Abstract:
The characteristics of the entire gene group including a plurality of genes can be readily grasped. A plurality of search keys are accepted from a client, and a set of document groups each corresponding to the plurality of the accepted search keys is obtained, referring to a table where correspondence relationships between the search keys and the document groups are recorded. Then, a characteristic word list having the levels of relative importance is prepared in each of the search keys, and a characteristic table is prepared on the basis of the characteristic word lists. Finally, characteristic table is sorted, colored, and displayed.

Description:
CLAIM OF PRIORITY  
       [0001]     The present application claims priority from Japanese application JP 2004-284291 filed on Sep. 29, 2004, the content of which is hereby incorporated by reference into this application.  
       BACKGROUND OF THE INVENTION  
       [0002]     1. Field of the Invention  
         [0003]     The present invention relates to a text mining server and a text mining system for analyzing experimental results in life science fields.  
         [0004]     2. Background Art  
         [0005]     In the life science fields, much of information is stored as documents in a text-format, and it has become difficult for users to reach information that is really necessary due to large quantities thereof. In recent years, with the improvement of text mining technologies, means for performing text mining on such documents in a text-format to obtain useful information has been widely used. Applications thereof include an analysis of experimental results of microarrays. The analysis of experimental results of microarrays includes grasping the characteristics of as many as tens to hundreds of genes in some form. In order to realize the analysis, one method obtains related document information in each gene and performs text mining on the entire document group that has been obtained. Known genes are registered in a public database and unique IDs are assigned thereto. A search is performed to obtain document information using such KeyID assigned to each gene.  
         [0006]     Conventional text mining has method 1 where “the KeyID is transmitted from a client computer to a server computer. The server computer compares the received KeyID with a KeyID/document link table and obtains a document list relating to the KeyID. Then, a characteristic word list is obtained from the text of documents listed in the obtained document list, using a characteristic word extraction program” and method 2 where “genes and characteristic words are held in a longitudinal axis and a lateral axis, and the levels of importance of the characteristic words are calculated as elements to display them in a table”, for example. Documents relating to the text mining include the following Patent Document 1.  
         [0007]     Patent Document 1: JP Patent Publication (Kokai) No. 2003-099427 A  
       SUMMARY OF THE INVENTION  
       [0008]     It is desired in text mining that characteristics that become “dominant” in “many” genes of an inputted gene (KeyID) group be “readily” grasped.  
         [0009]     However, in method 1, it is difficult to grasp characteristics that appear in “many” (namely, a plurality of) genes at a time. Also, in method 2, it is difficult to “readily” grasp the characteristics, since the elements of the table are numerals (in other words, further operations are required so as to grasp the characteristics). In some cases of method 2, coloring is performed depending on the level of importance. However, an item indicating the maximum value of the entire table is emphasized, for example, so that it is impossible to determine whether the item indicates the characteristics that are “dominant” in common with “many” genes (in other words, the problem is that values are evaluated not by a relative scale in each KeyID, but by an absolute scale unified in the entire table).  
         [0010]     It is an object of the present invention to provide means for readily grasping characteristics that become dominant in common with many genes of an inputted gene group.  
         [0011]     In order to achieve the aforementioned object, a text mining server of the present invention comprises search key accepting means for accepting a plurality of search keys and means for searching a database in which corresponding relationships between the search keys and document groups are recorded and for obtaining a set of document groups each corresponding to the plurality of the accepted search keys. The text mining server further comprises characteristic word list preparation means for extracting characteristic words from the obtained document groups and for calculating the level of relative importance in each of the plurality of the accepted search keys, thereby preparing a characteristic word list, characteristic table preparation means for preparing a characteristic table by collecting the characteristic word lists of each of the search keys, and output means for outputting the characteristic table as mining results. Further, a client computer comprises characteristic table reception means for receiving the characteristic table prepared in the text mining server and means for sorting and coloring the received characteristic table and for displaying the table.  
         [0012]     The functions of the text mining server and the client computer are realized by a computer program.  
         [0013]     According to the present invention, the characteristics of each gene are displayed using the levels of relative importance, so that important characteristic words in each gene can be grasped. Consequently, characteristics that become dominant in common with many genes can be grasped. Moreover, by performing sorting and coloring, the characteristics that become dominant in common with many genes can be visually captured. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0014]      FIG. 1  shows a conceptual diagram of a text mining system according to the present invention.  
         [0015]      FIG. 2  shows an example of a KeyID/document link table.  
         [0016]      FIG. 3  shows an example of document information.  
         [0017]      FIG. 4  shows an example of a screen of a KeyID transmission program.  
         [0018]      FIG. 5  shows an example of a flow chart of a characteristic word list preparation program.  
         [0019]      FIG. 6  shows an example of a characteristic word list.  
         [0020]      FIG. 7  shows an example of a flow chart of a characteristic table preparation program.  
         [0021]      FIG. 8  shows an example of a characteristic table.  
         [0022]      FIG. 9  shows an example of a sorted characteristic table.  
         [0023]      FIG. 10  shows an example of a colored characteristic table.  
         [0024]      FIG. 11  shows an example of a flow chart of text mining according to the present invention. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0025]     In the following, an embodiment of the present invention is concretely described with reference to the drawings.  
         [0026]      FIG. 1  shows a conceptual diagram of a text mining system according to the present invention. The system shown in this case comprises a client computer  1  (hereafter simply referred to as a client) for inputting and transmitting a KeyID and receiving and coloring a characteristic table, a text mining server computer  3  (hereafter simply referred to as a server) for performing text mining, a document information database  4  for holding document information, and a KeyID database  5  for holding a relation table (or information to be used as a basis of preparation thereof) of a KeyID and document information. Each element is connected via a network  2 .  
         [0027]     The client  1  comprises a terminal device  211  provided with a CPU  211 A and a memory  211 B, a hard disk device  212  where a KeyID transmission program  212 A, a characteristic table reception program  212 B, a characteristic table coloring program  212 C, and a characteristic table sorting program  212 D are stored, and a communication port  213  for connecting to a network. The server  3  comprises a terminal device  231  provided with a CPU  231 A and a memory  231 B, a hard disk device  232  to store a KeyID reception program  232 A for receiving a KeyID transmitted from the client  1 , a document information obtaining program  232 B for obtaining the following document information  232 C from the document information database  4 , a KeyID/document link table obtaining program  232 D for obtaining the following KeyID/document link table  232 E from the KeyID database  5 , a characteristic word list preparation program  232 F for extracting characteristic words from the document information  232 C, a characteristic table preparation program  232 G for preparing a characteristic table where the characteristics of KeyID groups are collected, and a characteristic table transmission program  232 H for transmitting the characteristic table as mining results, and a communication port  233  for connecting to the network.  
         [0028]     The document information  232 C is information of a necessary portion taken from the document information database  4 , and it is held in the hard disk device  232  of the server. The KeyID/document link table  232 E is prepared from the KeyID database  5  for holding the relation table (or information to be used as a basis of preparation thereof) of the KeyID and document information, and the KeyID/document link table  232 E is held in the hard disk device  232  of the server. In practice, information used for text mining is held locally in this manner from the databases connected to the network.  
         [0029]      FIG. 2  shows an example of the KeyID/document link table  232 E stored in the hard disk device  232  of the server  3 . Groups of KeyIDs  31  and document IDs  32  relating to each KeyID are stored. In the table, for example, regarding a gene having a KeyID of “AA0000”, four documents, namely, “Text 1”, “Text 2”, “Text 3”, and “Text 4” are registered as documents relating thereto. Regarding a gene having a KeyID of “AB1111”, two documents, namely, “Text2” and “Text5” are registered as documents relating thereto.  
         [0030]      FIG. 3  shows an example of the document information  232 C stored in the hard disk device  232  of the server  3 . In the document information  232 C, groups of document IDs  41 , authors  42  of each document ID, titles  43 , and text  44  are stored. The document IDs  41  correspond to the document IDs  32  of  FIG. 2 . In this example, although the authors, titles, and text are stored as document information, other information such as abstracts and published years, for example, may be stored as document information.  
         [0031]      FIG. 4  shows an example of a screen of the KeyID transmission program  212 A operating on the client  1 . A menu  51 , a KeyID input field  52 , and a transmission button  54  are disposed on the screen. When KeyIDs are inputted into the KeyID input field  52  (they are inputted as shown by numeral  53 , for example. A plurality of KeyIDs may be inputted), by pressing down the transmission button  54 , the inputted KeyIDs  53  are transmitted to the text mining server  3 .  
         [0032]      FIG. 5  shows an example of a flow chart of the characteristic word list preparation program  232 F operating on the server  3 . The preparation of a characteristic word list is initiated by receiving one of the KeyIDs received via the KeyID reception program  232 A (step  61 A), and then related documents are obtained (step  61 B) by comparing the KeyID with the KeyID/document link table  232 E ( FIG. 2 ). Next, characteristic words are extracted from the related documents that have been obtained and the levels of importance thereof are calculated (step  61 C). Although the calculation method of the levels of importance is arbitrary, examples include a method that employs tf (Term Frequency) and idf (Inverse Document Frequency) widely used in the field of text mining. The tf and idf is a method in which when T(W) represents the total number of documents that include a word W, N represents the total number of documents, and F(W, Q) represents the frequency of appearance of the word W in a document Q, the level of importance of the word W in the document Q is defined by “F(W, Q)*Log[N/T(W)]”. F(W, Q) corresponds to the tf, and Log[N/T(W)] corresponds to the idf. Regarding the characteristic words to be extracted, ten characteristic words are extracted in descending order of the levels of importance, for example. Next, the level of relative importance of each characteristic word is calculated (step  61 D).  
         [0033]      FIG. 6  shows an example of the characteristic word list prepared via the characteristic word list preparation program  232 F. In this list, a KeyID  71 , characteristic words  72  of the KeyID, and the levels of relative importance  73  of the characteristic words are stored. In this case, the level of relative importance is a value obtained by dividing the level of importance (tf and idf values, for example) calculated in each word by the maximum level of importance. Thus, each characteristic word list always contains a word indicating one in the level of relative importance, and the values of the levels of relative importance are not more than one. The characteristic word list is finally sent to the characteristic table preparation program  232 G.  
         [0034]      FIG. 7  shows an example of a flow chart of the characteristic table preparation program  232 G operating on the server  3 . The characteristic table preparation program  232 G prepares a characteristic table from the characteristic word lists prepared as many as the number of the KeyIDs that has been received via the KeyID reception program  232 A. The procedure of the preparation starts by receiving a characteristic word list group prepared via the characteristic word list preparation program  232 F (step  11 A). Next, a list X in which the characteristic words of each KeyID are merged is obtained (step  11 B) and a table Y having the KeyIDs and the list X in a longitudinal axis and a lateral axis respectively, is prepared (step  11 C). Then, the levels of relative importance are inserted as the elements of the prepared table Y on the basis of each characteristic word list (step  11 D).  
         [0035]      FIG. 8  shows an example of the characteristic table prepared via the characteristic table preparation program  232 G. The characteristic table has KeyIDs  81  that are received via the KeyID reception program  232 A in a longitudinal axis, characteristic words  82  in a lateral axis, and the levels of relative importance  83  as elements. The KeyIDs  81  correspond to numeral  71  of  FIG. 6 , the characteristic words  82  correspond to numeral  72  of  FIG. 6 , and the levels of relative importance  83  correspond to numeral  73  of  FIG. 6 .  
         [0036]      FIG. 9  shows an example of the characteristic table sorted via the characteristic table sorting program  212 D. The characteristic table has KeyIDs  91  in a longitudinal axis, characteristic words  92  in a lateral axis, and the levels of relative importance  93  as elements. The objects of sorting are the columns of the characteristic table received via the characteristic table reception program  212 B and the sorting is performed on the basis of the following, for example.  
         [0000]     (i) The sum of the levels of relative importance is calculated in each column and the columns are arranged from the left of the table in descending order of summed values.  
         [0000]     (ii) If the summed values are the same in (i) above, the numbers of the KeyIDs having the level of relative importance greater than zero in each column are compared and a column having a larger number is disposed on the left of the table.  
         [0000]     (iii) If the numbers of the KeyIDs are the same in (ii) above, the maximum values in each column are compared and a column having a higher value is disposed on the left of the table.  
         [0000]     (iv) If all the conditions of (i) to (iii) above are the same, sorting is performed in alphabetical order, for example.  
         [0037]     In accordance with this procedure, word groups indicating dominant characteristic relative to the inputted KeyIDs are collected on the left of the characteristic table, thereby readily enabling the grasping of the characteristics.  
         [0038]      FIG. 10  shows an example of the characteristic table colored via the characteristic table coloring program  212 C. The characteristic table has KeyIDs  111  in a longitudinal axis, characteristic words  112  in a lateral axis, and colored cells  113  as elements.  FIG. 10  corresponds to  FIG. 9  and the cells  113  are colored on the basis of the levels of relative importance  93  of  FIG. 9 . Although a coloring method is arbitrary, a method employing a heat map used for expression analysis of microarrays can be considered, for example. With this coloring, the differences of the intensity of the characteristics can be visually grasped in each column of the characteristic table, and it becomes possible to readily grasp a KeyID that intensely indicates the characteristics in one column.  
         [0039]      FIG. 11  shows an example of a flow chart regarding a procedure from inputting the KeyIDs to obtaining the colored characteristic table, using the present system. The preparation of a characteristic table is initiated by inputting a plurality of KeyIDs in the client  1  (step  101 A), and then the plurality of the inputted KeyIDs are transmitted to the server  3  (step  101 B). The server  3  receives the transmitted KeyIDs (step  102 A) and obtains related documents in each KeyID (step  102 B) by comparing the received KeyIDs with the KeyID/document link table  232 E ( FIG. 2 ). In step  102 C that follows, the characteristic word list preparation program  232 F is executed on the related documents of each KeyID and a characteristic word list ( FIG. 6 ) is prepared in each KeyID. Further, a characteristic table is prepared (step  102 D) from a prepared characteristic word list group using the characteristic table preparation program  232 G, and then transmitted to the client  1  via the characteristic table transmission program  232 H (step  102 E). The client  1  receives the transmitted characteristic table (step  103 A), performs sorting using the characteristic table sorting program  212 D (step  103 B), and performs coloring using the characteristic table coloring program  212 C and displays it (step  103 C), thereby ending the flow of the procedure.