e-Stract: a process for knowledge-based retrieval of electronic information

• US 20010049671A1
• Filed: 06/05/2001
• Published: 12/06/2001
• Est. Priority Date: 06/05/2000
• Status: Abandoned Application

• Current Assignee: Werner B. Joerg
• Original Assignee: Werner B. Joerg
• Inventors: Joerg, Werner B.
• Application Number:
  US09/874,822
• Publication Number:
  US 20010049671A1
• Time in Patent Office: Days
• US Class Current: 706/50
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

This invention addresses the problems of current search techniques on the Internet—volume, ranking, difficulty to assess—and extends the solution to all kinds of electronic information accessible through networks and databases. The solution principle engages the help of specialists in particular domains and supplies them with tools to effectively scour the information resources for high quality information in their field, to commit that knowledge to distributed databases, to construct dedicated knowledge environments, and to submit corresponding context information to centralized registries. End users implicitly access mirrored services of these registries and use the context information to focus their searches onto the resources qualified by the expert network. Many of the individual techniques involved in building the tools for deployment, operation and exploitation of such “Networks of Qualified Knowledge” are well known and may in the future be replaced by more effective techniques. The essence of the invention lies in the way these techniques are put to use to implement the presented process.

Claims

1. A process to create, operate and exploit Networks of Qualified Knowledge:

a. Said networks host knowledge about select electronic information (“

document”

) relevant to their domain of discourse.

b. Access to such knowledge is enabled through context-directed retrieval.

c. Said process facilitates the linking of quality information with means for interaction and collaborative problem solving.

2. A process to create knowledge about select electronic information. Said process includes:

a. Acquisition of raw information from a plurality of electronic information sources, including but not limited to, local and remote files and data directories, databases, Internet.

b. Extraction of Key items through analysis of the source information, to identify terms, phrases, shapes, sequences or patterns.

c. Pattern and distribution analysis of key items to determine role and relevance (“

rating”

) for each key item in the document.

d. A fuzzy-logic based technique to derive intrinsic contextual information through matching of weighted key item patterns.

e. A vicinity technique to derive external contextual information from information sources that reference the document under consideration

f. A fitting technique that exploits the results of d. (intrinsic context) and e. (external context) to consolidate the context evaluation of the document considered and to enrich the set of context definitions.

3. A computer method implementing phase a. of

claim 2

as an asynchronous tool available to one or more operators (“

Knowledge Engineers”

) locally or through a computer network with the following services;

a. A graphical user interface to define Extraction tasks as a combination of search criteria, extraction method and backend filters (context filters).

b. A technique to save, modify and restore such tasks for periodic or occasional execution.

c. Said search criteria include but are not limited to generic techniques (such as local/remote directory scans, “

bookmark”

files and other URL lists), customized techniques (e.g. to scan databases, launch search request through Internet search engines, meta search engines or Internet directories), and breadth of embedded link navigation.

d. Said extraction methods include but are not limited to “

know it all”

techniques such as “

looking for known key items”

, and to adaptive techniques such as NGrams and “

looking for new key items”

.

e. A collection of methods to perform the actual network scans and search launches as defined by the Extraction tasks. The results are added successively to a document queue.

f. A method to pre-scan all retrieved documents for non-self referential links and perform iterative navigation of the embedded links to the breadth as defined by the Extraction tasks, and add new references successively to the document queue.

g. A method to prevent duplicate entries into the document queue.

h. A graphical user interface to define database update tasks based on a choice of criteria such as fixed periodic intervals, most frequent use, least frequent use, and other.

i. A method to implement one or more “

autonomous”

bots, monitoring database usage and generating update lists according to the criteria set by the operator in said database update tasks.

4. A computer method implementing phase b. of

claim 2

, with the following services for, but not limited to, textual key items:

a. A dictionary of key items classified into items of interest, items to ignore and items frequently misspelled. Grammatical variations of items are recorded in rule form, to allow for limited grammatical analysis of documents.

b. A user interface to the dictionary to search, review and modify its contents (items and associated grammatical rules).

c. A method for remote access to dictionaries produced by other operators, for content initialization, update and exchange.

d. An optional reverse index database that records links to given documents. Such database may be local or remote.

e. A optional method that extracts non-self referential links and submits them to said reverse index.

f. A collection of user selectable methods consistent with phase d. of

claim 3

, to extract known key items of interest and new potential key items from the documents.

g. A user interface to alert the operator and enable user supported validation of new potential key items.

h. A method to record occurrences (distribution and frequency) of valid key items in a document abstract. Said document abstracts are queued on pending key item validations.

5. A computer method to implement phase c. of

claim 2

with the following services:

a. A user interface to specify rating criteria. Said criteria may include, but are not limited to, folding the key item distribution with standard distribution functions. Width, symmetry and center are typical parameters for such functions. Said functions may extend over the entire document under consideration or fixed portions or relative portions thereof

b. A collection of methods that implement the allowable operator selections for rating criteria and that record the resulting value(s) in said document abstract.

6. A computer method to implement phases d., e. and f. of

claim 2

with the following services:

a. A database (“

Context base”

) that holds context definitions (“

name”

) and context descriptions (fuzzy set in key items). Entries may consist of definition only (“

named context”

), description only (“

un-named context”

) or both (“

defined context”

).

b. A user interface to said context base to search, review and modify its content.

c. A method for remote access to context bases produced by other operators, for content initialization, update and exchange.

d. A set of 3 basic operating modes—

priming, learning and normal operation. Said priming mode implies that the extraction process executes over one or more reference documents;

said learning mode implies that the extraction process executes over a trusted set of documents;

normal operation does not make any such assumption.

e. A set of methods and user interaction for priming operation;

collections of extracted key items are presented for manual allocation to context definitions (“

context induction”

).

f. A set of methods and user interaction for learning operation;

key item patterns are used to refine existing context descriptions (“

context fitting”

).

g. A set of methods for normal operation to match context descriptions from said context base to key item patterns in said document abstract. The methods support both matching of clustered patterns for localized context, and matching of document wide patterns for overall context. They support both non-subtractive and subtractive extraction (items that match a context are “

removed”

from the document abstract).

h. A method that retrieves referral knowledge from said reverse index or from a third party reverse index service. It locates all references pointing to the document under consideration within the documents addressed by such referral knowledge. It extracts key items in the “

vicinity”

of the references and attempts to match them (“

external contexts”

) to the intrinsic contexts or known context definitions. Depending on the operating mode (phase d. above) such matchings are used for automated context learning (refinement and extension).

i. A method to create a data structure (“

knowledge record”

or k-record) summarizing the findings from said document abstract.

j. A method to reconcile discrepancies between external and intrinsic contexts and record the best fittings in said k-record.

7. A process to enrich knowledge created through the process of

claim 2

. Said process includes:

a. Filtering of k-records to retain only records that match user defined context criteria. Said criteria are formally defined as fuzzy expressions over context definitions and warrant that minimum or maximum matching thresholds are met.

b. K-records that are not filtered out are submitted to the operator for inspection, optional annotation and committing to a database.

8. A computer method to implement

claim 7

, with the following services:

a. A database (“

Knowledge base”

or k-base) that holds the summarized information (k-record) about the documents of interest.

b. A server for remote access to the k-base by the distribution mechanism of the e-Stract process.

c. A user interface to search, review and modify the content of the k-base.

d. A method to filter k-records in accordance with the filter criteria set by the generating Extraction task (phase a. in

claim 3

).

e. A user interface to review and edit the content of k-records, to access the document referenced therein, to add annotations to the record and to commit the completed record. If the document is already referenced by a record in the k-base, the operator may delete/modify either, or merge them.

f. A method that identifies records generated by database update requests, bypassing the filtering mechanism and comparing the results with the current entries—

small changes are updated automatically;

large changes are presented to the operator through the interface e. above.

9. The embodiment realized through claims 3, 4, 5, 6, and 8 constitute a Knowledge Engineer'"'"'s tool “

EX-Stract”

.

10. A process to elucidate knowledge recorded in k-bases. [Elucidation in this context deals with augmenting existing knowledge by structuring it, associating it with other knowledge, complementing it with means for interaction, and annotating it to form a knowledge node (k-node) for particular target audiences]. Said process includes:

a. Connectivity to qualified knowledge sources (k-bases and other k-nodes).

b. A toolset to build structured k-nodes as dedicated knowledge delivery environments.

c. A technique to control access to the resources of a k-node by individuals and groups.

d. Support for team-based problem solving.

e. Personalized remote visibility control of k-node resources

11. A computer method implementing

claim 10

, with the following services:

a. A collection of methods defining templates for items (e.g. container, text entity, graphic object, book) and services (e.g. chat, conference, meeting, file exchange) offered. Such templates are listed in the object library.

b. An access method to local and remote k-bases, paired with a context filter.

c. A collection of methods for the instantiation of templates as e-Stract objects and allocation of attributes such as context information and descriptive notes.

d. A collection of methods for the maintenance of user lists/group lists, allocation of access policies with individual objects, and association of objects and access rights.

e. An action permission scheme that limits individual operations of objects to selectable access rights.

f. A database (k-node) that holds the instances of e-Stract objects and their graph structure for access path validations.

g. A server for remote access to the k-node by end-users and other k-nodes.

h. An execution framework that supports concurrent access of end-users and operators under the constraints imposed by access rights and action permissions of individual objects.

i. A method to register select objects with a (centralized) registry (

claim 13

)

j. A user interface supporting all actions under this claim.

k. The embodiment of actions and interfaces under this claim constitute the Content Manager'"'"'s tool “

AB-Stract”

.

12. A process to distribute k-node objects across a virtual network for context-directed retrieval. Said process includes:

a. A centralized submission mechanism for e-Stract objects characterized by their type and associated contexts. [Centralized does not mean unique;

each Network of Qualified Knowledge may boast its own registry].

b. A distribution mechanism of submitted context information to end-users through computer networks.

13. A computer method implementing

claim 12

, with following services:

a. A database serving as (central) context registry (CCR), accepting submissions, verifying their validity, testing for consistency, maintaining corresponding context graphs and monitoring the periodical renewal.

b. A method (context routing service, or CRS) to distribute and periodically update context graphs to strategically positioned locations for efficient (implicit) access by end-users.

14. A process for context-directed retrieval of e-Stract objects and associated services. Said process includes:

a. A mechanism for implicit connection to the context network and efficient focusing on contexts of interest.

b. A mechanism to launch searches, optionally refined by Boolean expressions, on all (and only those) k-nodes that satisfy the given context conditions.

c. A mechanism to receive and display the results, and enable the available services.

15. A computer method implementing

claim 14

with following services:

a. A “

Context Lens”

method that connects to the closest CRS, retrieves portions of the context graph as required by the end-user'"'"'s successive choices. Upon completion of the choices, it requests pertinent node information from the CRS for the search builder.

b. A graphical user interface that shows the local connectivity between contexts and their “

distance”

from the current context. Said distance effect is achieved with shading and perspective. The interface provides controls for “

zooming” and

navigating along the context graph.

c. A user interface to specify searches (Boolean, key term based, or other constraints such as object type, dates) within the context space focused on with said context lens.

d. A search builder that uses the node information from said context lens and the Boolean search to launch concurrent searches on the nodes of interest.

e. A method to receive and present the search results to the end-user.

f. A user interface to display the search results;

to support navigation through said results; and

to access the k-node services associated with said results.

g. The embodiment of actions and interfaces under this claim constitute the Enduser'"'"'s tool “

VUe-Stract”

.