at least two different concepts such that each of said two concepts is neither an ancestor nor a descendant of the other concept in the intension.