Patent Abstract:
A computerized method of analyzing a knowledgebase comprising; assembling a collection of queries made by users to obtain information from the knowledgebase; identifying in each query, sets of collocated words in that query to form a list of collocated word sets in the collection; from the list, identifying and presenting frequently collocated word sets in the collection. Likewise, a histogram of scaled relative difference between the frequency of word sets at first and second time intervales may be presented.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims priority from U.S. Provisional Patent Application No. 61/709,746 filed Oct. 4, 2012, the contents of which are hereby incorporated herein by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates generally to data analysis, and more particularly to software, devices and methods for analysing, and optionally improving, knowledge bases and the handling of queries to such knowledge bases. 
       BACKGROUND OF THE INVENTION 
       [0003]    In recent years, computerized searching of data has become prevalent. As the public Internet has grown, so has the need for indexing and organizing data. 
         [0004]    One search technique that is particularly useful in searching contained amounts of information is disclosed in U.S. Pat. No. 7,171,409, the contents of which are hereby incorporated by reference. As disclosed therein, a knowledgebase may be searched by receiving a natural language query. Based on the query, the best one of many responses may be presented. 
         [0005]    Using natural language queries to query a knowledgebase may be an effective way to extract information from the knowledge base. At the same time, the nature of a presented query may identify a deficiency or flaw in the content of the knowledgebase or in how it is being searched. Similarly, an analysis of many queries may provide insight into a perception or a behavior on the part of users making the queries. 
         [0006]    Accordingly, there remains a need for effectively analyzing data derived from queries and using the analysis to extract further information, and possibly refine knowledge bases and search techniques. 
       SUMMARY OF THE INVENTION 
       [0007]    In accordance with an aspect of the present disclosure, there is provided a computerized method of analyzing a knowledgebase comprising: assembling a collection of queries made by users to obtain information from the knowledgebase; identifying in each query, sets of collocated words in that query to form a list of collocated word sets in the collection; from the list, identifying and presenting frequently collocated word sets in the collection. 
         [0008]    In accordance with another aspect of the present disclosure there is provided a computerized method of analyzing a knowledgebase. The method comprises assembling a collection of queries made by users to obtain information from the knowledgebase; identifying in each query in the collection in a first and second time interval, word sets in that query and theft frequency to form a first and second list of frequently used word sets in the collection in the first time interval and second time intervals respectively. For each word set in the first list and the second list, a relative difference between theft respective frequencies in the first list and second list is calculated. Each relative difference is scaled by a scale factor proportional to the frequency for that word set in the first or second interval to form scaled relative differences. A histogram of the scaled relative differences may be generated and presented. The histogram may be presented as a tag cloud. 
         [0009]    Other aspects and features of the present invention will become apparent to those of ordinary skill in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0010]    In the figures which illustrate by way of example only, embodiments of the present invention, 
           [0011]      FIG. 1  illustrates a computer network and network interconnected computing device, operable to analyse query data and provide results, exemplary of an embodiment of the present invention; 
           [0012]      FIG. 2  is a functional block diagram of software stored and executing at the device of  FIG. 1 ; 
           [0013]      FIG. 3  is a diagram illustrating a database schema for a database used by a device of  FIG. 1 ; 
           [0014]      FIG. 4  depicts a flow chart illustrating the execution of software at the device of  FIG. 1 , exemplary of an embodiment of the present invention; 
           [0015]      FIG. 5  is a diagram illustrating a database schema for a database used by a device of  FIG. 1 ; 
           [0016]      FIG. 6  is a flow chart illustrating the execution of software at the device of  FIG. 1 , exemplary of an embodiment of the present invention; 
           [0017]      FIG. 7  illustrates exemplary output provided by the device of  FIG. 1 ; 
           [0018]      FIG. 8  is a diagram illustrating a further database schema for a database used by a device of  FIG. 1 ; 
           [0019]      FIGS. 9-11  illustrate exemplary output provided by the device of  FIG. 1   
       
    
    
     DETAILED DESCRIPTION 
       [0020]      FIG. 1  illustrates a network interconnected computing device  12 . Computing device  12  which may be a conventional network server is a device exemplary of the present invention including software adapting it to operate in manners exemplary of embodiments of the present invention. 
         [0021]    As illustrated, computing device  12  is in communication with a computer network  10  in communication with other computing devices such as end-user computing devices  14  and other computer servers (not specifically illustrated). Network  10  is preferably the public Internet, but could similarly be a private local area packet switched data network coupled to computing device  12 . So, network  10  could, for example, be an Internet protocol, X.25, IPX compliant or similar network. 
         [0022]    Example end-user computing devices  14  are illustrated. End-user computing devices  14  are conventional network interconnected computers, used to access data from network interconnected servers, such as computing device  12 . Device  12  may, for example, take the form of a person computer, laptop, tablet, mobile phone, or other programmable computing device. 
         [0023]    Example computing device  12  preferably includes a network interface physically connecting computing device  12  to data network  10 , and a processor coupled to conventional computer memory. Example computing device  12  may further include input and output peripherals such as a keyboard, display and mouse. As well, computing device  12  may include a peripheral usable to load software exemplary of the present invention into its memory for execution from a software readable medium, such as medium  20 . As such, computing device  12  includes a conventional filesystem, preferably controlled and administered by the operating system governing overall operation of computing device  12 . This filesystem preferably hosts search data in database  30 , and analysis software  46  exemplary of an embodiment of the present invention, as detailed below. In the illustrated embodiment, computing device  12  also includes hypertext transfer protocol (“HTTP”) files used to provide an administrator or other user with an interface to access computing device  12 . 
         [0024]    As will become apparent, computing device  12  includes software  46  capable of analyzing search information, representative of natural language user queries to a knowledgebase. In particular, exemplary software  46  is capable of analyzing text queries to locate and analyze frequently used words, or sets of two or words (word clusters), and extract data therefrom that may be used to identify themes in queries presented by the user. In the depicted embodiment, the word clusters take the form of single words or collocated words in a query. In an embodiment, the word clusters are collocated word pairs occurring in the queries. In a further embodiment, the word clusters are adjacent words—and may be adjacent word pairs, or three, four or more adjacent words. Possibly, single words may also be considered and treated as word clusters. 
         [0025]    In particular, computing device  12  maintains database  30  including a collection of user queries presented to search software used to query the content of a knowledgebase. In the depicted embodiment, computing device  12  may maintain a database of natural language queries presented to a natural language query interface. For example, computing device  12  may include a database that stores user queries presented to search software detailed in the &#39;409 patent. In an alternate embodiment, database  30  may store an entire database containing a knowledgebase and queries made to that knowledgebase. 
         [0026]    As disclosed in the &#39;409 patent, natural language user queries may be received at a computing device and parsed. Stored Boolean expressions associated with candidate responses are applied to the user queries to identify one or more candidate responses that address the user query. One or more responses associated with the best matching Boolean expressions may be presented to the end user as a response to the query. As such, anticipated queries may be precisely answered from data in the knowledgebase. A system in accordance with the &#39;409 patent is used by many consumer agencies—e.g. banks, merchants, service providers—in order to provide end-user customers with end-user support, by way of questions submitted over the Internet. Ideally, typical questions are predicted and lead to a single best response. 
         [0027]    Computing device  12  receives the natural language queries that have been input by users to query the knowledgebase, and stores these in database  30 . The natural language queries may be received directly at computing device  12 , or may be provided to computing device  12  by way of network  10 , by way of another server. In any event, database  30  contains entries representative of the collection of user searches for information in a knowledgebase. Ideally, entries in database  30  include the entire collection of queries made to a knowledgebase. 
         [0028]    The queries may be collected over time, and stored in one or more tables of database  30 . As such, database  30  may include all queries received during a particular time interval. Queries may be include multiple fields, that may used for search and indexing criteria, including date of receipt (DATE_STAMP); query content (QUERY); response (RESPONSE_ID); etc. Other fields (not illustrated) may also be maintained in database  30 . 
         [0029]    Now, the knowledgebase typically contains information that is related—for example the knowledgebase could be an intranet site, the Internet site of a particular entity (e.g. corporation, partnership, or the like); a wiki maintained by an entity; a knowledgebase answering frequently asked questions; a social network feed-like a twitter feed, or the like. As noted, in a particular embodiment, the knowledgebase may be collection of answers to customer questions. As a consequence, proper analysis of natural language queries made to the knowledgebase may allow for improvement of the knowledgebase and search algorithms used by the knowledgebase. Likewise, the analysis may provide insight into the thoughts or wishes of the users, and allow for the provision of enhanced products or services to the users. 
         [0030]      FIG. 2  illustrates a functional block diagram of software components preferably implemented at computing device  12 . As will be appreciated, software components embodying such functional blocks may be loaded from medium  20  ( FIG. 1 ) and stored within persistent memory at computing device  12 . Alternatively, the software components may reside at another computing device executed as a software as a service. Data to be processed may be provided from computing device  12 , and results provided to computing device  12 . 
         [0031]    As illustrated, typical software components include operating system software  40 ; a database engine  42 ; analysis software  46 ; a presentation component  60 ; and an optional an http server application  44 , exemplary of embodiments of the present invention. Further, database  30  is again illustrated. Again database  30  may be stored within memory at computing device  12 . As well data files  48  used by search software  46 , presentation component  50  and http server application  44  are illustrated. 
         [0032]    Operating system software  40  may, for example, be a Linux based operating system software; OS/X operating system; Microsoft operating system software, or the like. Operating system software  40  also includes a TCP/IP stack, allowing communication of computing device  12  with data network  10 . Database engine  42  may be a conventional relational or object oriented database engine, such as Microsoft SQL Server, Oracle, DB2, Sybase, Pervasive or any other database engine known to those of ordinary skill in the art. Database engine  42  thus typically includes an interface for interaction with operating system software  40 , and other application software, such as analysis software  46 . Database engine  42  is used to add, delete and modify records at database  30 . HTTP server application  44  may be an Apache, Cold Fusion, Postures or similar server application, also in communication with operating system software  30  and database engine  42 . 
         [0033]    Optional HTTP server application  44  allows computing device  12  to act as a conventional http server, and thus provide a plurality of HTTP pages for access by network interconnected computing devices, such as end-user computing devices  14 . HTTP pages that make up these pages may be implemented using one of the conventional web page languages such as hypertext mark-up language (“HTML”), Java, javascript or the like. These pages may be stored within files  48 . 
         [0034]    Analysis software  46  adapts computing device  12 , in combination with database engine  42  and operating system software  40 , to function in manners exemplary of embodiments of the present invention. Analysis software  46  may analyse stored user queries, and store analysis results to database  30 . Results may be further used to generate reports or other representation of the analysis by way of presentation component  50  and/or or present these to users by way of presentation component  50 , or to users by way of HTTP pages, or otherwise. Analysis software  46  may for example, include suitable CGI or Perl scripts; Java; Microsoft Visual Basic application, C/C++ applications; or similar applications created in conventional ways by those of ordinary skill in the art. 
         [0035]    HTTP pages provided to computing devices  14  in communication with computing device  12  may provide permitted users at devices  14  access to analysis software  46 . The interface may be stored as HTML or similar data in files  48 . 
         [0036]    Of course, any of the above components (e.g. software components, database, etc.) may be distributed over multiple computing devices. 
         [0037]    An example organization of database  30  is illustrated in  FIG. 3 . As illustrated, example database  30  includes three tables: query table  32 ; word table  34 ; and word cluster table  36 . A tabulated word cluster count for each unique word cluster in word table  34  may be stored in a fourth table  38 . 
         [0038]    As illustrated, each entry of query table  32  may include a query (QUERY—in ASCII or similar text format); an identifier of a response that was returned to the query (RESPONSE_ID); the date of the query (DATE_STAMP); and a unique numerical identifier of the query (QUERY_ID). As will become apparent, each query stored in queries table  32  is used to populate WORDS table  34 , and COLLOCATION table  36 . In particular, each word in each query is used to create an entry in WORDS table  34 . Each entry in WORDS table  34  identifies a word used in a query (WORD—in ASCII or similar text format); the query that is the source of the word (by numerical query identifier in QUERY_ID); and a unique identifier of the word (in WORD _ID). Word cluster—i.e. words, word pairs (and optionally word triplet, quadruples, etc.) of each query are stored in COLLOCATION table  36 . The identity of the word cluster (i.e. word, word pair, triplet, etc. in ASCII or similar may be stored in WORD_CLUSTER). Again, in which query (in QUERY_ID) a particular word cluster may be found, as well as the individual words within the word cluster (WORD_ID_ 1 , WORD_ID_ 2 , WORD_ID_ 3  . . . —as referenced to table  34 ) may be stored in table  36 . Each word cluster may also be uniquely numerically identified in CLUSTER_ID. Additionally, for each unique word cluster in table  36 , a count may be stored in table  38  (COUNT) along with an identity of the cluster in ASCII (in WORD_CLUSTER). 
         [0039]    Now, in operation, analysis software  46  processes each stored query in database  30 , to identify word clusters (in the illustrated example collocated word pairs) as illustrated in  FIG. 4 . Specifically, for each entry of interest in table  32 , the text is retrieved in block S 402  and normalized in block S 404 . Normalization in block S 404  includes removing punctuation; converting the text to a uniform case (e.g. lower case); and removing contractions (e.g. can&#39;t →cannot). Optionally, common words like “the”, “a”, “an”, and others may be removed from the normalized query. Likewise, words may be stemmed—e.g. or reducing inflected (or sometimes derived) words to their stem (e.g. running, runs →run). Entries of table  32  may be processed as received. 
         [0040]    In block S 406 , each word of the n words in the query may be added to table  34 , and thus tokenized. That is, for each word in the query is added to a separate entry of table  34 . Once all words in a query have been added to table  34 , collocated word pairs within a query are identified. Specifically, in block S 408 , for each word in a query, word pairs of that word and each remaining word within the query are constructed. Specifically for a query of n words (as normalized), collocated word pairs may be constructed by pair the j th  word in the query with the j+1 st , j+2 nd  . . . q th  word, for j=1 to q, in the query. Each word pair so constructed may be stored in COLLOCATION table  36 . For consistency, each word pair in table  36  may be constructed with words in the pair in alphabetical order. As well, the identity of each word in a collocated word pair (by WORD _ID, as stored in table  34 ) may be stored in table  36 . At the conclusion of block S 408 , all the word pairs for an query entry in table  32  will have been added to table  36 . Table  36  will thus contain a list of word clusters (e.g. words, collocated word pairs, etc.) in the collection of queries in database  30 . Steps S 400  may be performed each time a new record is added to table  32 , or on demand for all queries in table  32  that have not been processed. 
         [0041]    In block S 410 , table  38  may be updated with a count of each word pair. Specifically, for any word pair added to table  36 , a record for that word pair in table  38  may be queried (by WORD_CLUSTER) and an associated count (COUNT) may be updated to increase the count for that word cluster by one (1). If the word cluster does not yet exist in table  38 , it may be added. 
         [0042]    Optionally, instead of searching for collocated pairs, software  46  may search for other word clusters, such as collocated triplets, or quadruples, or a combination of pairs and triplets, or pairs, triplets and quadruples. Alternatively, software  46  may also search for single words in the queries. Again, single words may be added to table  36 . 
         [0043]    In the embodiment of  FIGS. 3 and 4 , word clusters include any two (or more) word pairs that may be formed from a particular query, regardless of how proximate those words are within their associated query. 
         [0044]    In an alternate embodiment, analysis software  46  processes each stored query in database  30 , to identify word clusters formed as one or more adjacent words in the query, as illustrated in  FIG. 6 . A simplified database schema as depicted in  FIG. 5  may be used to store analysis results. Specifically, for each new query entry in table  132 , the text is retrieved in block S 602 , normalized in block S 604 , and tokenized in block S 606  as described with reference to  FIG. 4 . 
         [0045]    The tokenized words in the query may be temporarily stored—in an array or other data structure. Once all words in a query have been added to the data structure, word clusters representing collocated words—in the form of adjacent word pairs, adjacent word triplets, or four five or more adjacent words, and possible single words—within a query are identified. Specifically, in blocks S 608 -S 616 , for each word in a query, word clusters of that word and its adjacent word; the adjacent two words; adjacent three words; up to the remaining adjacent words in the query are formed. Adjacency is established in a single direction within the query—from left to right. Each word duster so constructed may be stored in a suitable data structure—for example in table  136  ( FIG. 5 ) of database  30 . All clusters of length L, for L=1 to the length of the query k, may be so formed, by repeating block S 608  for all clusters of adjacent words of length 1 to k-j (where j is the position the first word in the clusters within the query, and k is the length of the query). At the conclusion of block S 616 , all word clusters formed of adjacent words in the query may be identified, counted and stored. Table  136  will thus contain a list of word clusters (e.g. adjacent words) in the collection of queries in database  30 , links to associated queries and the correct responses may be stored in table  134 . Steps S 600  may be performed each time a new record is added to table  132 , or on demand for all queries in table  132  that have not been processed. 
         [0046]    Empirically, collocated pairs and triplets provide more useable information for analysis and presentation. If collocation of three, four or more words in a query is assessed, then shorter collocated word sets contained within longer ones need not be retained in table  36  or  136  (e.g. single words or two word sets contained in any set of three collocated words need not be stored). As noted, single words may also be treated as word clusters. 
         [0047]    Of course, other collocation or similar extraction techniques may be used to produce slightly different outputs from the same set of queries. 
         [0048]    In any event, after performing blocks S 400  of  FIG. 4 , or S 600  of  FIG. 6 , table  38 /table  136  of database  30  will include a list of all collocated word clusters (pairs and optionally singletons, triplets, quadruples, etc.) in the collection of queries in database  30 , and the number of occurrences of each word pair in the set of queries stored in table  32 /table  132 . 
         [0049]    This data may be output for visualization by presentation component  50 . For example, the data may be output in CSV or similar format for review by a user. Each word, word pair, etc. and its frequency may be extracted from table  38  and output. Preferably, the data is output as a histogram for further graphical presentation. For example, a histogram of the ten (or twenty—or arbitrarily many) most frequently appearing words or word pairs in table  38 /table  136  may be output as a word cloud. To do so, entries of table  38 /table  136  may be sorted by COUNT field and the desired number of associated word clusters (from the WORD_CLUSTER field) may be provided to visualization component  50 . 
         [0050]    Presentation component  50  may, for example, include a tag cloud generation tool. Example Tag cloud generation tools, include Wordle. Tag clouds typically show more important (i.e. more frequent) terms in larger fonts, or in differing colours. In any event, tag clouds may be used to quickly identify frequently collocated word clusters (i.e. word pairs) in queries stored in database  30 . The tag cloud generation may simply be provided with the word pairs of interest, and their count in database  30 . 
         [0051]    As such, tag clouds may be used to identify themes in queries in database  30 , and thus frequent questions in an associated knowledgebase, or deficiencies in the knowledgebase. 
         [0052]    Conveniently, as word clusters are linked to the queries from which they originate (through QUERY_ID), each word pair as presented in the histogram may be used to further present the underlying queries within the queries in database  30  in which the word pair occurs. To this end, presented CSV data may include the queries from which the word pairs originate. Likewise, the presented tag cloud could include links that result in lists of query terms that contain the word pair. The links, could for example, cause execution of an SQL query on table  132  to retrieve the associated quer(ies) for the word pair. Similarly, each query could further link to the response that was used to answer the query, through for example, the RESPONSE_ID of the record in the QUERIES table, which could further be retrieved through a suitable script. 
         [0053]    An example tag cloud, is depicted in  FIG. 7 . This tag cloud was generated from the following queries in database  30   
         [0000]    
       
         
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 fx idt ouf of balance 
               
               
                   
                 cprref bcc 
               
               
                   
                 eft return debit 
               
               
                   
                 rrs requestor info. 
               
               
                   
                 cprref telephone maintenance 
               
               
                   
                 fx currency code 
               
               
                   
                 pda identification for new account 
               
               
                   
                 sdb remove account 
               
               
                   
                 special arrangement 
               
               
                   
                 cprref telephone maintenance 
               
               
                   
                 bus access to deposited funds 
               
               
                   
                 ips redeem 
               
               
                   
                 ips features of ergic 
               
               
                   
                 poa transaction 
               
               
                   
                 cprref telephone maintenance 
               
               
                   
                 loss report ...... sent link 
               
               
                   
                 nsl asked to change password for Sentra Persaud SP00319 
               
               
                   
                 nsl asked to change password for Sentra Persaud SP00319 
               
               
                   
                 pda reduce cops joint 
               
               
                   
                 IPS issue joint 
               
               
                   
                 cprref telephone maintenance 
               
               
                   
                 pda sign - change name from married to maiden 
               
               
                   
                 dispute 
               
               
                   
                 cprref telephone maintenance .. spoke to her earlier 
               
               
                   
                 tfsa discretionary pricing 
               
               
                   
                 ips reference number 
               
               
                   
                 op password format 
               
               
                   
                 legal 
               
               
                   
                 Bist 
               
               
                   
                 cprref collections 
               
               
                   
                 estate 
               
               
                   
                 cprref visa 
               
               
                   
                 bizline visa 
               
               
                   
                 abgl commonly used numbers 
               
               
                   
                   
               
             
          
         
       
     
         [0054]    Optionally, a user interface may allow a user to further refine the analysis, by for example limiting the analysed records to specific dates (by, for example, filtering to records in table  36  resulting from queries in the date range). The user interface may be presented as an HTML page by way of HTTP server  44 . 
         [0055]    In a further example depicted in  FIGS. 9 to 11 , software  46  may be used to generate comparative information to assess themes at particular times or over particular time intervals. 
         [0056]    For example, the analysis of some arbitrary set of queries at time T 1  is illustrated below Table 1. For simplicity, the actual queries from which the word cluster counts illustrated in Table 1 are derived are not illustrated. 
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Cluster (Theme) 
                 Count T1 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 credit card 
                 1100 
               
               
                   
                 credit limit 
                 150 
               
               
                   
                 new credit card 
                 344 
               
               
                   
                 Cancel 
                 111 
               
               
                   
                 cancel credit card 
                 80 
               
               
                   
                 Reward points 
                 219 
               
               
                   
                 Redeem points 
                 75 
               
               
                   
                 increase limit 
                 112 
               
               
                   
                 Application form 
                 2364 
               
               
                   
                 Fraud 
                 908 
               
               
                   
                 fraud protection 
                 700 
               
               
                   
                 Statement 
                 353 
               
               
                   
                 pay balance 
                 143 
               
               
                   
                 current balance 
                 456 
               
               
                   
                 Dispute charge 
                 45 
               
               
                   
                 Second card 
                 2 
               
               
                   
                 lost card 
                 178 
               
               
                   
                 Stolen 
                 123 
               
               
                   
                 Payment 
                 709 
               
               
                   
                 miss payment 
                 42 
               
               
                   
                 one-day offer 
                 347 
               
               
                   
                 TOTAL QUESTIONS 
                 7500 
               
               
                   
                   
               
             
          
         
       
     
         [0057]    Received queries may again be analysed at time T 2  and the resulting twenty-three themes illustrated below are identified Table 2. 
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Cluster (Theme) 
                 Count T2 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 credit card 
                 1367 
               
               
                   
                 credit limit 
                 265 
               
               
                   
                 new credit card 
                 550 
               
               
                   
                 Cancel 
                 89 
               
               
                   
                 cancel credit card 
                 71 
               
               
                   
                 Reward points 
                 645 
               
               
                   
                 Redeem points 
                 456 
               
               
                   
                 increase limit 
                 123 
               
               
                   
                 Application form 
                 2399 
               
               
                   
                 Fraud 
                 523 
               
               
                   
                 fraud protection 
                 213 
               
               
                   
                 Statement 
                 500 
               
               
                   
                 pay balance 
                 177 
               
               
                   
                 current balance 
                 790 
               
               
                   
                 Dispute charge 
                 12 
               
               
                   
                 Second card 
                 67 
               
               
                   
                 lost card 
                 209 
               
               
                   
                 Stolen 
                 167 
               
               
                   
                 Payment 
                 900 
               
               
                   
                 miss payment 
                 67 
               
               
                   
                 one-day offer 
                 1 
               
               
                   
                 spousal card 
                 187 
               
               
                   
                 TOTAL QUESTIONS 
                 8500 
               
               
                   
                   
               
             
          
         
       
     
         [0058]    Of note, the example word cluster counts at T 1  are obtained from an analysis of 7500 queries. Example word cluster counts at T 2  are obtained from an analysis of 8500 queries. 
         [0059]    As described, queries at T 1  and T 2  are identified. Queries at T 1  and at T 2  may actually represent queries received over some time interval with T 1  and T 2  equal to T 1f -T 1i  and T 2f -T 2i , respectively, where T 1i , T 2i  represent the beginning of the intervals T 1  and T 2 , respectively and T 1f  and T 2f  represent the end of those intervals T 1  and T 2 , respectively. Corresponding records may be retrieved from database  30 , and steps S 400  may be performed. 
         [0060]    Tables  234  and  236  depicted in  FIG. 8 , like table  134  ( FIG. 5 ) may be populated for intervals T 1 , T 2  and thus would include word/cluster counters counts specific to the interval T 1 , T 2 . As well, the interval may be stored in table  234 . 
         [0061]    The identified themes for intervals T 1  and T 2  may be visualized as suitable histograms depicted in  FIGS. 9 and 10 . Again, visualization component  50  may be used to generate the histograms. Notably histograms of  FIGS. 9 and 10  are in the form of word clouds (in the form of bubbles) and depict more prominent themes in larger font (or as larger graphical sets—i.e. bubbles), with less prominent themes depicted in smaller font (or as smaller graphical sets). 
         [0062]    Now, interestingly, in order to further analyse the data at times T 1  and T 2 , a histogram of change or deltas (Δ) from T 1  to T 2  may also be calculated and presented. 
         [0063]    In order to meaningfully calculate such a delta, the relative change in counts from time/interval T 1  and T 2  may be determined. To do this, absolute counts at T 1  may be normalized taking into account that the analysis at T 1  results from an analysis of 7,500 queries. Counts at T 2  can be similarly normalized taking into account that the analysis at T 2  reflects 8,500 queries. 
         [0064]    Thus, a measure of the relative difference for any count of a word cluster from T 1  to T 2  for any word cluster (e.g word, word pair, triplet, etc.) may be expressed as 
         [0000]    
       
         
           
             
               
                 
                   CountT 
                   2 
                 
                  
                 
                   ( 
                   
                     Cluster 
                     i 
                   
                   ) 
                 
               
               
                 TotalCountT 
                 2 
               
             
             - 
             
               
                 
                   CountT 
                   1 
                 
                  
                 
                   ( 
                   
                     Cluster 
                     i 
                   
                   ) 
                 
               
               
                 TotalCountT 
                 1 
               
             
           
         
       
       
         where CountT 2 (Cluster i ) is the raw count of a specific word cluster—Cluster i  at T 2  and CountT 1 (Cluster i ) is the raw count of the same specific word cluster—Cluster i  at T 1 . TotalCountT 1 , TotalCountT 2 , represent the total number of queries analysed at/for intervals/times T 1  and T 2 , respectively. 
       
     
         [0066]    The results are illustrated below in TABLE 3. 
         [0000]    
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Cluster (Theme) 
                 Count T1 
                 Count T2 
                 Raw Delta 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 credit card 
                 1100 
                 1367 
                 0.014156863 
               
               
                 credit limit 
                 150 
                 265 
                 0.011176471 
               
               
                 new credit card 
                 344 
                 550 
                 0.018839216 
               
               
                 Cancel 
                 111 
                 89 
                 −0.004329412 
               
               
                 Cancel credit card 
                 80 
                 71 
                 −0.002313725 
               
               
                 reward points 
                 219 
                 645 
                 0.046682353 
               
               
                 redeem points 
                 75 
                 456 
                 0.043647059 
               
               
                 increase limit 
                 112 
                 123 
                 −0.000462745 
               
               
                 application form 
                 2364 
                 2399 
                 −0.032964706 
               
               
                 Fraud 
                 908 
                 523 
                 −0.059537255 
               
               
                 fraud protection 
                 700 
                 213 
                 −0.06827451 
               
               
                 Statement 
                 353 
                 500 
                 0.011756863 
               
               
                 pay balance 
                 143 
                 177 
                 0.001756863 
               
               
                 current balance 
                 456 
                 790 
                 0.032141176 
               
               
                 dispute charge 
                 45 
                 12 
                 −0.004588235 
               
               
                 second card 
                 2 
                 67 
                 0.007615686 
               
               
                 lost card 
                 178 
                 209 
                 0.000854902 
               
               
                 Stolen 
                 123 
                 167 
                 0.003247059 
               
               
                 Payment 
                 709 
                 900 
                 0.01134902 
               
               
                 miss payment 
                 42 
                 67 
                 0.002282353 
               
               
                 one-day offer 
                 347 
                 1 
                 −0.04614902 
               
               
                 spousal card 
                 0 
                 187 
                 0.022 
               
               
                 TOTAL QUESTIONS 
                 7500 
                 8500 
               
               
                   
               
             
          
         
       
     
         [0067]    As will be appreciated, the relative difference may be more directly calculated as 
         [0000]    
       
         
           
             
               
                 
                   CountT 
                   2 
                 
                  
                 
                   ( 
                   
                     Cluster 
                     i 
                   
                   ) 
                 
               
               - 
               
                 
                   CountT 
                   1 
                 
                  
                 
                   ( 
                   
                     Cluster 
                     i 
                   
                   ) 
                 
               
             
             
               
                 TotalCountT 
                 2 
               
                
               
                 ( 
                 
                   orTotalCountT 
                   1 
                 
                 ) 
               
             
           
         
       
     
         [0068]    Possibly, the relative difference (raw delta) could be graphically or otherwise presented for further consideration. This calculation, however, over-emphasizes small absolute changes that amount to high relative differences from T 1  to T 2 . 
         [0069]    Put another way, a change of, for example 100/1000 to 300/2000 for one theme is equal in percentage count change to one of 5/1000 to 15/2000 in another theme. The fact that the former theme has raw count values (100, 300) of a larger magnitude than the latter theme (5, 15) means that the change in the former theme is likely more significant and should appear larger in any graphical depiction of change (e.g. theme cloud). 
         [0070]    As such, the relative difference may further scaled logarithmically to de-emphasize small absolute changes in the count for any particular cluster between times T 1  and T 2 . 
         [0071]    To this end, example logarithmic scaling may be performed as follows: 
         [0000]    
       
         
           
             
               scaled 
                
               
                   
               
                
               Δ 
             
             = 
             
               
                 ( 
                 
                   
                     
                       [ 
                       
                         
                           
                             
                               
                                 
                                   
                                     CountT 
                                     2 
                                   
                                    
                                   
                                     ( 
                                     
                                       Cluster 
                                       i 
                                     
                                     ) 
                                   
                                 
                                 
                                   TotalCountT 
                                   2 
                                 
                               
                               - 
                             
                           
                         
                         
                           
                             
                               
                                 
                                   CountT 
                                   1 
                                 
                                  
                                 
                                   ( 
                                   
                                     Cluster 
                                     i 
                                   
                                   ) 
                                 
                               
                               
                                 TotalCountT 
                                 1 
                               
                             
                           
                         
                       
                       ] 
                     
                      
                     log 
                      
                     
                         
                     
                      
                     10 
                      
                     
                       ( 
                       
                         
                           max 
                            
                           
                             ( 
                             
                               
                                 
                                   
                                     
                                       
                                         Count 
                                         1 
                                       
                                        
                                       
                                         ( 
                                         
                                           cluster 
                                           i 
                                         
                                         ) 
                                       
                                     
                                     , 
                                   
                                 
                               
                               
                                 
                                   
                                     
                                       CountT 
                                       2 
                                     
                                      
                                     
                                       ( 
                                       
                                         cluster 
                                         i 
                                       
                                       ) 
                                     
                                   
                                 
                               
                             
                             ) 
                           
                         
                         1.5 
                       
                     
                   
                   
                     max 
                      
                     
                       ( 
                       
                         
                           
                             
                               CountT 
                               1 
                             
                              
                             
                               ( 
                               
                                 Cluster 
                                 i 
                               
                               ) 
                             
                           
                           
                             TotalCountT 
                             1 
                           
                         
                         , 
                         
                           
                             
                               CountT 
                               2 
                             
                              
                             
                               ( 
                               
                                 Cluster 
                                 i 
                               
                               ) 
                             
                           
                           
                             TotalCountT 
                             2 
                           
                         
                       
                       ) 
                     
                   
                 
                 ) 
               
               3 
             
           
         
       
     
         [0072]    Notably, 
         [0000]    
       
         
           
             max 
              
             
               ( 
               
                 
                   
                     
                       CountT 
                       1 
                     
                      
                     
                       ( 
                       
                         Cluster 
                         i 
                       
                       ) 
                     
                   
                   
                     TotalCountT 
                     1 
                   
                 
                 , 
                 
                   
                     
                       CountT 
                       2 
                     
                      
                     
                       ( 
                       
                         Cluster 
                         i 
                       
                       ) 
                     
                   
                   
                     TotalCountT 
                     2 
                   
                 
               
               ) 
             
           
         
       
       
         represents the maximum of the ratio of counts (expressed as a fraction of the total queries being counted) for the themes (clusters) at T 1  and T 2 . 
       
     
         [0000]    
       
         
           
             [ 
             
               
                 
                   
                     
                       CountT 
                       2 
                     
                      
                     
                       ( 
                       
                         Cluster 
                         i 
                       
                       ) 
                     
                   
                   
                     TotalCountT 
                     2 
                   
                 
                 - 
                 
                   
                     
                       CountT 
                       1 
                     
                      
                     
                       ( 
                       
                         Cluster 
                         i 
                       
                       ) 
                     
                   
                   
                     TotalCountT 
                     1 
                   
                 
               
               
                 max 
                  
                 
                   ( 
                   
                     
                       
                         
                           CountT 
                           1 
                         
                          
                         
                           ( 
                           
                             Cluster 
                             i 
                           
                           ) 
                         
                       
                       
                         TotalCountT 
                         1 
                       
                     
                     , 
                     
                       
                         
                           CountT 
                           2 
                         
                          
                         
                           ( 
                           
                             Cluster 
                             i 
                           
                           ) 
                         
                       
                       
                         TotalCountT 
                         2 
                       
                     
                   
                   ) 
                 
               
             
             ] 
           
         
       
       
         thus calculates the relative difference of the count of Cluster i  between interval T 1  and T 2 . The maximum (max) function is used in the denominator to ensure equal relative difference in either direction (i.e., increasing or decreasing) will have the same absolute value. An increase from 10/100 to 20/150 will thus have the same absolute value as a change from 20/150 to 10/100. 
       
     
         [0075]    Now, log 10(max(countT 1 (Cluster i )countT 2 (Cluster i ))) 1.5  calculates order of magnitude of the larger of the raw count of clusters at T 1  and T 2 . Again, the maximum function ensures that equivalent increases and decrease return equal (absolute) values, The exponent (1.5) acts as a multiplier used to exaggerate the magnitude effect of the logarithm function. 
         [0076]    log 10(max(countT 1 (Cluster i ),countT 2 (Cluster i ))) 1.5  thus acts as a scale factor that is proportional to the count that has changed, and more particular to a multiple of the logarithm of that count, In this was changes In small counts, are scaled by a smaller scale factor than changes in larger counts. As will be appreciated other scale factors could similarly accomplish such scaling 
         [0077]    The additional exponent (3) in 
         [0000]    
       
         
           
             
               [ 
               
                 
                   
                     [ 
                     
                       
                         
                           
                             
                               
                                 
                                   CountT 
                                   2 
                                 
                                  
                                 
                                   ( 
                                   
                                     Cluster 
                                     i 
                                   
                                   ) 
                                 
                               
                               
                                 TotalCountT 
                                 2 
                               
                             
                             - 
                           
                         
                       
                       
                         
                           
                             
                               
                                 CountT 
                                 1 
                               
                                
                               
                                 ( 
                                 
                                   Cluster 
                                   i 
                                 
                                 ) 
                               
                             
                             
                               TotalCountT 
                               1 
                             
                           
                         
                       
                     
                     ] 
                   
                    
                   log 
                    
                   
                       
                   
                    
                   10 
                    
                   
                     ( 
                     
                       
                         max 
                          
                         
                           ( 
                           
                             
                               
                                 
                                   
                                     
                                       countT 
                                       1 
                                     
                                      
                                     
                                       ( 
                                       
                                         cluster 
                                         i 
                                       
                                       ) 
                                     
                                   
                                   , 
                                 
                               
                             
                             
                               
                                 
                                   
                                     countT 
                                     2 
                                   
                                    
                                   
                                     ( 
                                     
                                       cluster 
                                       i 
                                     
                                     ) 
                                   
                                 
                               
                             
                           
                           ) 
                         
                       
                       1.5 
                     
                   
                 
                 
                   max 
                    
                   
                     ( 
                     
                       
                         
                           
                             CountT 
                             1 
                           
                            
                           
                             ( 
                             
                               Cluster 
                               i 
                             
                             ) 
                           
                         
                         
                           TotalCountT 
                           1 
                         
                       
                       , 
                       
                         
                           
                             CountT 
                             2 
                           
                            
                           
                             ( 
                             
                               Cluster 
                               i 
                             
                             ) 
                           
                         
                         
                           TotalCountT 
                           2 
                         
                       
                     
                     ) 
                   
                 
               
               ] 
             
             3 
           
         
       
       
         provides a further numeric spread between the typical lowest computed delta values in any dataset and the typical highest computed data values in any dataset, and preserves the sign of the relative difference. 
       
     
         [0079]    The resulting scaled relative difference values are depicted in TABLE 4 
         [0000]    
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 THEME 
                 Count T 1   
                 Count T 2   
                 Scaled Delta 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 credit card 
                 1100 
                 1367 
                 0.116788553 
               
               
                 credit limit 
                 150 
                 265 
                 2.472987167 
               
               
                 new credit card 
                 344 
                 550 
                 2.304057802 
               
               
                 Cancel 
                 111 
                 89 
                 −0.626512978 
               
               
                 cancel credit card 
                 80 
                 71 
                 −0.184678476 
               
               
                 reward points 
                 219 
                 645 
                 24.31689101 
               
               
                 redeem points 
                 75 
                 456 
                 43.89690274 
               
               
                 increase limit 
                 112 
                 123 
                 −0.000820587 
               
               
                 application form 
                 2364 
                 2399 
                 −0.274493225 
               
               
                 Fraud 
                 908 
                 523 
                 −15.66178099 
               
               
                 fraud protection 
                 700 
                 213 
                 −43.26164271 
               
               
                 Statement 
                 353 
                 500 
                 0.696005015 
               
               
                 pay balance 
                 143 
                 177 
                 0.022993793 
               
               
                 current balance 
                 456 
                 790 
                 4.963088638 
               
               
                 dispute charge 
                 45 
                 12 
                 −4.294992112 
               
               
                 second card 
                 2 
                 67 
                 13.551677 
               
               
                 lost card 
                 178 
                 209 
                 0.00185518 
               
               
                 Stolen 
                 123 
                 167 
                 0.164269198 
               
               
                 Payment 
                 709 
                 900 
                 0.161217407 
               
               
                 miss payment 
                 42 
                 67 
                 0.364765973 
               
               
                 one-day offer 
                 347 
                 1 
                 −65.87005352 
               
               
                 spousal card 
                 0 
                 187 
                 40.15144876 
               
               
                 TOTAL QUESTIONS 
                 7500 
                 8500 
               
               
                   
               
             
          
         
       
     
         [0080]    Conveniently, scaled relative difference values (ScaledDelta(Cluster i )) may be presented by presentation component  50  as a histogram (e.g. word cloud) corresponding to the word clouds generated at T 1  and T 2 . 
         [0081]    An example histogram representing changes in word cluster frequency from T 1  to T 2  is illustrated hi  FIG. 11 . As will be appreciated, word clusters (themes) that are trending—i.e. changing frequency/count. Further conveniently, positive and negative relative differences may be presented in contrasting colours—for example values that are negative (i.e. negative change) may be represented by presentation software  50  using a particular colour or font while changes that are positive may be represented in a further colour or font, thus allowing an analyst to determine those queries that are trending (i.e. increasing in frequency) and those that are falling off (i.e. decreasing in frequency). 
         [0082]    Additionally, scaled relative differences of word cluster counts that have counts equal to (or near) zero in either interval T 1  or T 2  may be marked as new themes (e.g. “spousal card” and “second card” in the above example), or as dropped-off themes (e.g. “one day offer”). Similar scaled relative differences of word cluster counts that are below a threshold need not/are not illustrated. 
         [0083]    Possibly, graphic logos or icons could be used to identify new themes; themes of increasing or decreasing change; or themes that have dropped off. Additionally, mousing or cursing over a particular tag/cloud or bubble may provide additional information about the relative change, and possibly absolute counts reflected by the bubble. 
         [0084]    Conveniently, the histogram in the form of a word cloud/histogram may be viewed in overlying relationship or separately to the histogram/word clouds formed at T 1  and T 2  exemplified in  FIGS. 9 and 10 . 
         [0085]    Of course, the above described embodiments are intended to be illustrative only and in no way limiting. The described embodiments of carrying out the invention are susceptible to many modifications of form, arrangement of parts, details and order of operation. The invention, rather, is intended to encompass ail such modification within its scope, as defined by the claims.

Technology Classification (CPC): 6