Abstract:
When the characteristics of the entire gene group consisting of a plurality of genes are to be grasped, the tendency that the characteristics of a gene having a large number of documents become dominant can be avoided. 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 by searching a database in which corresponding relationships between the search keys and the document groups are recorded. Next, an associative search is performed on a document database with respect to each of the search keys using the obtained document groups as keys to obtain a new set of document groups including the obtained document groups. Characteristic words are extracted from the new set of document groups, and a characteristic word list is sent to the client as mining results.

Description:
CLAIM OF PRIORITY  
       [0001]     The present application claims priority from Japanese application JP 2004-191915 filed on Jun. 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 program 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. An application thereof includes 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. A search is performed to obtain document information using a KeyID assigned to each gene (known genes are registered in a public database and unique IDs are assigned thereto).  
         [0006]     In conventional text mining, 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 related to the KeyID. Next, a characteristic word list is obtained from the text of documents included in the obtained document list, using a characteristic word extraction program. The characteristic word list is transmitted to the client computer, and then the client computer receives and displays the transmitted mining results, thereby ending the mining. Documents related to the text mining include the following Patent Document 1.  
         [0007]     Patent Document 1: JP Patent Publication (Kokai) No. 2004-152035 A  
       SUMMARY OF THE INVENTION  
       [0008]     The conventional text mining mentioned above has the following problems.  
         [0009]     1. The number of related documents is different in each gene. Thus, when the characteristics of the entire gene group consisting of a plurality of genes are to be grasped, the characteristics of a gene having a large number of documents become inevitably dominant.  
         [0010]     2. When a related document group is obtained in each gene, a link table of genes and document information is not necessarily updated. Thus, it is possible to obtain limited, erroneous, or past document information.  
         [0011]     It is an object of the present invention to provide a text mining method in which the problems of the prior art are reduced.  
         [0012]     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 associative search means for performing an associative search on a document database with respect to each of the plurality of the accepted search keys using the obtained document groups as keys and for obtaining a new set of document groups including the obtained document groups, characteristic word list preparation means for extracting characteristic words from the new set of document groups obtained via the associative search means and for preparing a characteristic word list, and output means for outputting the characteristic word list as mining results.  
         [0013]     The number of documents obtained in each search key via the associative search means may be set in advance. The output means may be adopted to output a list of documents obtained via the associative search means as mining results along with the characteristic word list.  
         [0014]     The functions of the text mining server are realized by a computer program.  
         [0015]     According to the present invention, document information used to extract the entire characteristics is adjusted such that the number of documents in each KeyID is a constant standard, so that more correct characteristics can be captured. Moreover, related documents are retrieved when the number of documents is adjusted, so that related documents that cannot be captured using the link table of KeyID/document information can also be obtained. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0016]      FIG. 1  shows a conceptual diagram of a text mining system according to the present invention.  
         [0017]      FIG. 2  shows an example of a KeyID/document link table.  
         [0018]      FIG. 3  shows an example of document information.  
         [0019]      FIG. 4  shows a correspondence table of the numbers of documents after associative search.  
         [0020]      FIG. 5  shows an example of a screen of a KeyID transmission program.  
         [0021]      FIG. 6  shows an example of a screen of a mining results reception program.  
         [0022]      FIG. 7  shows an image diagram of the input/output of an associative search performing program.  
         [0023]      FIG. 8  shows an example of a flow chart of an associative search performing program.  
         [0024]      FIG. 9  shows an example of a flow chart of text mining according to the present invention.  
         [0025]      FIGS. 10A and 10B  show an illustration to describe the difference between a conventional text mining method and the method of the present invention.  
         [0026]      FIG. 11  shows an illustration to compare a conventional method and the present invention using screens of a mining results reception program. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0027]     In the following, an embodiment of the present invention is concretely described with reference to the drawings.  
         [0028]      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 mining results, a text mining server computer  3  (hereafter simply referred to as a server) for performing text mining, a document information database  5  for holding document information, and a KeyID database  6  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 .  
         [0029]     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  2 , 12 A and a mining results reception program  212 B 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 the KeyID transmitted from the client  1 , a document information obtaining program  232 B for obtaining document information from the following document information  232 E using the KeyID, a KeyID/document link table obtaining program  232 C for obtaining the following KeyID/document link table from the KeyID database  6 , a KeyID/document link table  232 D where the corresponding relationship between the KeyID and document information is registered, document information  232 E where document information such as gene-related information is registered, a characteristic word extraction program  232 F for extracting characteristic words from a document obtained from the document information  232 E, a mining results transmission program  232 G for transmitting the results of text mining, an associative search performing program  232 H for performing an associative search on the document information  232 E on the basis of the characteristic words extracted via the characteristic word extraction program  232 F, and a correspondence table  2321  of the numbers of documents after associative search, and a communication port  233  for connecting to the network. The document information  232 E is information of the document information database  5 , and it is held in the server. The KeyID/document link table  232 D is obtained (prepared) from the KeyID database  6  for holding the relation table (or information to be used as a basis of preparation thereof) of the KeyID and document information using the KeyID/document link table obtaining program  232 C and the KeyID/document link table  232 D is held in the server. In practice, information used for text mining is held locally from the databases connected to the network in this manner.  
         [0030]     Also, associative search is a method for retrieving a document by which a document or a document group is used as a key and a document similar to such document or document group is retrieved. The technique of associative search per se is disclosed by JP Patent Publication (Kokai) No. 2002-358315 A, for example. An associative search performing program of the present invention employs a known associative search technique.  
         [0031]      FIG. 2  shows an example of the KeyID/document link table  232 D 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 related thereto. Regarding a gene having a KeyID of “AB1111”, two documents, namely, “Text2” and “Text5” are registered as documents related thereto.  
         [0032]      FIG. 3  shows an example of the document information  232 E stored in the hard disk device  232  of the server  3 . In the document information  232 E, 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.  
         [0033]      FIG. 4  shows an example of the correspondence table  2321  of the numbers of documents after associative search, the table being stored in the hard disk device  232  of the server  3 . The numbers  401  of related documents correspond to the numbers of documents related to each of the KeyIDs of the KeyID/document link table  232 D. In most cases, the numbers  402  of documents after associative search have a fixed value (the maximum value of the number of related documents+5, for example). However, a determination method thereof may be arbitrary as long as “a constant standard is determined”. Also, the numbers  402  of documents after associative search is set such that it does not exceed a value set on the basis of an observed value.  
         [0034]      FIG. 5  shows an example of a screen of the KeyID transmission program  212 A operating on the client  1 . A menu  61 , a KeyID input field  62 , and a transmission button  64  are displayed on the screen. When KeyIDs are inputted into the KeyID input field  62  (They are inputted as shown by numeral  63 , for example. A plurality of KeyIDs may be inputted), by pressing down the transmission button  64 , the inputted KeyIDs  63  is transmitted to the server  3 .  
         [0035]      FIG. 6  shows an example of a screen of the mining results reception program  212 B operating on the client  1 . The screen is displayed when mining results are transmitted from the server  3 . A menu  71 , a document list  72  of the mining results and a characteristic word list  73  of the mining results are displayed on the screen.  
         [0036]      FIG. 7  shows a conceptual diagram representing the input/output of the associative search performing program  232 H operating on the server  3 . By performing the associative search performing program on a document group  81 , the input documents  81  and a new document group  82  related to the input documents  81  can be obtained.  
         [0037]      FIG. 8  shows an example of a flow chart of the associative search performing program  232 H operating on the server  3 . When the program is initiated, first, an input document group related to one KeyID is received (step  91 A). Next, a characteristic word list is obtained from the input document group using the characteristic word extraction program  232 F (step  91 B). The characteristic word list is a list of words that characterize a document list and an extraction method thereof 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 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.  
         [0038]     Next, the characteristic words in the extracted characteristic word list are connected with OR, and a document search is performed on the document information database  5  to narrow candidates of related documents (step  91 C). The similarity of each document of the results of the OR search and the input document group is calculated (step  91 D). An algorithm for calculating the similarity used in step  91 D may be arbitrary. For example, the SMART method widely employed in the field of similar document search is used. Finally, the input document group and documents of the higher rank in the similarity are outputted at the same time (step  91 E). In this occasion, the number of output documents (=the number of input documents+the number of related documents) is set to be a standard value determined in advance in accordance with the correspondence table  2321  of the numbers of documents after associative search in  FIG. 4 . In this manner, as in the case of “AB1111” shown in  FIG. 2 , for example, even if the number of documents registered in relation to the KeyID is as small as two, it is adjusted to be the number of documents that is equal to a standard determined in advance (30 documents in the example of  FIG. 4 ).  
         [0039]      FIG. 9  shows an example of a flow chart of mining using a text mining system improved in accordance with the present invention. The flow chart corresponds to a conventional text mining process in which step  102 C for performing associative search is inserted.  
         [0040]     First, a plurality of KeyIDs are inputted in the client  1  (step  101 A), and mining is initiated by transmitting the plurality of inputted KeyIDs to the server  3  (step  101 B). The server  3  receives the transmitted KeyIDs (step  102 A), and obtains related documents in each KeyID by comparing the received KeyIDs with the KeyID/document link table  232 D ( FIG. 2 ) (step  102 B). In the following step  102 C, the associative search performing program  232 H is performed on the related documents of each KeyID and the numbers of related documents in each KeyID are adjusted to be the numbers of documents after associative search shown in  FIG. 4 . In this manner, a new document list is obtained. With respect to KeyIDs such that the numbers of documents registered in the KeyID/document link table ( FIG. 2 ) exceed the numbers of documents after associative search shown in  FIG. 4 , the number of documents is not increased through the associative search.  
         [0041]     Next, a characteristic word list is obtained (step  102 D) using the characteristic word extraction program and a document list in which related documents relative to all KeyIDs are merged. The characteristic word list is a list of words that characterize the document list and is obtained using the tf and idf method, for example. The server  3  finally transmits the document list and the characteristic word list to the client  1  as mining results (step  102 E). The client  1  receives and displays the transmitted mining results (step  103 A), thereby ending the mining.  
         [0042]      FIGS. 10A and 10B  show an illustration to describe the difference between conventional text mining and the text mining of the present invention having a step where the numbers of documents are adjusted in each KeyID through associative search.  FIG. 10B  corresponds to a portion of a flow chart (the process of  102  in  FIG. 9 ) of the text mining in the present invention, and  FIG. 10A  corresponds to a portion of a flow chart (the process of  102  in  FIG. 9  in which step  102 C is eliminated) of the conventional text mining. In the illustration, a KeyID group  111 A includes KeyIDs received by the server  3  from the client  1 . A related document group  111 B is a document list obtained by the server  3  using the received KeyIDs and the KeyID/document link table. Twenty three documents are extracted relative to KeyID 1 , three documents are extracted relative to KeyID 2 , and two documents are extracted relative to KeyID 3 . A characteristic word group  111 C is mining results transmitted to the client  1  by the server  3  in the conventional text mining. The second related document group  112 D is a document list obtained via the associative search of the present invention shown in step  102 C of  FIG. 9 . Further, a characteristic word group  112 C is mining results transmitted to the client  1  by the server  3  in the text mining of the present invention.  
         [0043]     In  FIG. 10A , characteristic words are extracted relative to the document group  111 B. However, the number of documents is greatly different in each KeyID (KeyID 1  has twenty three documents, KeyID 2  has three documents, and KeyID 3  has two documents), so that most of the extracted characteristic word list  111 C represents the characteristics of KeyID 1  (p 53 , for example). By contrast, in the present invention shown by  FIG. 10B , the associative search performing program  232 H is performed on the first related document group  111 B, and characteristic words are extracted from the second related document group  112 D in which the number of documents is adjusted in each KeyID. As a result of the numbers of documents that have been adjusted, the characteristics (p 53 ) of KeyID 1  alone has moved down in the list and the entire characteristics (Cancer) has come to the top.  
         [0044]      FIG. 11  shows an illustration to compare the conventional technique and the present invention using screens of the mining results reception program  212 B operating on the client  1 . Reference  121 A shows an example of a screen displaying results via the conventional mining method, and reference  122 A shows an example of a screen displaying results via the mining method of the present invention. Reference  121 B represents a document group list according to the conventional technique, and reference  122 B represents a document group list according to the present invention. Also, reference  121 C represents a characteristic word list according to the conventional technique, and reference  122 C represents a characteristic word list according to the present invention. Reference  122 B shows that a new related document group (New Text1, for example) is obtained as compared with reference  121 B. Also, reference  122 C shows the entire characteristics of KeyIDs as compared with reference  121 C.