Method and system for concept generation and management

US 20070174041A1
Filed: 04/30/2004
Published: 07/26/2007
Est. Priority Date: 05/01/2003
Status: Abandoned Application

First Claim

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1. A method for defining and generating a set of concepts and identifying said concepts in text, comprising:

a) defining said set of concepts wherein;

i) each of said concepts comprises a pattern;

ii) each of said patterns comprising one of the following;

1) a description sufficiently constrained to be matchable to zero or more extents;

each of said extents comprising a set of zero or more items wherein each of said items is an instance of a linguistic entity;

each of said instances of said linguistic entity is identified in a) text, or b) a knowledge resource;

or c) both a) and b); and

said pattern is matchable to zero or more of said extents corresponding to said description;

or 2) an operator and a list of zero or more arguments wherein each of said arguments is a further pattern; and

said pattern comprising said operator and said list of arguments is matchable to extents that are the result of applying said operator to further extents that are matchable by said arguments;

or 3) a reference to a further concept comprising a further pattern; and

said pattern comprising said reference to said further concept is matchable to extents that are matchable by said further pattern; and

iii) any said further pattern is a pattern; and

b) generating said concepts from text or one or more sources of knowledge; and

c) identifying said concepts in text.

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Abstract

The present invention is in two parts. The first part is manual, semi-automatic, and automatic methods and a system for generating concepts. The second part is a method and system for the management of concepts. Such concepts (lower case c) are linguistics-based patterns or set of patterns. Each pattern comprises other patterns, concepts, and linguistic entities of various kinds, and operations on or between those patterns, concepts, and linguistic entities. The present invention improves upon the notion of Concepts as defined within the Concept Specification Language (CSL) of PCT Application No. WO 02/27524 by Fass et al. (2001). CSL Concepts are linguistics-based Patterns or set of Patterns. Each Pattern comprises other Patterns, Concepts, and linguistic entities of various kinds, and Operations on or between those Patterns, Concepts, and linguistic entities. Central to the first part of the invention are notions of a “User concept Description” (UcD), User Concept Description (UCD), “concept wizard,” and “Concept wizard.” UcDs and UCDs are representations of what is used to generate a concept or Concept, including, but not limited to, knowledge sources used as the basis of generation, the data model used to control generation, and instructions (Directives) governing generation. The concept wizards and Concept wizards are tools for navigating users through concept and Concept generation.

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112 Claims

1. A method for defining and generating a set of concepts and identifying said concepts in text, comprising:
- a) defining said set of concepts wherein;
  
  i) each of said concepts comprises a pattern;
  
  ii) each of said patterns comprising one of the following;
  
  1) a description sufficiently constrained to be matchable to zero or more extents;
  
  each of said extents comprising a set of zero or more items wherein each of said items is an instance of a linguistic entity;
  
  each of said instances of said linguistic entity is identified in a) text, or b) a knowledge resource;
  
  or c) both a) and b); and
  
  said pattern is matchable to zero or more of said extents corresponding to said description;
  
  or 2) an operator and a list of zero or more arguments wherein each of said arguments is a further pattern; and
  
  said pattern comprising said operator and said list of arguments is matchable to extents that are the result of applying said operator to further extents that are matchable by said arguments;
  
  or 3) a reference to a further concept comprising a further pattern; and
  
  said pattern comprising said reference to said further concept is matchable to extents that are matchable by said further pattern; and
  
  iii) any said further pattern is a pattern; and
  
  b) generating said concepts from text or one or more sources of knowledge; and
  
  c) identifying said concepts in text.
- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
- - 2. The method of claim 1 wherein each said linguistic entity comprises:
    - a) a morpheme;
      
      or b) a word or phrase;
      
      or c) a lexically-related term;
      
      or d) a constituent or subconstituent;
      
      or e) an expression in a linguistic notation representing a phonological, morphological, syntactic, semantic, or pragmatic-level description of text;
      
      or f) a combination of one or more of linguistic entities.
  - 3. The method of claim 1 wherein said linguistic entity is identified in a text and the start position and end position of said linguistic entity in said text is recorded.
  - 4. The method of claim 1 wherein each said operator may comprise:
    - a) a zero-argument operator that expresses information including;
      
      i) match information, or ii) syntax information, or iii) semantic information;
      
      or b) a one-argument operator that expresses information including;
      
      i) match information, or ii) tense, or iii) syntactic categories, or iv) Boolean relations, or v) lexical relations, or vi) semantic categories;
      
      or c) a two-argument operator that expresses information including;
      
      i) relationships within and across sentences, or ii) syntactic relationships, or iii) Boolean relations;
      
      or iv) semantic relationships.
  - 5. The method of claim 4 wherein one of said two-argument operators comprises nonimmediately_dominates(X,Y) wherein:
    - a) X matches any extent;
      
      b) Y matches any extent; and
      
      c) the result is the extent matched by Y if each of the linguistic entities of Y'"'"'s extent are a subconstituent of all linguistic entities of X'"'"'s extent.
  - 6. The method of claim 4 wherein one of said two-argument operators is nonimmediately_dominates(X,Y) when it is “
    - wide-matched”
      
      , wherein a) X matches any extent;
      
      b) Y matches any extent; and
      
      c) the result is said extent matched by X if each of the linguistic entities of Y'"'"'s extent are a subconstituent of all linguistic entities of X'"'"'s extent.
  - 7. The method of claim 4 wherein for one of said two-argument operators is nonimmediately_precedes(X,Y) wherein:
    - a) X matches any extent;
      
      b) Y matches any extent, and c) the result is an extent that covers the extent matched by Y and an extent matched by X if the extent matched by X precedes the extent matched by Y.
  - 8. The method of claim 1 wherein each of said patterns may further comprise a) a parameter that is matchable to extents matched by any pattern that is bound to said parameter, and wherein b) any pattern may be bound to a parameter.
  - 9. The method of claim 8 wherein each of said patterns may further comprise a) a reference to a further concept comprising a further pattern and b) a list of zero or more arguments wherein each of said arguments comprise a further pattern;
    - and said pattern comprising said reference to said further concept is matchable to extents that are matchable by said further pattern in said further concept, where any parameters in said further concept are bound to said further patterns in said list of zero or more arguments.
  - 10. The method of claim 1 wherein each of said concepts may further comprise a) a name for said concept and b) a set of one or more instructions selected from the following:
    - i) whether successful matches of said concept against text are “
      
      visible”
      
      ;
      
      ii) the number of matches of a concept required in a document for said document to be returned;
      
      iii) the name for said concept that is being generated;
      
      iv) the name of a file into which that concept is written;
      
      or v) whether or not said file is encrypted.
  - 11. The method of claim 1 wherein a User concept Description (UcD) is used to generate a concept, specifying ways in which concepts can be generated from different types of knowledge (knowledge sources) by way of different data models, governed by various instructions, said UcD comprising:
    - a) one or more knowledge sources that provide raw content used to generate concepts, b) one or more data models used to combine said knowledge sources used to generate concepts, and c) one or more instructions governing said generation of said concepts.
  - 12. The method of claim 11 wherein said knowledge sources are selected from one of:
    - a) text-based knowledge sources;
      
      b) linguistic knowledge sources;
      
      c) knowledge sources based on concept specification languages;
      
      d) statistical knowledge sources;
      
      or e) a combination of knowledge sources a)-d).
  - 13. The method of claim 11 wherein said data models are selected from one of:
    - a) linguistic data models;
      
      b) logical data models;
      
      c) statistical data models;
      
      or d) a combination of data models a)-c).
  - 14. The method of claim 11 wherein said instructions are selected from one of:
    - a) whether successful matches of the concept against text are “
      
      visible”
      
      in annotated output of the matched text;
      
      b) the number of matches of a concept required in a document for said document to be returned;
      
      c) the name of the concept (that is, the concept name) that is being generated;
      
      d) the name of the file into which that concept is written;
      
      e) whether or not said file is encrypted;
      
      f) a combination of instructions a)-e).
  - 15. The method of claim 11 wherein a UcD is one of three types:
    - a) a basic UcD is a data structure in template form that is used to define types b) and c);
      
      b) an unpopulated UcD, which is a version of a), specifies the knowledge sources, data models, and instructions used in a knowledge-source based UcD (or one of its subtypes such as a text-based UcD) or a data-model based UcD (or one of its subtypes);
      
      c) a populated UcD, which is a version of b) with filled-in information about particular knowledge sources, data models, and instructions used in a particular instance of knowledge-source based UcD (or one of its subtypes) or a data-model based UcD (or one of its subtypes), that is, it is “
      
      filled out”
      
      with information during the generation of an actual concept.
  - 16. The method of claim 15 wherein said UcDs of three types (basic, unpopulated, populated) are organized hierarchically into a graph of UcDs wherein:
    - a) the top level of said graph is occupied by said basic UcD;
      
      b) the next level is occupied by said unpopulated UcDs including, but not limited to, said knowledge-source based UcD and data-model based UcDs;
      
      c) inherited information is optionally passed down from said basic UcD at said top level to said unpopulated UcDs at said next level;
      
      d) the next one or more levels are occupied by further unpopulated UcDs including, but not limited to, subtypes of said knowledge-source based UcD (such as a text-based UcD) or subtypes of said data-model based UcD (such as the logical-based UcD);
      
      e) inherited information is optionally passed down from said unpopulated UcDs at the higher level to said unpopulated UcDs at said next one or more levels, and further optionally passed within said one of more levels;
      
      f) the next level is occupied by said populated UcDs, wherein said UcDs are populated by i) one or more particular knowledge sources and instructions, supplied by the user, and ii) a generated concept, supplied by said concept generation method, g) said graph is optionally stored in a concept database.
  - 17. The method of claim 1 wherein said generating step comprises:
    - a) inputting of text fragments wherein a user is prompted for one or more text fragments;
      
      b) splitting fragments into words;
      
      c) manually selecting relevant words in the text fragments (default selection is available);
      
      d) manually adding synonyms, hypernyms, and hyponyms for any selected relevant word (default selections of key words, synonyms, and hypernyms is available);
      
      e) matching of concepts wherein i) a predefined set of concepts from the user are run over the fragments and all matches are returned, ii) when matching, the part of speech of individual words is determined by standard concept processing engine algorithms, and iii) the resulting matches are known as a “
      
      concept matches”
      
      ;
      
      f) removing certain concept matches, said removal depending on i) what words have been marked as “
      
      relevant”
      
      , ii) the interpretation placed on “
      
      relevant”
      
      by the user (the algorithm may optionally do one or both steps automatically), iii) wherein using the interpretation of “
      
      relevant”
      
      selected, the algorithm removes certain concept matches;
      
      g) building concept chains (tiling) from the concept matches kept from the previous step, where a “
      
      chain”
      
      is a sequence of concept matches;
      
      h) ranking chains;
      
      i) writing out chains as a concept; and
      
      j) outputting the concept into a file with certain instructions attached;
      
      i) naming the concept produced when chains are written out, ii) naming the file for storing said concept, iii) selecting whether said concept is visible or hidden for matching purposes, and iv) selecting whether said file is encrypted or not.
  - 18. The method of claim 17 wherein a User concept Description (UcD) is used to generate a concept.
  - 19. The method of claim 1 wherein said concept wizard is used to navigate a user through the method of generating a concept, said concept wizard:
    - a) providing users with instructions on entering data for the generation of a concept, according to the knowledge sources, data model, and other generation instructions used;
      
      b) different concept wizards are used, depending on the UcD selected;
      
      c) Input from the abstract user interface is taken through the concept wizard is passed to the concept generator for the creation of actual concepts;
      
      d) Input from the concept generator taken into the concept wizard includes information about choices of knowledge sources and data models for generation, and instructions governing generation.
  - 20. The method of claim 21 wherein said concept wizard interacts with a hierarchically organized graph of UcDs optionally stored in a concept database, wherein:
    - a) said concept wizard is invoked;
      
      b) said concept wizard calls upon the unpopulated UcDs in said UcD graph;
      
      c) said concept wizard displays to the user all the knowledge-source based and data-model based concept generation options, extracted from said unpopulated UcDs;
      
      d) said user inputs into said concept wizard his or her choice of concept generation by selecting a particular knowledge-source or data-model as the basis for generation;
      
      e) the unpopulated UcD corresponding to said user'"'"'s choice is accessed from the UcD graph;
      
      f) said concept wizard displays to the user the concept generation options for that knowledge-source or data-model based UcD;
      
      g) The user inputs generation choices of particular knowledge-sources and instructions;
      
      h) The particular semi-populated UcD is then passed to the concept generator;
      
      i) The concept generator generates a concept as part of producing a populated UcD which is. i) stored in the concept database, and ii) also placed in the UcD graph which is optionally stored in the concept database. g) The concept wizard then displays to the user the generated concept for that populated UcD plus optionally all of the user'"'"'s concept generation options that led to the generation of that particular concept.
  - 21. The method of claim 1 further comprising managing said concepts.
  - 22. The method of claim 21 wherein a User concept Group (UcG) is used to group and name a set of concepts, said UcG comprising:
    - a) a named concept that refers to named groups of concepts or Patterns, or other groups;
      
      b) said UcGs can be extracted from any set of concepts.
  - 23. The method of claim 21 wherein a concept database is used to store concepts, said database:
    - a) keeps an up-to-date set of CSL files;
      
      b) keeps a record of what CSL files correspond to what UcDs and UcGs; and
      
      c) guarantees consistency of stored UcDs and UcGs (such that said UcDs and UcGs in said database can be compiled).
  - 24. The method of claim 21 wherein managing said concepts is performed by a concept manager that comprises a concept database administrator and a concept editor.
  - 25. The method of claim 24 wherein said concept database administrator a) is responsible for loading, storing, and managing uncompiled and compiled concepts, UcDs and UcGs in the concept database;
    - b) is responsible for loading, storing, and managing compiled concepts ready for annotation and for generation;
      
      c) is responsible for managing a UcD graph;
      
      d) allows users to view relationships among concepts, UcDs, and UcGs in the concept database;
      
      e) allows users to search for concepts, UcDs, and UcGs;
      
      f) allows users to search for the presence of concepts in UcDs and UcGs;
      
      g) allows users to search for dependencies of UcDs and UcGs on concepts;
      
      h) makes sure the concept database always contains a set of concepts, UcDs, and UcG that are logically consistent and consistent such that said sets in can be compiled;
      
      i) keeps CSL files up to date with the changing definitions of concepts, UcDs, and UcGs;
      
      j) checks the integrity of concepts, UcDs, and UcGs (such that if A depends on B, then B can not be deleted);
      
      k) handles dependencies within and between concepts, UcDs, and UcGs;
      
      l) allows functions performed by concept editor to add, remove, and modify concepts, UcDs, and UcGs in the Database without fear of breaking other concepts, UcDs, or UcGs in the same database.
  - 26. The method of claim 24 wherein said concept editor a) allows users to view relationships among concepts, UcDs, and UcGs in the concept database;
    - b) allows users to search for concepts, UcDs, and UcGs;
      
      c) allows users to search for the presence of concepts in UcDs and UcGs;
      
      d) allows users to search for dependencies of UcDs and UcGs on concepts;
      
      e) allows users to add, remove, and modify all types of concept (if users have appropriate permissions);
      
      f) allows users to add, remove, and modify all types of UcD except Basic UcDs;
      
      g) pre-sets permissions so that only certain privileged users can edit unpopulated UcDs;
      
      h) allows users to users save a UcD under a different name, and can also change any other properties they like;
      
      i) allows users to add, remove, and modify User concept Groups (UcGs);
      
      j) allows users to save a UcG under a different name;
      
      k) allows users to change a concept Group name, description, and any other properties they like in UcGs;
      
      l) allows users to add, remove, and modify user-defined hierarchies.

27. A method for defining and generating a set of concepts and identifying said concepts in text, comprising:
- a) identifying linguistic entities in the text of documents and other text-forms;
  
  b) annotating said identified linguistic entities in a text markup language to produce linguistically annotated documents and other text-forms;
  
  c) storing said linguistically annotated documents and other text-forms;
  
  d) defining concepts that also makes use of patterns wherein;
  
  i) each of said concepts comprises a pattern;
  
  ii) each of said patterns comprising one of the following;
  
  1) a description sufficiently constrained to be matchable to zero or more extents;
  
  each of said extents comprising a set of zero or more items wherein each of said items is an instance of a linguistic entity, each of said instances of said linguistic entity is identified in a) text, or b) a knowledge resource;
  
  or c) both a) and b); and
  
  said pattern is matchable to zero or more of said extents corresponding to said description;
  
  or 2) an operator and a list of zero or more arguments wherein each of said arguments is a further pattern; and
  
  said pattern comprising said operator and said list of arguments is matchable to extents that are the result of applying said operator to further extents that are matchable by said arguments;
  
  or 3) a reference to a further concept comprising a further pattern; and
  
  said pattern comprising said reference to said further concept is matchable to extents that are matchable by said further pattern; and
  
  iii) any said further pattern is a pattern; and
  
  e) generating said concepts from text of documents and other text-forms, and other sources of knowledge;
  
  f) managing said concepts, both generated and non-generated;
  
  g) identifying concepts using linguistic information, where said concepts occur in one of;
  
  i) said text of documents and other text-forms in which linguistic entities have been identified in step a);
  
  or ii) said linguistically annotated documents and other text-forms of step b);
  
  or iii) stored linguistically annotated documents and other text-forms of step c);
  
  h) annotation of said identified concepts in said text markup language to produce conceptually annotated documents and other text-forms;
  
  i) storage of said conceptually annotated documents and other text-forms.
- View Dependent Claims (28, 29, 30, 31)
- - 28. A system for implementing said method according to claim 27 consisting of one of:
    - a) a client server configuration comprising i) a server, wherein said server comprises 1) a communications interface to one or more clients over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising a module or submodules for a concept processing engine, and 5) one or more output devices; and
      
      ii) one or more clients, wherein each client comprises 1) a communications interface to a server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a concept processing engine, and 5) one or more output devices;
      
      or b) a client server farm configuration comprising i) a front end server which 1) optionally contains modules for concept or concept processing and may itself act in the capacity of a client when it accesses remote databases located on a database server, 2) receives queries over a network or other communication connection from one or more clients, 3) passes said queries over said network or other communication connection to the back end servers in the server farm which 4) processes said queries, and 5) sends said queries to said front end server, which sends said queries on to said clients;
      
      ii) a server farm of one or more back end servers, where each back end server comprises 1) a communications interface to the front end server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a concept processing engine, and 5) one or more output devices, and 6) receives queries from clients via the front end server over said network or other communication connection;
      
      7) does substantially all the processing necessary to formulate responses to said queries (though said front end server may also do some concept processing), and provides said responses to said front end server, which passes said responses on to said clients, 8) said back end server may itself act in the capacity of a client when said back end server accesses remote databases located on a database server; and
      
      iii) one or more clients, wherein each client comprises 1) a communications interface to the front end server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a concept processing engine, and 5) one or more output devices.
  - 29. The system according to claim 28 wherein the concept processor takes as input text in documents and other text-forms in the form of a signal from one or more input devices to a user interface, and carries out predetermined processes (including, but not limited to, processes for information retrieval and information extraction) to produce a) a collection of text in documents and other text-forms, which are output from the user interface in the form of a signal to one or more output devices, and b) concepts (and, possibly, UcDs, UcGs, and hierarchies of those three entities), which are stored in a concept database.
  - 30. The system according to claim 29 wherein predetermined processes (including, but not limited to, processes for information retrieval and information extraction), accessed by said user interface, comprise the following main processes:
    - synonym processor, annotator, concept generation (including the concept wizard, example maker, and concept generator), concept database, concept manager, and CSL parser.
  - 31. The system according to claim 30 wherein said concept generation comprise:
    - a) concept wizard;
      
      b) example maker;
      
      c) concept generator;
      
      d) knowledge repositories as input including, but not limited to i) text-based knowledge sources (text documents or text fragments);
      
      ii) linguistic knowledge sources including vocabulary specifications;
      
      lexical relations (synonyms, hypernyms, hyponyms), syntactic categories, semantic entities (one or more tags for names of people, names of places, measures, dates;
      
      document level tags such as #subject, #from, #to, #date);
      
      iii) knowledge sources based on concept specification languages (concepts, operators, patterns, grammar specifications in terms of concepts, imported concepts, one or more internal database concepts to be used for generation); and
      
      iv) statistical knowledge sources frequencies of words (derived from text documents, text fragments, vocabulary items, and other data sources) and frequencies of tags (such as syntactic tags like noun phrase, document structure tags from HTML, and semantic tags from XML);
      
      e) knowledge repositories as output comprising generated concepts.

32. A method for defining and generating a set of Concepts and identifying said Concepts in text, comprising:
- a) defining said set of Concepts wherein;
  
  i) each of said Concepts comprises a Pattern;
  
  ii) each of said Patterns comprising one of the following;
  
  1) a Basic Pattern comprising a description sufficiently constrained to be matchable to zero or more extents;
  
  each of said extents comprising a set of zero or more items wherein each of said items is an instance of a linguistic entity;
  
  each of said instances of said linguistic entity is identified in b) text, or b) a knowledge resource;
  
  or c) both a) and b); and
  
  said Basic Pattern is matchable to zero or more of said extents corresponding to said description;
  
  or 2) an Operator Pattern comprising an Operator and a list of zero or more Arguments wherein each of said Arguments is a further Pattern; and
  
  said Operator Pattern is matchable to extents that are the result of applying said Operator to further extents that are matchable by said Arguments;
  
  or 3) a Concept Call comprising a reference to a further Concept comprising a further Pattern; and
  
  said Concept Call is matchable to extents that are matchable by said further Pattern; and
  
  iii) any said further Pattern is a Pattern; and
  
  b) generating said Concepts from text or one or more sources of knowledge; and
  
  c) identifying said Concepts in text.
- View Dependent Claims (33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76)
- - 33. The method of claim 32 wherein each said linguistic entity comprises:
    - a) a morpheme;
      
      or b) a word or phrase;
      
      or c) a lexically-related term;
      
      or d) a constituent or subconstituent;
      
      or e) an expression in a linguistic notation representing a phonological, morphological, syntactic, semantic, or pragmatic-level description of text;
      
      or f) any combination of one or more linguistic entities.
  - 34. The method of claim 32 wherein said linguistic entity is identified in text and a record is made that said linguistic entity starts in one position within said text and ends in a second position.
  - 35. The method of claim 32 wherein each said Operator may comprise:
    - a) a zero-argument Operator that expresses information including;
      
      i) match information, or ii) syntax information, or iii) semantic information;
      
      or b) a one-argument Operator that expresses information including;
      
      i) match information, or ii) tense, or iii) syntactic categories, or iv) Boolean relations, or v) lexical relations, or vi) semantic categories;
      
      or c) a two-argument Operator that expresses information including;
      
      i) relationships within and across sentences, or ii) syntactic relationships, or iii) Boolean relations;
      
      or iv) semantic relationships.
  - 36. The method of claim 35 wherein one of said two-argument Operators comprises NonImmediately_Dominates(X,Y) wherein:
    - a) X matches any extent;
      
      b) Y matches any extent; and
      
      c) the result is the extent matched by Y if all the linguistic entities of Y'"'"'s extent are subconstituents of all linguistic entities of X'"'"'s extent.
  - 37. The method of claim 35 wherein one of said two-argument Operators comprises NonImmediately_Dominates(X,Y) when it is is “
    - wide-matched”
      
      , wherein a) X matches any extent;
      
      b) Y matches any extent; and
      
      c) the result is said extent matched by X if all the linguistic entities of Y'"'"'s extent are subconstituents of all linguistic entities of X'"'"'s extent.
  - 38. The method of claim 35 wherein one of said two-argument Operators comprises NonImmediately_Precedes(X,Y) wherein:
    - a) X matches any extent;
      
      b) Y matches any extent, and c) the result is an extent that covers the extent matched by Y and an extent matched by X if the extent matched by X precedes the extent matched by Y.
  - 39. The method of claim 32 wherein each of said Patterns may further comprise a) a Parameter that is matchable to the extents matched by any Pattern that is bound to said Parameter, and wherein b) any Pattern may be bound to a Parameter.
  - 40. The method of claim 39 wherein said Patterns further comprise a Concept Call comprising a) a reference to a further Concept comprising a further Pattern and b) a list of zero or more Arguments wherein each of said Arguments comprise a further Pattern;
    - and said Concept Call is matchable to extents that are matchable by said further Pattern in said further Concept, where any Parameters in said further Concept are bound to said further Patterns in said list of zero or more Arguments.
  - 41. The method of claim 32 wherein each of said Concepts may further comprise a) a name for said Concept and b) a set of one or more Directives selected from the following:
    - i) whether successful matches of said Concept against text are “
      
      visible”
      
      ;
      
      ii) the number of matches of a Concept required in a document for said document to be returned;
      
      iii) the name for said Concept that is being generated;
      
      vi) the name of a file into which that Concept is written;
      
      v) whether or not said file is encrypted.
  - 42. The method of claim 32 wherein a User Concept Description (UCD) is used to generate a Concept, specifying ways in which Concepts can be generated from different types of knowledge (knowledge sources) by way of different data models, governed by various Directives, said UCD comprising:
    - a) one or more knowledge sources that provide raw content used to generate Concepts, b) one or more data models used to combine said knowledge sources used to generate Concepts, and c) one or more Directives governing said generation of said Concepts.
  - 43. The method of claim 32 wherein said knowledge sources are selected from one of:
    - a) text-based knowledge sources;
      
      b) linguistic knowledge sources;
      
      c) CSL-based knowledge sources;
      
      d) statistical knowledge sources;
      
      or e) a combination of knowledge sources a)-d).
  - 44. The method of claim 43 wherein said text-based knowledge sources are selected from one of:
    - a) one or more vocabulary items;
      
      b) one or more text fragments;
      
      c) one or more text documents;
      
      or d) some combination of a)-c).
  - 45. The method of claim 43 wherein said linguistic knowledge sources are selected from one or more of:
    - a) one or more lexical relations comprising i) one or more synonyms;
      
      ii) one or more superordinate terms (hypernyms); and
      
      iii) one or more subordinate terms (hyponyms);
      
      b) one or more syntactic categories;
      
      c) one or more semantic entities comprising i) one or more tags for names of people, names of places, names of companies and products, job titles, monetary expressions, percentages, measures, numbers, dates, time of day, and time elapsed/period of time during which something lasts;
      
      ii) one or more document level tags such as #subject, #from, #to, #date;
      
      d) some combination of a)-c).
  - 46. The method of claim 43 wherein said CSL-based knowledge sources are selected from one of:
    - a) one or more Concepts;
      
      b) one or more Concept Calls;
      
      c) one or more Operators;
      
      d) one or more Patterns;
      
      e) grammar specifications (in terms of Concepts);
      
      f) some combination of a)-e).
  - 47. The method of claim 43 wherein said statistical knowledge sources are selected from one of:
    - a) frequencies of words derived from text documents, text fragments, vocabulary items, and other data sources;
      
      b) frequencies of tags such as syntactic tags like noun phrase, document structure tags from HTML, and semantic tags from XML;
      
      c) some combination of a) and b).
  - 48. T he method of claim 42 wherein a knowledge source-based UCD is a UCD in which:
    - a) options about knowledge sources are presented to users before options about data models or Directives;
      
      b) the selection of certain knowledge sources prioritizes the subsequent choices of data models and Directives presented to users (text fragments are most closely associated with the linguistic data model, documents with the statistical data model, and CSL Operators with the logical data model).
  - 49. The method of claim 46 wherein a knowledge source-based UCD has subtypes that include, but are not limited to, a vocabulary-based UCD, text-based UCD, document-based UCD, Operator-based UCD, imported Concept-based UCD, and internal Concept-based UCD.
  - 50. The method of claim 42 wherein said data models are selected from one of:
    - a) linguistic data models;
      
      b) logical data models;
      
      c) statistical data models;
      
      or d) a combination of data models a)-c).
  - 51. The method of claim 50 wherein said linguistic data model comprises:
    - a) identification of linguistic entities in the text of documents and other text-forms;
      
      b) annotation of said identified linguistic entities in a text markup language to produce linguistically annotated documents and other text-forms;
      
      c) storage of said linguistically annotated documents and other text-forms;
      
      d) identification of concepts using linguistic information, where said concepts are represented in a concept specification language and said concepts occur in one of;
      
      i) said text of documents and other text-forms in which linguistic entities have been identified in step a);
      
      or ii) said linguistically annotated documents and other text-forms of step b);
      
      or iii) stored linguistically annotated documents and other text-forms of step c);
      
      e) annotation of said identified concepts in said text markup language to produce conceptually annotated documents and other text-forms;
      
      f) storage of said conceptually annotated documents and other text-forms;
      
      g) defining and learning concept representations of said concept specification language;
      
      h) checking user-defined descriptions of concepts represented in said concept specification language; and
      
      i) retrieval by matching said user-defined descriptions of concepts against said conceptually annotated documents and other text-forms.
  - 52. The method of claim 50 wherein said logical data model includes, but is not limited to, the Boolean Operators AND, OR, NOT, and ANDNOT.
  - 53. The method of claim 50 wherein said statistical data model includes, but is not limited to, support vector machines.
  - 54. The method of claim 42 wherein a data model-based UCD is a UCD in which:
    - a) options about data models are presented to users before options about knowledge sources or Directives;
      
      b) the selection of certain data models prioritizes the subsequent choices of knowledge sources and Directives presented to users (the linguistic data model is most closely associated with text fragments, the statistical data model with documents, and the logical data model with CSL Operators.
  - 55. The method of claim 42 wherein said Directives are selected from one of:
    - a) whether successful matches of the Concept against text are “
      
      visible”
      
      in annotated output of the matched text;
      
      b) the number of matches of a Concept required in a document for said document to be returned;
      
      c) the name of the Concept (that is, the Concept name) that is being generated;
      
      d) the name of the file into which that Concept is written;
      
      e) whether or not said file is encrypted;
      
      f) a combination of Directives a)-e).
  - 56. The method of claim 42 wherein a UCD is one of three types:
    - a) a basic UCD is a data structure in template form that is used to define types b) and c);
      
      b) an unpopulated UCD, which is a version of a), specifies the knowledge sources, data models, and Directives used in a knowledge-source based UCD (or one of its subtypes such as a text-based UCD) or a data-model based UCD (or one of its subtypes);
      
      c) a populated UCD, which is a version of b) with filled-in information about particular knowledge sources, data models, and Directives used in a particular instance of knowledge-source based UCD (or one of its subtypes) or a data-model based UCD (or one of its subtypes), that is, it is “
      
      filled out”
      
      with information during the generation of an actual Concept.
  - 57. The method of claim 56 wherein said UCDs of three types (basic, unpopulated, populated) are organized hierarchically into a graph of UCDs wherein:
    - a) the top level of said graph is occupied by said basic UCD;
      
      b) the next level is occupied by said unpopulated UCDs including, but not limited to, said knowledge-source based UCD and data-model based UCDs;
      
      c) inherited information is optionally passed down from said basic UCD at said top level to said unpopulated UCDs at said next level;
      
      d) the next one or more levels are occupied by further unpopulated UCDs including, but not limited to, subtypes of said knowledge-source based UCD (such as a text-based UCD) or subtypes of said data-model based UCD (such as the logical-based UCD);
      
      e) inherited information is optionally passed down from said unpopulated UCDs at the higher level to said unpopulated UCDs at said next one or more levels, and further optionally passed within said one of more levels;
      
      f) the next level is occupied by said populated UCDs, wherein said UCDs are populated by i) one or more particular knowledge sources and Directives, supplied by the user, and ii) a generated Concept, supplied by said Concept generation method, g) said graph is optionally stored in a Concept database.
  - 58. The method of claim 56 wherein an unpopulated text-based UCD comprises:
    - a) holding input text fragments, b) holding selected relevant words, c) holding synonyms, hypernyms, and hyponyms for said selected relevant words, d) holding Directives for Concept generation, and e) holding generated Concept that has been written to a file.
  - 59. The method of claim 32 wherein said generating step comprises:
    - a) inputting of text fragments wherein a user is prompted for one or more text fragments;
      
      b) splitting fragments into words;
      
      c) manually selecting relevant words in the text fragments (default selection is available);
      
      d) manually adding synonyms, hypernyms, and hyponyms for any selected relevant word (default selections of key words, synonyms, and hypernyms is available);
      
      e) matching of Concepts wherein i) a predefined set of Concepts from the user are run over the fragments and all matches are returned, ii) when matching, the part of speech of individual words is determined by standard Concept processing engine algorithms, and iii) the resulting matches are known as a “
      
      Concept matches”
      
      ;
      
      f) removing certain Concept matches, said removal depending on i) what words have been marked as “
      
      relevant”
      
      , ii) the interpretation placed on “
      
      relevant”
      
      by the user (the algorithm may optionally do one or both steps automatically), iii) wherein using the interpretation of “
      
      relevant”
      
      selected, the algorithm removes certain Concept matches;
      
      g) building Concept chains (tiling) from the Concept matches kept from the previous step, where a “
      
      chain”
      
      is a sequence of Concept matches;
      
      h) ranking chains;
      
      i) writing out chains as a Concept; and
      
      j) outputting the Concept into a file with certain Directives attached;
      
      i) naming the Concept produced when chains are written out, ii) naming the CSL file for said Concept, iii) selecting whether said Concept is visible or hidden for matching purposes, and iv) selecting whether said CSL file is encrypted or not.
  - 60. The method of claim 59 wherein what is “
    - relevant”
      
      when removing certain Concept matches is selected from one of four interpretations;
      
      a) a Concept match is kept if all of the Arguments of its match are marked as relevant, e.g., the match of the Concept noun verb against dog eats is kept only if both dog and eats are marked as relevant;
      
      b) a Concept match is kept if one or more of the Arguments of its match are marked as relevant, e.g., the match of the Concept noun verb against dog eats is kept only if one or more of the Arguments—
      
      dog, eats, or dog and eats—
      
      are marked as relevant;
      
      c) a Concept match is kept if all the words marked as relevant fall inside the extent of the match (up to and including the boundaries of that extent);
      
      d) a Concept match is kept if one or more of the words marked as relevant fall inside the extent of the match (up to and including the boundaries of that extent).
  - 61. The method of claim 59 wherein:
    - a) a “
      
      chain”
      
      is a sequence of Concept matches such that no two matches in the chain overlap (i.e., a chain is a set of adjacent Concept matches (tiles) with no overlapping extents);
      
      b) no match can be added to a particular chain without violating a) (i.e., the chains are of maximum length);
      
      c) no word can belong to two different Concepts in the same chain;
      
      d) the tiler produces a set of chains as few in number as one through to as many in number as there are different paths between words.
  - 62. The method of claim 59 wherein:
    - a) a “
      
      chain”
      
      is a sequence of Concept matches such that a set of adjacent Concept matches (tiles) with overlapping extents is allowed;
      
      b) one word can belong to two different Concepts in the same chain;
      
      c) the tiler takes all connections between words, preferring to find shorter spans rather than larger ones, and produces a single optimal chain.
  - 63. The method of claim 59 wherein, when a “
    - chain”
      
      is a sequence of Concept matches such that no two matches in the chain overlap, every chain from the tiling (Concept chain building) step is ranked and only the chains with maximum rank are kept, where the rank of a chain is calculated as follows;
      
      a) “
      
      match Coverage”
      
      is the number of words in the match of that whole chain that overlap extent between the first and last relevant words;
      
      b) “
      
      match Context”
      
      is the number of words in the match that are outside of the extent between the first and last relevant words;
      
      c) “
      
      match Rank”
      
      is “
      
      Match Coverage”
      
      minus “
      
      Match Context”
      
      ; and
      
      d) the final rank is the sum of all Match Ranks for a given chain minus the length of the chain (wherein subtracting the chain length is intended to boost ranking of shorter chains, which are likely the ones that consists of longer/more meaningful matches).
  - 64. The method of claim 59 wherein chains are written out as a Concept as follows:
    - a) take the first chain;
      
      b) take the first Concept match;
      
      c) look up said match in a knowledge base of Concepts to get Concept;
      
      d) write out said Concept;
      
      e) if there is another match in said chain, write out an AND Operator and go to step c) with the next Concept match;
      
      f) if there are no more matches and if there is another chain, then write out an OR Operator and go to step b) with the next chain;
      
      else exit with completed chain (the defined Concept covers the text fragments).
  - 65. The method of claim 59 wherein:
    - a) inputting of text fragments is replaced by inputting of positive and negative text fragments (the user is prompted for one or more each of these); and
      
      b) selecting relevant words is replaced by selecting relevant words in said positive and negative text fragments (the relevant words in positive text fragments are words that should match the generated Concept, while the relevant words in negative text fragments are words that should not match the generated Concept).
  - 66. The method of claim 59 wherein a User Concept Description (UCD) is used to generate a Concept.
  - 67. The method of claim 32 wherein said Concept wizard is used to navigate a user through the method of generating a Concept, said Concept wizard:
    - a) providing users with instructions on entering data for the generation of a Concept, according to the knowledge sources, data model, and other generation Directives used;
      
      b) different Concept wizards are used, depending on the UCD selected;
      
      c) Input from the abstract user interface is taken through the Concept wizard is passed to the Concept generator for the creation of actual Concepts;
      
      d) Input from the Concept generator taken into the Concept wizard includes information about choices of knowledge sources and data models for generation, and Directives governing generation.
  - 68. The method of claim 67 wherein said Concept wizard interacts with a hierarchically organized graph of UCDs optionally stored in a Concept database, wherein:
    - a) said Concept wizard is invoked;
      
      b) said Concept wizard calls upon the unpopulated UCDs in said UCD graph;
      
      c) said Concept wizard displays to the user all the knowledge-source based and data-model based Concept generation options, extracted from said unpopulated UCDs;
      
      d) said user inputs into said Concept wizard his or her choice of Concept generation by selecting a particular knowledge-source or data-model as the basis for generation;
      
      e) the unpopulated UCD corresponding to said user'"'"'s choice is accessed from the UCD graph;
      
      f) said Concept wizard displays to the user the Concept generation options for that knowledge-source or data-model based UCD;
      
      g) The user inputs generation choices of particular knowledge-sources and Directives;
      
      h) The particular semi-populated UCD is then passed to the Concept generator;
      
      i) The Concept generator generates a Concept as part of producing a populated UCD which is. i) stored in the Concept database, and ii) also placed in the UCD graph which is optionally stored in the Concept database. g) The Concept wizard then displays to the user the generated Concept for that populated UCD plus optionally all of the user'"'"'s Concept generation options that led to the generation of that particular Concept.
  - 69. The method of claim 32 wherein said generating step comprises:
    - a) inputting of text fragments wherein a user is prompted for one or more text fragments;
      
      b) splitting fragments into words;
      
      c) manually selecting relevant words in the text fragments (default selection is available);
      
      d) manually adding synonyms, hypernyms, and hyponyms for any selected relevant word (default selections of key words, synonyms, and hypernyms are available);
      
      e) inputting names of Concepts that need to be combined into a new Concept;
      
      f) selecting Operators from a set of available Operators including, but not limited to;
      
      i) OR, AND, and ANDNOT, ii) Immediately Precedes and Precedes, iii) Precedes within less than N words and Precedes outside of (greater than) N words, iv) Immediately Dominates and Dominates, and v) Related and Cause; and
      
      g) performing an integrity check on every candidate comprising an Operator and zero or more Arguments;
      
      h) converting into a chain every acceptable candidate comprising an Operator and zero or more Arguments;
      
      i) writing out chains as a Concept; and
      
      j) outputting the Concept into a file with certain Directives attached;
      
      i) naming the Concept produced when chains are written out, ii) naming the CSL file for said Concept, iii) selecting whether said Concept is visible or hidden for matching purposes, and iv) selecting whether said CSL file is encrypted or not.
  - 70. The method of claim 69 wherein a User Concept Description (UCD) is used to generate a Concept.
  - 71. The method of claim 32 further comprising managing said Concepts.
  - 73. The method of claim 71 wherein a Concept database is used to store Concepts, said database:
    - a) keeps an up-to-date set of CSL files;
      
      b) keeps a record of what CSL files correspond to what UCDs and UCGs; and
      
      c) guarantees consistency of stored UCDs and UCGs (such that said UCDs and UCGs in said database can be compiled).
  - 74. The method of claim 71 wherein managing said Concepts is performed by a Concept manager that comprises a Concept database administrator and a Concept editor.
  - 75. The method of claim 74 wherein said Concept database administrator a) is responsible for loading, storing, and managing uncompiled and compiled Concepts, UCDs and UCGs in the Concept database;
    - b) is responsible for loading, storing, and managing compiled Concepts ready for annotation and for generation;
      
      c) is responsible for managing a UCD graph;
      
      d) allows users to view relationships among Concepts, UCDs, and UCGs in the Concept database;
      
      e) allows users to search for Concepts, UCDs, and UCGs;
      
      f) allows users to search for the presence of Concepts in UCDs and UCGs;
      
      g) allows users to search for dependencies of UCDs and UCGs on Concepts;
      
      h) makes sure the Concept database always contains a set of Concepts, UCDs, and UCG that are logically consistent and consistent such that said sets in can be compiled;
      
      i) keeps CSL files up to date with the changing definitions of Concepts, UCDs, and UCGs;
      
      j) checks the integrity of Concepts, UCDs, and UCGs (such that if A depends on B, then B can not be deleted);
      
      k) handles dependencies within and between Concepts, UCDs, and UCGs;
      
      l) allows functions performed by Concept editor to add, remove, and modify Concepts, UCDs, and UCGs in the Database without fear of breaking other Concepts, UCDs, or UCGs in the same database.
  - 76. The method of claim 74 wherein said Concept editor a) allows users to view relationships among Concepts, UCDs, and UCGs in the Concept database;
    - b) allows users to search for Concepts, UCDs, and UCGs;
      
      c) allows users to search for the presence of Concepts in UCDs and UCGs;
      
      d) allows users to search for dependencies of UCDs and UCGs on Concepts;
      
      e) allows users to add, remove, and modify all types of Concept (if users have appropriate permissions);
      
      f) allows users to add, remove, and modify all types of UCD except Basic UCDs;
      
      g) pre-sets permissions so that only certain privileged users can edit unpopulated UCDs;
      
      h) allows users to users save a UCD under a different name, and can also change any other properties they like;
      
      i) allows users to add, remove, and modify User Concept Groups (UCGs);
      
      j) allows users to save a UCG under a different name;
      
      k) allows users to change a Concept Group name, description, and any other properties they like in UCGs;
      
      l) allows users to add, remove, and modify user-defined hierarchies.

72. The method of claim 72 wherein a User Concept Group (UCG) is used to group and name a set of Concepts, said UCG comprising:
- a) a named Concept that refers to named groups of Concepts or Patterns, or other groups;
  
  b) said UCGs can be extracted from any set of Concepts.

77. A method for defining and generating a set of concepts and identifying said concepts in text, comprising:
- a) identifying linguistic entities in the text of documents and other text-forms;
  
  b) annotating said identified linguistic entities in a text markup language to produce linguistically annotated documents and other text-forms;
  
  c) storing said linguistically annotated documents and other text-forms;
  
  d) defining Concepts that also makes use of Patterns wherein;
  
  i) each of said Concepts comprises a Pattern;
  
  ii) each of said Patterns comprising one of the following;
  
  1) a Basic Pattern comprising a description sufficiently constrained to be matchable to zero or more extents;
  
  each of said extents comprising a set of zero or more items wherein each of said items is an instance of a linguistic entity;
  
  each of said instances of said linguistic entity is identified in c) text, or b) a knowledge resource;
  
  or c) both a) and b); and
  
  said Basic Pattern is matchable to zero or more of said extents corresponding to said description;
  
  or 2) an Operator Pattern comprising an Operator and a list of zero or more Arguments wherein each of said Arguments is a further Pattern; and
  
  said Operator Pattern is matchable to extents that are the result of applying said Operator to further extents that are matchable by said Arguments;
  
  or 3) a Concept Call comprising a reference to a further Concept comprising a further Pattern; and
  
  said Concept Call is matchable to extents that are matchable by said further Pattern; and
  
  iii) any said further Pattern is a Pattern; and
  
  e) generating said Concepts from text of documents and other text-forms, and other sources of knowledge;
  
  f) managing said Concepts, both generated and non-generated;
  
  g) identifying Concepts using linguistic information, where said Concepts occur in one of;
  
  i) said text of documents and other text-forms in which linguistic entities have been identified in step a);
  
  or iv) said linguistically annotated documents and other text-forms of step b);
  
  or v) stored linguistically annotated documents and other text-forms of step c);
  
  h) annotation of said identified Concepts in said text markup language to produce conceptually annotated documents and other text-forms;
  
  i) storage of said conceptually annotated documents and other text-forms.
- View Dependent Claims (78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112)
- - 78. A system for implementing said method according to claim 77 consisting of one of:
    - a) a client server configuration comprising i) a server, wherein said server comprises 1) a communications interface to one or more clients over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising a module or submodules for a Concept processing engine, and 5) one or more output devices; and
      
      ii) one or more clients, wherein each client comprises 1) a communications interface to a server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a Concept processing engine, and 5) one or more output devices;
      
      or b) a client server farm configuration comprising i) a front end server which 1) optionally contains modules for Concept or concept processing and may itself act in the capacity of a client when it accesses remote databases located on a database server, 2) receives queries over a network or other communication connection from one or more clients, 3) passes said queries over said network or other communication connection to the back end servers in the server farm which 4) processes said queries, and 5) sends said queries to said front end server, which sends said queries on to said clients;
      
      ii) a server farm of one or more back end servers, where each back end server comprises 1) a communications interface to the front end server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a Concept processing engine, and 5) one or more output devices, and 6) receives queries from clients via the front end server over said network or other communication connection;
      
      7) does substantially all the processing necessary to formulate responses to said queries (though said front end server may also do some Concept processing), and provides said responses to said front end server, which passes said responses on to said clients, 8) said back end server may itself act in the capacity of a client when said back end server accesses remote databases located on a database server; and
      
      iii) one or more clients, wherein each client comprises 1) a communications interface to the front end server over a network or other communication connection, 2) one or more central processing units (CPUs), 3) one or more input devices, 4) one or more program and data storage areas comprising one or more submodules for a Concept processing engine, and 5) one or more output devices.
  - 79. The system of claim 78 wherein the Concept processor takes as input text in documents and other text-forms in the form of a signal from one or more input devices to a user interface, and carries out predetermined processes (including, but not limited to, processes for information retrieval and information extraction) to produce a) a collection of text in documents and other text-forms, which are output from the user interface in the form of a signal to one or more output devices, and b) Concepts (and, possibly, UCDs, UCGs, and hierarchies of those three entities), which are stored in a Concept database.
  - 80. The system according to claim 79 wherein predetermined processes (including, but not limited to, processes for information retrieval and information extraction), accessed by said user interface, comprise the following main processes:
    - synonym processor, annotator, Concept generation (including the Concept wizard, example maker, and Concept generator), Concept database, Concept manager, and CSL parser.
  - 81. The system according to claim 80 wherein said abstract user interface is a specification of instructions that is independent of different types of user interface such as command line interfaces, web browsers, and pop-up windows in Microsoft and other operating systems applications, said abstract user interface:
    - a) receives both input and output from the user interface, Concept manager, and Concept wizard, b) sends output to the synonym processor, annotator, and document loader, c) instructions received include, but are not limited to, those for the loading of text documents, the processing of synonyms, the identification of Concepts, the generation of Concepts, and the management of Concepts.
  - 82. The system according to claim 80 wherein said synonym processor a) takes as input a synonym resource, b) tailors the synonyms to the domain in which the Concept processing engine operates, c) produces outputs wherein the pruned synonym resource is used as a knowledge source, d) produces a processed synonym resource that contains the synonyms of the input resource, tailored to the domain in which the Concept processing engine operates, e) said pruned synonym resource is used as a knowledge source for annotation (Concept identification), Concept generation, and CSL parsing.
  - 83. The system according to claim 80 wherein said annotator, accessed by said abstract user interface, uses said document loader which passes text documents from a document database to the annotator, and outputs one or more linguistically or conceptually annotated documents.
  - 84. The system according to claim 83 wherein said annotator takes as input one or more text documents, outputs one or more annotated documents, and is comprised of a linguistic annotator which passes linguistically annotated documents to a conceptual annotator.
  - 85. The system according to claim 84 wherein said linguistically annotated documents, are annotated with a representation in a Text Markup Language.
  - 86. The system according to claim 85 wherein said Text Markup Language (TML) has the syntax of XML, and conversion to and from TML is accomplished with an XML converter.
  - 87. The system according to claim 85 wherein said linguistic annotator, taking as input one or more text documents, and outputting one or more linguistically annotated documents, comprises one or more of the following:
    - a) a preprocessor;
      
      b) a tagger; and
      
      c) a parser.
  - 88. The system according to claim 87 wherein said preprocessor, taking as input one or more text documents or the documents output by any other appropriate linguistic identification process, and producing as output one or more preprocessed documents, comprises means for one or more of the following:
    - a) breaking text into words;
      
      b) marking phrase boundaries;
      
      c) identifying numbers, symbols, and other punctuation;
      
      d) expanding abbreviations; and
      
      e) splitting apart contractions.
  - 89. The system according to claim 87 wherein said tagger takes as input a set of tags, one or more preprocessed documents or the documents output by any other appropriate linguistic identification process and produces as output one or more documents tagged with the appropriate part of speech from a given tagset.
  - 90. The system according to claim 87 wherein said parser takes as input one or more tagged documents or the documents output by any other appropriate linguistic identification process and produces as output one or more parsed documents.
  - 91. The system according to claim 84 wherein said conceptual annotator takes as input one or more linguistically annotated documents, a list of CSL Concepts and Concept Rules for annotation, and optionally data from a synonym resource, and outputs one or more conceptually annotated documents.
  - 92. The system according to claim 84 wherein said input of one or more linguistically annotated documents to said conceptual annotator comprises at least one of the following sources:
    - a) the linguistic annotator directly;
      
      b) storage in some linguistically annotated form such as the representation produced by the final linguistic identification process of the linguistic annotator; and
      
      c) storage in TML followed by conversion from TML to the representation produced by the final linguistic identification process of the linguistic annotator.
  - 93. The system according to claim 84 wherein said conceptually annotated documents are a) annotated with a representation in TML;
    - or b) stored;
      
      or c) both a) and b).
  - 94. The system according to claim 80 wherein said Concept generation comprise:
    - a) Concept wizard;
      
      b) example maker;
      
      c) Concept generator;
      
      d) knowledge repositories as input including, but not limited to i) text-based knowledge sources (text documents or text fragments);
      
      ii) linguistic knowledge sources including vocabulary specifications;
      
      lexical relations (synonyms, hypernyms, hyponyms), syntactic categories, semantic entities (one or more tags for names of people, names of places, measures, dates;
      
      document level tags such as #subject, #from, #to, #date);
      
      iii) CSL-based knowledge sources (Concepts, Concept Calls, Operators, Patterns, grammar specifications in terms of Concepts, imported Concepts, one or more internal database Concepts to be used for generation); and
      
      iv) statistical knowledge sources frequencies of words (derived from text documents, text fragments, vocabulary items, and other data sources) and frequencies of tags (such as syntactic tags like noun_phrase, document structure tags from HTML, and semantic tags from XML);
      
      e) knowledge repositories as output comprising generated Concepts.
  - 95. The system according to claim 94 wherein said Concept wizard has the following properties:
    - a) provides users with instructions on entering data for the generation of a Concept, according to the knowledge sources, data model, and other generation Directives used;
      
      b) different Concept wizards are used, depending on the UCD selected;
      
      c) the Concept wizard receives input from the abstract user interface that includes instructions and text documents;
      
      d) the Concept wizard receives input from the Concept generator that includes information about choices of knowledge sources and data models for generation, and Directives governing generation;
      
      e) output from the Concept wizard is passed to the Concept generator for the creation of actual Concepts.
  - 96. The system according to claim 95 wherein said Concept wizard interacts with a hierarchically organized graph of UCDs optionally stored in a Concept database, wherein:
    - a) said Concept wizard is invoked;
      
      b) said Concept wizard calls upon the unpopulated UCDs in said UCD graph;
      
      c) said Concept wizard displays to the user all the knowledge-source based and data-model based Concept generation options, extracted from said unpopulated UCDs;
      
      d) said user inputs into said Concept wizard his or her choice of Concept generation by selecting a particular knowledge-source or data-model as the basis for generation;
      
      e) the unpopulated UCD corresponding to said user'"'"'s choice is accessed from the UCD graph;
      
      f) said Concept wizard displays to the user the Concept generation options for that knowledge-source or data-model based UCD;
      
      g) The user inputs generation choices of particular knowledge-sources and Directives;
      
      h) The particular semi-populated UCD is then passed to the Concept generator;
      
      i) The Concept generator generates a Concept as part of producing a populated UCD which is. i) stored in the Concept database, and ii) also placed in the UCD graph which is optionally stored in the Concept database. g) The Concept wizard then displays to the user the generated Concept for that populated UCD plus optionally all of the user'"'"'s Concept generation options that led to the generation of that particular Concept.
  - 97. The system according to claim 94 wherein said example maker:
    - a) takes as input a Concept from the Concept generator and generates a list of words and phrases that match that Concept;
      
      b) users can mark the words and phrases in the list as appropriate or inappropriate;
      
      c) said marked-up list is returned to said Concept generator.
  - 98. The system according to claim 94 wherein said Concept generator:
    - a) is accessed by the abstract user interface through the Concept wizard;
      
      b) engages in two-way interaction with the example maker wherein Concepts are passed to the example maker, and lists of word and phrases generated by the example maker, marked as appropriate or inappropriate by a user, are returned to the Concept generator;
      
      c) take as input knowledge repositories including, but not limited to i) documents, text fragments, and other text-forms;
      
      ii) “
      
      highlighted documents and text fragments”
      
      produced by highlighting instances of Concepts in the text of said documents, text fragments, and other text-forms, said highlighted documents and text fragments having been 1) produced on-the-fly or 2) produced earlier and stored either a) as is, or b) converted to TML (to produce “
      
      highlighted documents and text fragments in TML format”
      
      ), stored, and converted from TML for use by the Concept generator;
      
      iii) linguistically annotated documents and text fragments that have been 1) produced on-the-fly or 2) produced earlier and stored either a) as is, or b) converted to TML (to produce “
      
      linguistically annotated documents and text fragments in TML format”
      
      ), stored, and converted from TML for use by the Concept generator;
      
      iv) conceptually annotated documents and text fragments that have been 1) produced on-the-fly or 2) produced earlier and stored either a) as is, or b) converted to TML (to produce “
      
      conceptually annotated documents and text fragments in TML format”
      
      ), stored, and converted from ML for use by the Concept generator;
      
      v) “
      
      highlighted linguistically annotated documents and text fragments”
      
      produced by highlighting instances of Concepts in the text of said linguistically annotated documents, text fragments, and other text-forms, said highlighted linguistically annotated documents and text fragments having been 1) produced on-the-fly or 2) produced earlier and stored either a) as is, or b) converted to TML (to produce “
      
      highlighted linguistically annotated documents and text fragments in TML format”
      
      ), stored, and converted from TML for use by the Concept generator;
      
      vi) other text-based knowledge sources;
      
      vii) linguistic knowledge sources including vocabulary specifications;
      
      lexical relations (synonyms, hypernyms, hyponyms), syntactic categories, semantic entities (one or more tags for names of people, names of places, measures, dates;
      
      document level tags such as #subject, #from, #to, #date);
      
      viii) CSL-based knowledge sources (Concepts, Concept Calls, Operators, Patterns, grammar specifications in terms of Concepts, imported Concepts, one or more internal database Concepts to be used for generation); and
      
      ix) statistical knowledge sources frequencies of words (derived from text documents, text fragments, vocabulary items, and other data sources) and frequencies of tags (such as syntactic tags like noun phrase, document structure tags from HTML, and semantic tags from XML);
      
      x) data models;
      
      xi) user Concept definitions (UCDs), possibly in a UCD graph;
      
      xii) Concepts from the Concept database for use in generation;
      
      xiii) Concepts, user Concept groups (UCGs), and user-defined hierarchies mediated through the Concept manager;
      
      d) comprises various subtypes of Concept generator, depending on the UCD selected;
      
      e) outputs Concepts which are sent to the Concept database via the Concept manager, and f) outputs instructions to the Concept wizard.
  - 99. The system according to claim 98 wherein a User Concept Description (UCD) is used to generate a Concept, specifying ways in which Concepts can be generated from different types of knowledge (knowledge sources) by way of different data models, governed by various Directives, said UCD comprising:
    - a) one or more knowledge sources that provide raw content used to generate Concepts, b) one or more data models used to combine said knowledge sources used to generate Concepts, and c) one or more Directives governing said generation of said Concepts.
  - 100. The system according to claim 99 wherein said knowledge sources are selected from one of:
    - a) text-based knowledge sources;
      
      b) linguistic knowledge sources;
      
      c) CSL-based knowledge sources;
      
      d) statistical knowledge sources;
      
      or e) a combination of knowledge sources a)-d).
  - 101. The system according to claim 99 wherein a knowledge source-based UCD is a UCD in which:
    - a) options about knowledge sources are presented to users before options about data models or Directives;
      
      b) the selection of certain knowledge sources prioritizes the subsequent choices of data models and Directives presented to users (text fragments are most closely associated with the linguistic data model, documents with the statistical data model, and CSL Operators with the logical data model).
  - 102. The system according to claim 99 wherein said data models are selected from one of:
    - a) linguistic data models;
      
      b) logical data models;
      
      c) statistical data models;
      
      or d) a combination of data models a)-c).
  - 103. The system according to claim 99 wherein a data model-based UCD is a UCD in which:
    - a) options about data models are presented to users before options about knowledge sources or Directives;
      
      b) the selection of certain data models prioritizes the subsequent choices of knowledge sources and Directives presented to users (the linguistic data model is most closely associated with text fragments, the statistical data model with documents, and the logical data model with CSL Operators.
  - 104. The system according to claim 99 wherein a UCD is one of three types:
    - a) a basic UCD is a data structure in template form that is used to define types b) and c);
      
      b) an unpopulated UCD, which is a version of a), specifies the knowledge sources, data models, and Directives used in a knowledge-source based UCD (or one of its subtypes such as a text-based UCD) or a data-model based UCD (or one of its subtypes);
      
      c) a populated UCD, which is a version of b) with filled-in information about particular knowledge sources, data models, and Directives used in a particular instance of knowledge-source based UCD (or one of its subtypes) or a data-model based UCD (or one of its subtypes), that is, it is “
      
      filled out”
      
      with information during the generation of an actual Concept.
  - 105. The system according to claim 104 wherein said UCDs of three types (basic, unpopulated, populated) are organized hierarchically into a graph of UCDs wherein:
    - a) the top level of said graph is occupied by said basic UCD;
      
      b) the next level is occupied by said unpopulated UCDs including, but not limited to, said knowledge-source based UCD and data-model based UCDs;
      
      c) inherited information is optionally passed down from said basic UCD at said top level to said unpopulated UCDs at said next level;
      
      d) the next one or more levels are occupied by further unpopulated UCDs including, but not limited to, subtypes of said knowledge-source based UCD (such as a text-based UCD) or subtypes of said data-model based UCD (such as the logical-based UCD);
      
      e) inherited information is optionally passed down from said unpopulated UCDs at the higher level to said unpopulated UCDs at said next one or more levels, and further optionally passed within said one of more levels;
      
      f) the next level is occupied by said populated UCDs, wherein said UCDs are populated by i) one or more particular knowledge sources and Directives, supplied by the user, and ii) a generated Concept, supplied by said Concept generation method, g) said graph is optionally stored in a Concept database.
  - 106. The system according to claim 98 wherein said types of Concept generator mirror the various types of UCD, hence there are:
    - a) knowledge-source based Concept generators which can be divided into, though are not limited to, text-based, linguistic-based, CSL-based, and statistical-based Concept generators; and
      
      b) data-model based Concept generators which can be divided into linguistic, logical, and statistical Concept generators.
  - 107. The system according to claim 80 wherein said Concept database is used to store Concepts, said database:
    - a) keeps an up-to-date set of CSL files;
      
      b) keeps a record of what CSL files correspond to what UCDs and UCGs; and
      
      c) guarantees consistency of stored UCDs and UCGs (such that said UCDs and UCGs in said database can be compiled).
  - 108. The system according to claim 98 wherein said UCD graph contains UCDs of three types (basic, unpopulated, populated) organized hierarchically into a graph of UCDs wherein:
    - a) the top level of said graph is occupied by said basic UCD;
      
      b) the next level is occupied by said unpopulated UCDs including, but not limited to, said knowledge-source based UCD and data-model based UCDs;
      
      c) inherited information is optionally passed down from said basic UCD at said top level to said unpopulated UCDs at said next level;
      
      d) the next one or more levels are occupied by further unpopulated UCDs including, but not limited to, subtypes of said knowledge-source based UCD (such as a text-based UCD) or subtypes of said data-model based UCD (such as the logical-based UCD);
      
      e) inherited information is optionally passed down from said unpopulated UCDs at the higher level to said unpopulated UCDs at said next one or more levels, and further optionally passed within said one of more levels;
      
      f) the next level is occupied by said populated UCDs, wherein said UCDs are populated by i) one or more particular knowledge sources and Directives, supplied by the user, and ii) a generated Concept, supplied by said Concept generation method, g) said graph is optionally stored in a Concept database.
  - 109. The system according to claim 80 wherein said Concept manager comprises a Concept database administrator and a Concept editor.
  - 110. The system according to claim 109 wherein said Concept database administrator a) is responsible for loading, storing, and managing uncompiled and compiled Concepts, UCDs and UCGs in the Concept database;
    - b) is responsible for loading, storing, and managing compiled Concepts ready for annotation and for generation;
      
      c) is responsible for managing a UCD graph;
      
      d) allows users to view relationships among Concepts, UCDs, and UCGs in the Concept database;
      
      e) allows users to search for Concepts, UCDs, and UCGs;
      
      f) allows users to search for the presence of Concepts in UCDs and UCGs;
      
      g) allows users to search for dependencies of UCDs and UCGs on Concepts;
      
      h) makes sure the Concept database always contains a set of Concepts, UCDs, and UCG that are logically consistent and consistent such that said sets in can be compiled;
      
      i) keeps CSL files up to date with the changing definitions of Concepts, UCDs, and UCGs;
      
      j) checks the integrity of Concepts, UCDs, and UCGs (such that if A depends on B, then B can not be deleted);
      
      k) handles dependencies within and between Concepts, UCDs, and UCGs;
      
      l) allows functions performed by Concept editor to add, remove, and modify Concepts, UCDs, and UCGs in the Database without fear of breaking other Concepts, UCDs, or UCGs in the same database.
  - 111. The system according to claim 109 wherein said Concept editor a) allows users to view relationships among Concepts, UCDs, and UCGs in the Concept database;
    - b) allows users to search for Concepts, UCDs, and UCGs;
      
      c) allows users to search for the presence of Concepts in UCDs and UCGs;
      
      d) allows users to search for dependencies of UCDs and UCGs on Concepts;
      
      e) allows users to add, remove, and modify all types of Concept (if users have appropriate permissions);
      
      f) allows users to add, remove, and modify all types of UCD except Basic UCDs, g) pre-sets permissions so that only certain privileged users can edit unpopulated UCDs;
      
      h) allows users to users save a UCD under a different name, and can also change any other properties they like;
      
      i) allows users to add, remove, and modify User Concept Groups (UCGs);
      
      j) allows users to save a UCG under a different name;
      
      k) allows users to change a Concept Group name, description, and any other properties they like in UCGs;
      
      l) allows users to add, remove, and modify user-defined hierarchies.
  - 112. The system according to claim 80 wherein said CSL parser a) takes as input a synonym database, CSL query, and CSL Concepts and Patterns;
    - b) engages in;
      
      i) word compilation;
      
      ii) Concept compilation;
      
      iii) downward synonym propagation; and
      
      iv) upward synonym propagation; and
      
      c) outputs CSL Concepts and Patterns for annotation.

Specification

Resources

Litigation Campaign Assessment

Current Assignee
Matrikon Inc. (Honeywell International Inc.)
Original Assignee
Matrikon Inc. (Honeywell International Inc.)
Inventors
Yeske, Ryan

Application Number

US10/555,126
Publication Number

US 20070174041A1
Time in Patent Office

Days
Field of Search
US Class Current

704/3
CPC Class Codes

G06F 40/30 Semantic analysis

Method and system for concept generation and management

First Claim

2 Assignments

0 Petitions

Accused Products

Abstract

126 Citations

112 Claims

Specification

Use Cases

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Method and system for concept generation and management

First Claim

2 Assignments

Subscription Required

Subscription Required

0 Petitions

Subscription Required

Accused Products

Subscription Required

Abstract

126 Citations

112 Claims

Specification

Subscription Required

Use Cases

Quick Links

Others