Abstract:
A computer-implemented system and method for identifying relevant documents is provided. A set of documents each associated with one or more concepts is identified. At least a portion of the documents in the set are clustered based on the concepts. A matrix that provides a summary of the documents most relevant to one such concept is generated by determining for each document a measure of similarity between a concept frequency occurrence and concept weights of each cluster. The matrix is populated with the calculated measures of similarity. Those documents associated with a threshold measure of similarity are identified as the most relevant documents to one such concept.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This patent application is a continuation of U.S. patent application Ser. No. 14/949,829, filed Nov. 23, 2015, pending; which is a continuation of U.S. Pat. No. 9,195,399, issued Nov. 24, 2015; which is a continuation of U.S. Pat. No. 8,725,736, issued May 13, 2014; which is a continuation of U.S. Pat. No. 8,380,718, issued Feb. 19, 2013; which is a continuation of U.S. Pat. No. 8,015,188, issued Sep. 6, 2011; which is a continuation of U.S. Pat. No. 7,809,727, issued Oct. 5, 2010; which is a continuation of U.S. Pat. No. 7,313,556, issued Dec. 25, 2007; which is a continuation of U.S. Pat. No. 6,978,274, issued Dec. 20, 2005, the priority filing dates of which are claimed and the disclosures of which are incorporated by reference. 
     
    
     FIELD 
       [0002]    The present invention relates in general to text mining and, in particular, to a computer-implemented system and method for identifying relevant documents. 
       BACKGROUND 
       [0003]    Document warehousing extends data warehousing to content mining and retrieval. Document warehousing attempts to extract semantic information from collections of unstructured documents to provide conceptual information with a high degree of precision and recall. Documents in a document warehouse share several properties. First, the documents lack a common structure or shared type. Second, semantically-related documents are integrated through text mining. Third, essential document features are extracted and explicitly stored as part of the document warehouse. Finally, documents are often retrieved from multiple and disparate sources, such as over the Internet or as electronic messages. 
         [0004]    Document warehouses are built in stages to deal with a wide range of information sources. First, document sources are identified and documents are retrieved into a repository. For example, the document sources could be electronic messaging folders or Web content retrieved over the Internet. Once retrieved, the documents are pre-processed to format and regularize the information into a consistent manner. Next, during text analysis, text mining is performed to extract semantic content, including identifying dominant themes, extracting key features and summarizing the content. Finally, metadata is compiled from the semantic context to explicate essential attributes. Preferably, the metadata is provided in a format amenable to normalized queries, such as database management tools. Document warehousing is described in D. Sullivan, “Document Warehousing and Text Mining, Techniques for Improving Business Operations, Marketing, and Sales,” Chs. 1-3, Wiley Computer Publishing (2001), the disclosure of which is incorporated by reference. 
         [0005]    Text mining is at the core of the data warehousing process. Text mining involves the compiling, organizing and analyzing of document collections to support the delivery of targeted types of information and to discover relationships between relevant facts. However, identifying relevant content can be difficult. First, extracting relevant content requires a high degree of precision and recall. Precision is the measure of how well the documents returned in response to a query actually address the query criteria. Recall is the measure of what should have been returned by the query. Typically, the broader and less structured the documents, the lower the degree of precision and recall. Second, analyzing an unstructured document collection without the benefit of a priori knowledge in the form of keywords and indices can present a potentially intractable problem space. Finally, synonymy and polysemy can cloud and confuse extracted content. Synonymy refers to multiple words having the same meaning and polysemy refers to a single word with multiple meanings. Fine-grained text mining must reconcile synonymy and polysemy to yield meaningful results. 
         [0006]    In the prior art, text mining is performed in two ways. First, syntactic searching provides a brute force approach to analyzing and extracting content based on literal textual attributes found in each document. Syntactic searching includes keyword and proximate keyword searching as well as rule-based searching through Boolean relationships. Syntactic searching relies on predefined indices of keywords and stop words to locate relevant information. However, there are several ways to express any given concept. Accordingly, syntactic searching can fail to yield satisfactory results due to incomplete indices and poorly structured search criteria. 
         [0007]    A more advanced prior art approach uses a vector space model to search for underlying meanings in a document collection. The vector space model employs a geometric representation of documents using word vectors. Individual keywords are mapped into vectors in multi-dimensional space along axes representative of query search terms. Significant terms are assigned a relative weight and semantic content is extracted based on threshold filters. Although substantially overcoming the shortcomings of syntactic searching, the multivariant and multidimensional nature of the vector space model can lead to a computationally intractable problem space. As well, the vector space model fails to resolve the problems of synonymy and polysemy. 
         [0008]    Therefore, there is a need for an approach to dynamically evaluating concepts inherent in a collection of documents. Such an approach would preferably dynamically discover the latent meanings without the use of a priori knowledge or indices. Rather, the approach would discover semantic relationships between individual terms given the presence of another item. 
         [0009]    There is a further need for an approach to providing a graphical visualization of concepts extracted from a document set through semantic indexing. Preferably, such an approach would extract the underlying meanings of documents through statistics and linear algebraic techniques to find clusters of terms and phrases representative of the concepts. 
       SUMMARY 
       [0010]    The present invention provides a system and method for indexing and evaluating unstructured documents through analysis of dynamically extracted concepts. A set of unstructured documents is identified and retrieved into a document warehouse repository. Individual concepts are extracted from the documents and mapped as normalized data into a database. The frequencies of occurrence of each concept within each document and over all documents are determined and mapped. A corpus graph is generated to display a minimized set of concepts whereby each concept references at least two documents and no document in the corpus is unreferenced. A subset of documents occurring within predefined edge conditions of a median value are selected. Clusters of concepts are grouped into themes. Inner products of document concept frequency occurrences and cluster concept weightings are mapped into a multi-dimensional concept space for each theme and iteratively generated until the clusters settle. The resultant data minima indicates those documents having the most pertinence to the identified concepts. 
         [0011]    An embodiment provides a computer-implemented system and method for identifying relevant documents. A set of documents each associated with one or more concepts is identified. At least a portion of the documents in the set are clustered based on the concepts. A matrix that provides a summary of the documents most relevant to one such concept is generated by determining for each document a measure of similarity between a concept frequency occurrence and concept weights of each cluster. The matrix is populated with the calculated measures of similarity. Those documents associated with a threshold measure of similarity are identified as the most relevant documents to one such concept. 
         [0012]    In summary, the present invention semantically evaluates terms and phrases with the goal of creating meaningful themes. Document frequencies and co-occurrences of terms and phrases are used to select a minimal set of highly correlated terms and phrases that reference all documents in a corpus. 
         [0013]    Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein is described embodiments of the invention by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]      FIG. 1  is a block diagram showing a system for dynamically evaluating latent concepts in unstructured documents, in accordance with the present invention. 
           [0015]      FIG. 2  is a block diagram showing the software modules implementing the document analyzer of  FIG. 1 . 
           [0016]      FIG. 3  is a process flow diagram showing the stages of text analysis performed by the document analyzer of  FIG. 1 . 
           [0017]      FIG. 4  is a flow diagram showing a method for dynamically evaluating latent concepts in unstructured documents, in accordance with the present invention. 
           [0018]      FIG. 5  is a flow diagram showing the routine for performing text analysis for use in the method of  FIG. 4 . 
           [0019]      FIG. 6  is a flow diagram showing the routine for creating a histogram for use in the routine of  FIG. 5 . 
           [0020]      FIG. 7  is a data structure diagram showing a database record for a concept stored in the database  30  of  FIG. 1 . 
           [0021]      FIG. 8  is a data structure diagram showing, by way of example, a database table containing a lexicon of extracted concepts stored in the database  30  of  FIG. 1 . 
           [0022]      FIG. 9  is a graph showing, by way of example, a histogram of the frequencies of concept occurrences generated by the routine of  FIG. 6 . 
           [0023]      FIG. 10  is a table showing, by way of example, concept occurrence frequencies generated by the routine of  FIG. 6 . 
           [0024]      FIG. 11  is a graph showing, by way of example, a corpus graph of the frequency of concept occurrences generated by the routine of  FIG. 5 . 
           [0025]      FIG. 12  is a flow diagram showing a routine for creating a matrix for use in the routine of  FIG. 5 . 
           [0026]      FIG. 13  is a table showing, by way of example, the matrix of themes generated by the routine of  FIG. 12 . 
           [0027]      FIG. 14  is a flow diagram showing a routine for determining results for use in the routine of  FIG. 5 . 
       
    
    
     DETAILED DESCRIPTION 
     Glossary 
       [0000]    
       
         
           
             Keyword: A literal search term which is either present or absent from a document. Keywords are not used in the evaluation of documents as described herein. 
             Term: A root stem of a single word appearing in the body of at least one document. 
             Phrase: Two or more words co-occurring in the body of a document. A phrase can include stop words. 
             Concept: A collection of terms or phrases with common semantic meanings. 
             Theme: Two or more concepts with a common semantic meaning. 
             Cluster: All documents for a given concept or theme.
 
The foregoing terms are used throughout this document and, unless indicated otherwise, are assigned the meanings presented above.
 
           
         
       
     
         [0034]      FIG. 1  is a block diagram showing a system  11  for dynamically evaluating latent concepts in unstructured documents, in accordance with the present invention. By way of illustration, the system  11  operates in a distributed computing environment  10  which includes a plurality of heterogeneous systems and document sources. The system  11  implements a document analyzer  12 , as further described below beginning with reference to  FIG. 2 , for evaluating latent concepts in unstructured documents. The system  11  is coupled to a storage device  13  which stores a document warehouse  14  for maintaining a repository of documents and a database  30  for maintaining document information. 
         [0035]    The document analyzer  12  analyzes documents retrieved from a plurality of local sources. The local sources include documents  17  maintained in a storage device  16  coupled to a local server  15  and documents  20  maintained in a storage device  19  coupled to a local client  18 . The local server  15  and local client  18  are interconnected to the system  11  over an intranetwork  21 . In addition, the document analyzer  12  can identify and retrieve documents from remote sources over an internetwork  22 , including the Internet, through a gateway  23  interfaced to the intranetwork  21 . The remote sources include documents  26  maintained in a storage device  25  coupled to a remote server  24  and documents  29  maintained in a storage device  28  coupled to a remote client  27 . 
         [0036]    The individual documents  17 ,  20 ,  26 ,  29  include all forms and types of unstructured data, including electronic message stores, such as electronic mail (email) folders, word processing documents or Hypertext documents, and could also include graphical or multimedia data. Notwithstanding, the documents could be in the form of structured data, such as stored in a spreadsheet or database. Content mined from these types of documents does not require preprocessing, as described below. 
         [0037]    In the described embodiment, the individual documents  17 ,  20 ,  26 ,  29  include electronic message folders, such as maintained by the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond, Wash. The database is an SQL-based relational database, such as the Oracle database management system, release 8, licensed by Oracle Corporation, Redwood Shores, Calif. 
         [0038]    The individual computer systems, including system  11 , server  15 , client  18 , remote server  24  and remote client  27 , are general purpose, programmed digital computing devices consisting of a central processing unit (CPU), random access memory (RAM), non-volatile secondary storage, such as a hard drive or CD ROM drive, network interfaces, and peripheral devices, including user interfacing means, such as a keyboard and display. Program code, including software programs, and data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage. 
         [0039]      FIG. 2  is a block diagram showing the software modules  40  implementing the document analyzer  12  of  FIG. 1 . The document analyzer  12  includes three modules: storage and retrieval manager  41 , text analyzer  42 , and display and visualization  43 . The storage and retrieval manager  41  identifies and retrieves documents  44  into the document warehouse  14  (shown in  FIG. 1 ). The documents  44  are retrieved from various sources, including both local and remote clients and server stores. The text analyzer  42  performs the bulk of the text mining processing. The display and visualization  43  complements the operations performed by the text analyzer  42  by presenting visual representations of the information extracted from the documents  44 . The display and visualization  43  can also generate a graphical representation which preserves independent variable relationships, such as described in common-assigned U.S. Pat. No. 6,888,548, issued May 3, 2005, the disclosure of which is incorporated by reference. 
         [0040]    During text analysis, the text analyzer  42  identifies terms and phrases and extracts concepts in the form of noun phrases that are stored in a lexicon  18  maintained in the database  30 . After normalizing the extracted concepts, the text analyzer  42  generates a frequency table  46  of concept occurrences, as further described below with reference to  FIG. 6 , and a matrix  47  of summations of the products of pair-wise terms, as further described below with reference to  FIG. 10 . Similarly, the display and visualization  43  generates a histogram  47  of concept occurrences per document, as further described below with reference to  FIG. 6 , and a corpus graph  48  of concept occurrences over all documents, as further described below with reference to  FIG. 8 . 
         [0041]    Each module is a computer program, procedure or module written as source code in a conventional programming language, such as the C++ programming language, and is presented for execution by the CPU as object or byte code, as is known in the art. The various implementations of the source code and object and byte codes can be held on a computer-readable storage medium or embodied on a transmission medium in a carrier wave. The document analyzer  12  operates in accordance with a sequence of process steps, as further described below with reference to  FIG. 5 . 
         [0042]      FIG. 3  is a process flow diagram showing the stages  60  of text analysis performed by the document analyzer  12  of  FIG. 1 . The individual documents  44  are preprocessed and noun phrases are extracted as concepts (transition  61 ) into a lexicon  45 . The noun phrases are normalized and queried (transition  62 ) to generate a frequency table  46 . The frequency table  46  identifies individual concepts and their respective frequency of occurrence within each document  44 . The frequencies of concept occurrences are visualized (transition  63 ) into a frequency of concepts histogram  48 . The histogram  48  graphically displays the frequencies of occurrence of each concept on a per-document basis. Next, the frequencies of concept occurrences for all the documents  44  are assimilated (transition  64 ) into a corpus graph  49  that displays the overall counts of documents containing each of the extracted concepts. Finally, the most relevant concepts are summarized (transition  65 ) into a matrix  46  that presents the results as summations of the products of pair-wise terms. 
         [0043]      FIG. 4  is a flow diagram showing a method  70  for dynamically evaluating latent concepts in unstructured documents  44  (shown in  FIG. 2 ), in accordance with the present invention. As a preliminary step, the set of documents  44  to be analyzed is identified (block  71 ) and retrieved into the document warehouse  14  (shown in  FIG. 1 ) (block  72 ). The documents  44  are unstructured data and lack a common format or shared type. The documents  44  include electronic messages stored in messaging folders, word processing documents, hypertext documents, and the like. 
         [0044]    Once identified and retrieved, the set of documents  44  is analyzed (block  73 ), as further described below with reference to  FIG. 5 . During text analysis, a matrix  47  (shown in  FIG. 2 ) of term-document association data is constructed to summarize the semantic content inherent in the structure of the documents  44 . As well, the frequency of individual terms or phrases extracted from the documents  44  are displayed and the results are optionally visualized (block  74 ). The routine then terminates. 
         [0045]      FIG. 5  is a flow diagram showing the routine  80  for performing text analysis for use in the method  70  of  FIG. 4 . The purpose of this routine is to extract and index terms or phrases for the set of documents  44  (shown in  FIG. 2 ). Preliminarily, each document in the documents set  44  is preprocessed (block  81 ) to remove stop words. These include commonly occurring words, such as indefinite articles (“a” and “an”), definite articles (“the”), pronouns (“I”, “he” and “she”), connectors (“and” and “or”), and similar non-substantive words. 
         [0046]    Following preprocessing, a histogram  48  of the frequency of terms (shown in  FIG. 2 ) is logically created for each document  44  (block  82 ), as further described below with reference to  FIG. 6 . Each histogram  48 , as further described below with reference to  FIG. 9 , maps the relative frequency of occurrence of each extracted term on a per-document basis. 
         [0047]    Next, a document reference frequency (corpus) graph  49 , as further described below with reference to  FIG. 10 , is created for all documents  44  (block  83 ). The corpus graph  49  graphically maps the semantically-related concepts for the entire documents set  44  based on terms and phrases. A subset of the corpus is selected by removing those terms and phrases falling outside either edge of predefined thresholds (block  84 ). For shorter documents, such as email, having less semantically-rich content, the thresholds are set from about 1% to about 15%, inclusive. Larger documents may require tighter threshold values. 
         [0048]    The selected set of terms and phrases falling within the thresholds are used to generate themes (and concepts) (block  85 ) based on correlations between normalized terms and phrases in the documents set. In the described embodiment, themes are primarily used, rather than individual concepts, as a single co-occurrence of terms or phrases carries less semantic meaning than multiple co-occurrences. As used herein, any reference to a “theme” or “concept” will be understood to include the other term, except as specifically indicated otherwise. 
         [0049]    Next, clusters are created (block  86 ) from groups of highly-correlated concepts and themes. Individual concepts and themes are categorized based on, for example, Euclidean distances calculated between each pair of concepts and themes and defined within a pre-specified range of variance, such as described in commonly-assigned U.S. Pat. No. 6,778,995, issued Aug. 17, 2004, the disclosure of which is incorporated by reference. 
         [0050]    A matrix  47  of the documents  44  is created (block  87 ), as further described below with reference to  FIG. 13 . The matrix  47  contains the inner products of document concept frequency occurrences and cluster concept weightings mapped into a multi-dimensional concept space for each theme. Finally, the results of the text analysis operations are determined (block  88 ), as further described below with reference to  FIG. 14 , after which the routine returns. 
         [0051]      FIG. 6  is a flow diagram showing the routine  90  for creating a histogram  48  (shown in  FIG. 2 ) for use in the routine of  FIG. 5 . The purpose of this routine is to extract noun phrases representing individual concepts and to create a normalized representation of the occurrences of the concepts on a per-document basis. The histogram represents the logical union of the terms and phrases extracted from each document. In the described embodiment, the histogram  48  need not be expressly visualized, but is generated internally as part of the text analysis process. 
         [0052]    Initially, noun phrases are extracted (block  91 ) from each document  44 . In the described embodiment, concepts are defined on the basis of the extracted noun phrases, although individual nouns or tri-grams (word triples) could be used in lieu of noun phrases. In the described embodiment, the noun phrases are extracted using the LinguistX product licensed by Inxight Software, Inc., Santa Clara, Calif. 
         [0053]    Once extracted, the individual terms or phrases are loaded into records stored in the database  30  (shown in  FIG. 1 ) (block  92 ). The terms stored in the database  30  are normalized (block  93 ) such that each concept appears as a record only once. In the described embodiment, the records are normalized into third normal form, although other normalization schemas could be used. 
         [0054]      FIG. 7  is a data structure diagram showing a database record  100  for a concept stored in the database  30  of  FIG. 1 . Each database record  100  includes fields for storing an identifier  101 , string  102  and frequency  103 . The identifier  101  is a monotonically increasing integer value that uniquely identifies each term or phrase stored as the string  102  in each record  100 . The frequency of occurrence of each term or phrase is tallied in the frequency  103 . 
         [0055]      FIG. 8  is a data structure diagram showing, by way of example, a database table  110  containing a lexicon  111  of extracted concepts stored in the database  30  of  FIG. 1 . The lexicon  111  maps out the individual occurrences of identified terms  113  extracted for any given document  112 . By way of example, the document  112  includes three terms numbered  1 ,  3  and  5 . Concept  1  occurs once in document  112 , concept  3  occurs twice, and concept  5  occurs once. The lexicon tallies and represents the occurrences of frequency of the concepts  1 ,  3  and  5  across all documents  44 . 
         [0056]    Referring back to  FIG. 6 , a frequency table is created from the lexicon  111  for each given document  44  (block  94 ). The frequency table is sorted in order of decreasing frequencies of occurrence for each concept  113  found in a given document  44 . In the described embodiment, all terms and phrases occurring just once in a given document are removed as not relevant to semantic content. The frequency table is then used to generate a histogram  48  (shown in  FIG. 2 ) (block  95 ) which visualizes the frequencies of occurrence of extracted concepts in each document. The routine then returns. 
         [0057]      FIG. 9  is a graph showing, by way of example, a histogram  48  of the frequencies of concept occurrences generated by the routine of  FIG. 6 . The x-axis defines the individual concepts  121  for each document and the y-axis defines the frequencies of occurrence of each concept  122 . The concepts are mapped in order of decreasing frequency  123  to generate a curve  124  representing the semantic content of the document  44 . Accordingly, terms or phrases appearing on the increasing end of the curve  124  have a high frequency of occurrence while concepts appearing on the descending end of the curve  124  have a low frequency of occurrence. 
         [0058]      FIG. 10  is a table  130  showing, by way of example, concept occurrence frequencies generated by the routine of  FIG. 6 . Each concept  131  is mapped against the total frequency occurrence  132  for the entire set of documents  44 . Thus, for each of the concepts  133 , a cumulative frequency  134  is tallied. The corpus table  130  is used to generate the document concept frequency reference (corpus) graph  49 . 
         [0059]      FIG. 11  is a graph  140  showing, by way of example, a corpus graph of the frequency of concept occurrences generated by the routine of  FIG. 5 . The graph  140  visualizes the extracted concepts as tallied in the corpus table  130  (shown in  FIG. 10 ). The x-axis defines the individual concepts  141  for all documents and the y-axis defines the number of documents  44  referencing each concept  142 . The individual concepts are mapped in order of descending frequency of occurrence  143  to generate a curve  144  representing the latent semantics of the set of documents  44 . 
         [0060]    A median value  145  is selected and edge conditions  146   a - b  are established to discriminate between concepts which occur too frequently versus concepts which occur too infrequently. Those documents falling within the edge conditions  146   a - b  form a subset of documents containing latent concepts. In the described embodiment, the median value  145  is document-type dependent. For efficiency, the upper edge condition  146   b  is set to 70% and the  64  concepts immediately preceding the upper edge condition  146   b  are selected, although other forms of threshold discrimination could also be used. 
         [0061]      FIG. 12  is a flow diagram showing the routine  150  for creating a matrix  47  (shown in  FIG. 2 ) for use in the routine of  FIG. 5 . Initially, those documents  44  having zero values for frequency counts are removed through filtering (block  151 ). The inner products of document concept frequency occurrences and cluster concept weightings mapped into a multi-dimensional concept space for each theme are calculated and used to populate the matrix (block  152 ). The individual cluster weightings are iteratively updated (block  153 ) to determine best fit. Those documents having the smallest inner products are deemed most relevant to a given theme and are identified (block  154 ). The routine then returns. 
         [0062]      FIG. 13  is a table  170  showing the matrix  47  generated by the routine of  FIG. 12 . The matrix  47  maps a cluster  171  to documents  172  based on a calculated inner product. Each inner product quantifies similarities between documents, as represented by a distance. The distance is mapped into a multi-dimensional concept space for a given document, as measured by the magnitude of a vector for a given term drawn relative to an angle θ, held constant for the given cluster. 
         [0063]    For a set of n documents, the distance d cluster  is calculated by taking the sum of products (inner product) by terms between document concept frequency occurrences and cluster concept weightings, using the following equation: 
         [0000]    
       
         
           
             
               d 
               cluster 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   → 
                   n 
                 
               
                
               
                 
                   doc 
                   
                     term 
                     i 
                   
                 
                 · 
                 
                   cluster 
                   
                     term 
                     i 
                   
                 
               
             
           
         
       
     
         [0000]    where doc term  represents the frequency of occurrence for a given term i in the selected document and cluster term  represents the weight of a given cluster for a given term i. The weights of the individual inner products are iteratively updated until the clusters settle. The goal is to calculate the minimum distances between as few clusters as possible until the rate of change goes constant. The rate of change can be calculated, for example, by taking the first derivative of the inner products over successive iterations. 
         [0064]      FIG. 14  is a flow diagram showing the routine  180  for determining results for use in the routine of  FIG. 5 . Duplicate documents  44  are removed from the results (block  181 ). The results are re-run (block  182 ), as necessary by repeating the text analysis operations (block  183 ), beginning with creating the corpus graph  49  (block  84  in  FIG. 5 ). After satisfactory results have been obtained (block  182 ), the routine returns. 
         [0065]    Satisfactory results are shown when a meaningful cluster of documents is found. Objectively, each document within a given theme will have an inner product falling within a pre-defined variance of other related documents, thereby reflecting a set amount of similarity. The cluster itself represents a larger grouping of document sets based on related, but not identical, themes. 
         [0066]    If necessary, the results are re-run (block  182 ). One reason to re-run the results set would be to re-center the median value  145  of the corpus graph  140  (shown in  FIG. 11 ) following the filtering of further documents  44 . The filtering of edge condition concept frequency occurrences will cause the curve  144  to be redefined, thereby requiring further processing. 
         [0067]    While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.