Patent Publication Number: US-10311111-B2

Title: Search method and apparatus and storage medium

Description:
TECHNICAL FIELD 
     The present invention relates to a search method and apparatus and a storage medium and is suited for use in, for example, a search apparatus for searching among past conversation history documents for conversation history documents whose content is similar to an inquiry from a customer when preparing a response to the inquiry at a call center or the like. 
     BACKGROUND ART 
     There are cases where contact with a customer is recorded and the recorded content is analyzed and utilized for, for example, customer care and product development thereafter. Although there have already been such cases, attention has been drawn to analysis and utilization of documents in which all exchanges of communications between customers and call centers that accept inquiries about products from the customers are recorded (texts including all inquiries, interim status, and final responses and hereinafter referred to as the conversation history document) as an example of utilization of big data. For example, the above-mentioned call centers use past conversation history documents when finding an appropriate solution for a new inquiry. 
     CITATION LIST 
     Patent Literature 
     [Patent Literature 1] Japanese Patent Application Laid-Open Publication No. 2007-304793 
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     Meanwhile, when preparing a response to a new inquiry from a customer, a problem-solving clerk at a call center or the like firstly inputs, for example, keywords to a search system or the like and searches past conversation history documents including such keywords in order to find similar cases, as reference for the response, in the past conversation history documents. Next, the problem-solving clerk reads the details of the past conversation history documents which seem to be similar on the basis of the search results. Then, the problem-solving clerk conducts a history lookup process to judge whether the read conversation history documents are useful for the new inquiry or not. 
     In a search process of them, the past similar conversation history documents are searched by using, for example, a system and whether or not there are any conversation history documents which are effective for preparation of the response is judged on the basis of the search results in subsequent check process, selection process, and judgment process. If any conversation history documents which can be used as reference for the new inquiry exist as a result of the judgment, the response is prepared by referring to such conversation history documents; however, if such conversation history documents do not exist, the aforementioned history lookup process is executed again by, for example, changing search conditions. 
     In order to prepare an effective response to an inquiry from a customer in a short time, it is desirable to reduce the number of executions of this history lookup process. Then, it is desirable to enhance accuracy of responses of the search results in order to reduce the number of executions of the history lookup process. 
     In this case, inventions which aim to reduce the number of executions of such history lookup process have conventionally existed. For example, it is an object of PTL 1 to omit the search process by a problem-solving clerk and shorten the history lookup process itself by mechanically extracting keywords from the content of communications exchanged between the problem-solving clerk and a customer and searching past conversation history documents including such keywords. 
     However, if such conventional technique is used, the response accuracy of the search result will depend on an expression form of the content of communications exchanged between the problem-solving clerk and the customer. Specifically speaking, the documents including the extracted keywords are searched according to the technique disclosed in PTL 1. Thus, even if these documents are past histories deeply related to the inquiry content, hits will not be obtained as a result of the search unless the keywords match or are similar; and as a result, the history lookup process will be executed again. 
     The present invention was devised in consideration of the above-described circumstances and aims at proposing a search method and apparatus and storage medium capable of enhancing response accuracy of a search result. 
     Means to Solve the Problems 
     In order to solve the above-described problems, provided according to the present invention is a search method executed by a search apparatus for searching among a plurality of conversation history documents, each of which includes an inquiry from a customer and a response to the inquiry, for the conversation history documents whose content of the inquiry is similar to an inquiry text representing content of a new inquiry from the customer, wherein the search method includes: a first step executed by the search apparatus classifying the plurality of conversation history documents into a plurality of clusters in accordance with contents; a second step executed by the search apparatus extracting a conversation history document representative of each of the clusters as a representative document for the relevant cluster; and a third step executed by the search apparatus searching for the conversation history documents having a relation with the inquiry text and presenting the representative document for the cluster, to which the conversation history documents detected by the search belong, as a result of the search. 
     Furthermore, provided according to the present invention is a search apparatus for searching among a plurality of conversation history documents, each of which includes an inquiry from a customer and a response to the inquiry, for the conversation history documents whose content of the inquiry is similar to an inquiry text representing content of a new inquiry from the customer, wherein the search apparatus includes: a conversation history document classification unit that classifies the plurality of conversation history documents into a plurality of clusters in accordance with contents; a representative document extraction unit that extracts a conversation history document representative of each of the clusters as a representative document for the relevant cluster; and a document search unit that searches for the conversation history documents having a relation with the inquiry text and presents the representative document for the cluster, to which the conversation history documents detected by the search belong, as a result of the search. 
     Furthermore, provided according to the present invention is a storage medium storing a program to be executed by a search apparatus for searching among a plurality of conversation history documents, each of which includes an inquiry from a customer and a response to the inquiry, for the conversation history documents whose content of the inquiry is similar to an inquiry text representing content of a new inquiry from the customer, wherein the program causes the search apparatus to execute processing including: a first step of classifying the plurality of conversation history documents into a plurality of clusters in accordance with contents; a second step of extracting a conversation history document representative of each of the clusters as a representative document for the relevant cluster; and a third step of searching for the conversation history documents having a relation with the inquiry text and presenting the representative document for the cluster, to which the conversation history documents detected by the search belong, as a result of the search. 
     When this search method and this search apparatus, and the program stored in this storage medium are used, a representative document for a cluster, to which the conversation history documents having the relation with the inquiry text belong, is presented. Therefore, the conversation history document with the inquiry content deeply related to the inquiry text can be presented without depending on, for example, the expression form of the inquiry content of the inquiry text. 
     Advantageous Effects of the Invention 
     A search method and apparatus, and storage medium capable of enhancing response accuracy of a search result can be implemented according to the present invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating an overall configuration of a search apparatus according to first and second embodiments; 
         FIG. 2  is a diagram illustrating a configuration example of a conversation history document; 
         FIG. 3  is a diagram illustrating a configuration example of a conversation history document; 
         FIG. 4  is a diagram illustrating a configuration example of a conversation history document; 
         FIG. 5  is a conceptual diagram illustrating a configuration example of a graph; 
         FIG. 6  is a conceptual diagram for explaining clusters; 
         FIG. 7  is a conceptual diagram for explaining a representative document; 
         FIG. 8  is a conceptual diagram illustrating a configuration example of inter-document relation information; 
         FIG. 9  is a flowchart illustrating a processing sequence for inter-conversation-history-document relation creation processing; 
         FIG. 10  is a conceptual diagram illustrating a configuration example of a similarity score list; 
         FIG. 11  is a flowchart illustrating a processing sequence for conversation history document search processing; 
         FIG. 12  is a schematic diagram illustrating a configuration example of a search text input screen; 
         FIG. 13  is a conceptual diagram illustrating inter-document relation information according to a second embodiment; 
         FIG. 14  is a flowchart illustrating a processing sequence for inter-conversation-history-document relation creation processing according to the second embodiment; 
         FIG. 15  is a block diagram illustrating an overall configuration of a search apparatus according to a third embodiment; 
         FIG. 16  is a conceptual diagram illustrating a configuration example of reference history information; 
         FIG. 17  is a flowchart illustrating a processing sequence for inter-conversation-history-document relation creation processing according to the third embodiment; 
         FIG. 18  is a chart for explaining the inter-conversation-history-document relation creation processing according to the third embodiment; 
         FIG. 19  is a chart for explaining the inter-conversation-history-document relation creation processing according to the third embodiment; 
         FIG. 20  is a flowchart illustrating a processing sequence for conversation history document search processing according to the third embodiment; 
         FIG. 21  is a block diagram illustrating an overall configuration of a search apparatus according to a fourth embodiment; 
         FIG. 22  is a flowchart illustrating a processing sequence for conversation history document search processing according to a fourth embodiment; and 
         FIG. 23  is a conceptual diagram for explaining another embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     An embodiment of the present invention will be described below in detail with reference to drawings. 
     (1) First Embodiment 
     (1-1) Configuration of Search Apparatus According to this Embodiment 
     Referring to  FIG. 1 , the reference numeral  1  represents a search apparatus according to this embodiment as a whole. This search apparatus  1  is configured by including a CPU (Central Processing Unit)  2 , a memory  3 , a storage apparatus  4 , a network interface  5 , an external storage media drive  6 , an input device  7 , and a display device  8  and they are connected to each other via an internal bus  9 . 
     The CPU  2  is a processor for controlling the entire search apparatus  1  as a whole. Moreover, the memory  3  is configured of, for example, a volatile semiconductor memory and is used to retain various kinds of programs such as an operating system (OS: Operating System)  10 . A document management unit  11 , an inter-document relation management unit  12 , and a search execution unit  13  which will be described later are also stored and retained in this memory  3 . Furthermore, the memory  3  is also used as a work memory for the CPU  2 . Therefore, the memory  3  is provided with a work area  14  to be used when the CPU  2  executes each processing. 
     The storage apparatus  4  is configured of, for example, hard disk drives or SSDs (Solid State Drives) and is used to save programs and data for a long period of time. In a case of this embodiment, the storage apparatus  4  stores, for example, conversation history document information  15  including all past conversation history documents, and an inter-document relation information  16  described later. 
     Incidentally, the “conversation history document(s)” in this embodiment is a document(s) (text(s)) which includes an inquiry text representing the content of an inquiry from a customer and a response to the inquiry, records the following items as an example on an inquiry case basis, and includes the following content: 
     1. an inquiry (communication content from a customer to a person in charge such as an operator at a call center); 
     2. an application log or system log collection request (communication content from the person in charge to the customer): 
     3. logs (communication content from the customer to the person in charge): 
     4. a research request (communication content from the person in charge to a product department); 
     5. a research response (communication content from the product department to the person in charge); and 
     6. a final answer (communication content from the person in charge to the customer). 
     Furthermore, the conversation history documents may sometimes include confirmation, questions, and/or requests from the person in charge about the inquiry, and the customer&#39;s response about such confirmation, questions, and/or requests. Furthermore, regarding the conversation history documents, one conversation history document may sometimes include a plurality of inquiries and a plurality of responses.  FIG. 2  to  FIG. 4  illustrate configuration examples of such conversation history documents. 
     The network interface  5  is configured of, for example, an NIC (Network Interface Card) and performs protocol control upon communications with other communication devices via a network  17 . Moreover, the external storage media drive  6  is a drive for a portable storage medium  18 , such as a disk medium like a CD (Compact Disk) or a DVD (Digital Versatile Disk) or a semiconductor memory card like an SD card, and reads data from, and writes data to, the mounted storage medium  18 . 
     The input device  7  is configured of, for example, a keyboard and a mouse and is used by a user to input various kinds of information and commands. Moreover, the display device  8  is configured of, for example, a liquid crystal display device and is used to display various kinds of information and various GUIs (Graphical User Interfaces). 
     (1-2) Various Functions Mounted on Search Apparatus 
     Next, various functions regarding data search, which are mounted on this search apparatus  1 , will be explained. This search apparatus  1  is equipped with an inter-conversation-history-document relation creation function that classifies past conversation history documents into a plurality of clusters according to their content and extracts a conversation history document which is representative of the relevant cluster (hereinafter referred to as the representative document), with respect to each cluster. 
     Practically, this search apparatus  1  retains all past conversation history documents as the conversation history document information  15  in the storage apparatus  4 . Then, the search apparatus  1  compares character components included in the relevant conversation history documents between the conversation history documents included in the conversation history document information  15  stored in the storage apparatus  4  (so-called a “concept search”) regularly (for example, weekly or monthly) or irregularly according to the user&#39;s instruction input via the input device  7  and calculates a degree of similarity between the respective conversation history documents as a numerical value. In the following explanation, this numerical value will be referred to as the similarity score. 
     Furthermore, the search apparatus  1  creates a relation structure between the conversation history documents whose calculated similarity score is equal to or higher than a preset threshold value (hereinafter referred to as the similarity score threshold value). In a case of this embodiment, the search apparatus  1  creates, for example, a graph  20  as illustrated in  FIG. 5  as the relation structure between the conversation history documents. This graph  20  is formed by connecting nodes  21  representing the conversation history documents whose similarity score is equal to or higher than the similarity score threshold value, with lines called edges  22 . 
     Furthermore, the search apparatus  1  classifies all the conversation history documents included in the conversation history document information  15  into a plurality of clusters  23  as illustrated in  FIG. 6  by classifying the thus-created graph  20  on the basis of each feature of character strings included in each conversation history document, that is, by performing so-called clustering. Subsequently, the search apparatus  1  extracts one conversation history document, as a representative document, from the conversation history documents belonging to the relevant cluster  23  with respect to the individual clusters  23 . 
     The “representative document” herein used in this embodiment indicates a conversation history document whose calculated value representing the relation with other conversation history documents within the relevant cluster is high (that is, whose relation number with other conversation history documents is the highest within the relevant cluster in this embodiment). For example, if a conversation history document including responses described in a larger number of conversation history documents within the relevant cluster exists among the conversation history documents belonging to the cluster, that conversation history document becomes a representative document for the cluster. Specifically speaking, when only four conversation history documents  24 A to  24 D as illustrated in  FIG. 7  belong to a certain cluster, the conversation history document  24 D including responses which are respectively described in the conversation history documents  24 A to  24 C becomes a representative document for the cluster. 
     Meanwhile, when an instruction is issued to search for a response example to an inquiry from a customer, the search apparatus  1  is also equipped with a conversation history document search function that searches for conversation history documents about inquiries whose content is similar to the content of the relevant inquiry, and presents, for example, a representative document for a cluster to which the relevant conversation history documents belong, as a result of the search to the user. 
     Practically, when the input device  7  is operated by the user such as an operator or a problem-solving clerk at a call center to input an inquiry text indicating the inquiry content from the customer, and an instruction to search for a response example to the relevant inquiry, the search apparatus  1  searches the conversation history documents about inquiries whose content is similar to the relevant inquiry text, by means of the concept search. 
     Then, when the search apparatus  1  detects the conversation history documents about the inquiries whose content is most similar to the inquiry text, as a result of this search, it displays a search result screen that indicates a link to a screen displaying a text of the representative document for a cluster, to which the relevant conversation history documents belong, and links to respective screens displaying texts of the conversation history documents whose inquiry content is similar to that of the relevant representative document (for example, the conversation history documents corresponding to the respective nodes  21  connected with a node  21  of the representative document with the edges  22  as illustrated in  FIG. 5 ), on the display device  8 . 
     For example, in the case of an example of  FIG. 7 , when any one of the conversation history documents  24 A to  24 D is detected as a conversation history document whose content is similar to inquiry texts such as an inquiry text stating that “backup of server fails to be completed within specified time limit” and an inquiry text stating that “backup fails to be completed as scheduled,” a search result screen that indicates a link to a screen displaying the text of the conversation history document  24 D that is the representative document for the cluster to which the conversation history documents  24 A to  24 D belong, and links to the respective screens indicating the texts of the conversation history documents  24 A to  24 C whose inquiry content is similar to that of the conversation history document  24 D will be displayed on the display device  8 . 
     A conversation history document classification unit  19  and a search execution unit  13  are stored in the memory  3  for the search apparatus  1  and an inter-document relation information  16  is stored in the storage apparatus  4  as illustrated in  FIG. 1  as means for implementing the above-described inter-conversation-history-document relation creation function and conversation history document search function (these two functions will be hereinafter collectively referred to as the data search function as considered proper). 
     The conversation history document classification unit  19  is a program having a function classifying the conversation history documents included in the conversation history document information stored in the storage apparatus  4  and is configured by including the document management unit  11  and the inter-document relation management unit  12 . 
     The document management unit  11  is a module having a function that extracts the relation between the conversation history documents included in the conversation history document information. This document management unit  11  is configured by including a conversation history document management unit  30  and a conversation history document relation calculation unit  31 . 
     The conversation history document management unit  30  is a module that manages the conversation history documents by, for example, writing data of the conversation history documents as the conversation history document information  15  to the storage apparatus  4 , reading the data of the conversation history documents included in the relevant conversation history document information  15  to the work area  14  for the memory  3 , or deleting unnecessary conversation history documents from the conversation history document information  15 . Moreover, the conversation history document relation calculation unit  31  is a module that calculates the relation between the conversation history documents as the aforementioned similarity score. 
     Furthermore, the inter-document relation management unit  12  is a module having a function that classifies (clusters) the conversation history documents stored in the storage apparatus  4 . This inter-document relation management unit  12  is configured by including a relation structure creation unit  32  and a relation structure analysis unit  33 . 
     The relation structure creation unit  32  is a module that creates the graph  20  described earlier with reference to  FIG. 5  on the basis of the similarity score between the conversation history documents as calculated by the conversation history document relation calculation unit  31  for the document management unit  11  and stores the created graph  20  as the inter-document relation information  16  in the storage apparatus  4 . Moreover, the relation structure analysis unit  33  is a module having a function that classifies the conversation history documents into a plurality of clusters on the basis of the inter-document relation information  16  stored in the storage apparatus  4 . 
     The search execution unit  13  is a program having a function that: searches the conversation history document information  15  for the conversation history documents whose inquiry content is similar to the inquiry text from the customer, which was given from the user; and displays a search result screen including a link to, for example, a screen for displaying the text of a representative document for a cluster, to which the detected conversation history documents belong, on the display device  8 . This search execution unit  13  is configured by including a search text acceptance and analysis unit  34  and a document search unit  35 ; and the document search unit  35  is configured by further including a similar document search unit  36 , a similar cluster detection unit  37 , and a representative document extraction unit  38 . 
     Among these components, the search text acceptance and analysis unit  34  is a module having a function that accepts the inquiry text of the inquiry from the customer, which is input by the user via the input device  7 . Moreover, the similar document search unit  36  is a module having a function that searches the conversation history document information  15  for the conversation history documents similar to the inquiry text accepted by the search text acceptance and analysis unit  34 . 
     Furthermore, the similar cluster detection unit  37  is a module having a function that identifies a cluster including the conversation history documents detected by the aforementioned search; and the representative document extraction unit  38  is a module having a function that, for example, extracts a representative document for each cluster on the basis of the analysis result of the relation structure analysis unit  33  for the inter-document relation management unit  12  and extracts the representative document for the cluster as identified by the similar cluster detection unit  37 . 
     Incidentally, in the following explanation, a processing subject of various processing will be described as a “module”; however, it is a matter of course that the CPU  2  ( FIG. 1 ) executes the relevant processing according to the module. 
     Meanwhile, the inter-document relation information  16  is information used to retain: the relation structure (graph structure) of the conversation history documents, which is created by the relation structure creation unit  32  for the inter-document relation management unit  12  and stored in the storage apparatus  4 ; and the results of clustering executed by the relation structure analysis unit  33  for the inter-document relation management unit  12 . 
     This inter-document relation information  16  has a table structure including an edge ID column  16 A, a start node ID column  16 B, an end node ID column  16 C, and a cluster number column  16 D as illustrated in  FIG. 8 . Then, the edge ID column  16 A stores an identifier (edge ID) assigned to each edge  22  of the graph  20  described earlier with reference to  FIG. 5 . 
     Furthermore, the start node ID column  16 B stores an identifier assigned to a node  21  (hereinafter referred to as the node ID) connected to a start point side of the relevant edge  22 ; and the end node ID column  16 C stores a node ID assigned to a node  21  connected to an end point side of the relevant edge  22 . 
     However, since an undirected graph is used in this embodiment as the relation structure between the conversation history documents as is apparent from  FIG. 5  and there is no concept of a start point and an end point of an edge  22 , merely the node ID of either one of two nodes  21  connected to the relevant edge  22  is stored in the start node ID column  16 B and the node ID of the other node  21  is stored in the end node ID column  16 C and which one of the start point and the end point of the edge  22  the node  21  is connected does not have any particular meaning. 
     Furthermore, the cluster number column  16 D stores an identification number (cluster number) of a cluster to which a conversation history document associated with the node  21  connected to the end point side of the relevant edge  22  belongs. 
     Incidentally, among information of each record (row) of the inter-document relation information  16 , information of the edge ID column  16 A, the start node ID column  16 B, and the end node ID column  16 C is registered by the conversation history document relation calculation unit  31  for the document management unit  11  and information of the cluster number column  16 D is registered by the relation structure analysis unit  33  for the inter-document relation management unit  12  as described later. 
     (1-3) Inter-Conversation-History-Document Relation Creation Processing 
       FIG. 9  illustrates a specific processing sequence for inter-conversation-history-document relation creation processing executed by the conversation history document classification unit  19  and the search execution unit  13  in association with the aforementioned inter-conversation-history-document relation creation function. This inter-conversation-history-document relation creation processing is executed regularly or irregularly upon receiving a processing execution instruction from the user. 
     Regarding the inter-conversation-history-document relation creation processing, the conversation history document management unit  30  for the document management unit  11  firstly reads one conversation history document, which is included in the conversation history document information  15  stored in the storage apparatus  4 , to the work area  14  for the memory  3  and notifies the conversation history document relation calculation unit  31  to that effect (SP 1 ). 
     After receiving such notification from the conversation history document management unit  30 , the conversation history document relation calculation unit  31  compares character components between the conversation history document read to the work area  14  (hereinafter referred to as the target conversation history document) and each of all the conversation history documents other than the target conversation history document (the concept search) and calculates each similarity score (SP 2 ). As a result, when the processing of this step SP 2  is completed, a similarity score list  40  as illustrated in  FIG. 10  is created in the work area  14  for the memory  3 . 
     Next, the conversation history document relation calculation unit  31  registers the relation with the target conversation history document in the inter-document relation information  16  ( FIG. 8 ) with respect to each of all the conversation history documents whose similarity score with the target conversation history document as calculated in step SP 2  is equal to or higher than the similarity score threshold value (for example, 70) (SP 3 ). 
     Specifically speaking, the conversation history document relation calculation unit  31  stores the identifier (node ID) of the node  21  corresponding to the target conversation history document in the start node ID column  16 B ( FIG. 8 ) of the inter-document relation information  16 , also stores the node ID of the node  21  corresponding to the conversation history document, whose similarity score with the target conversation history document is equal to or higher than the similarity score threshold value, in the end node ID column  16 C ( FIG. 8 ) of the inter-document relation information  16 , and stores the edge ID assigned to the edge  22  connecting the nodes  21 , which are respectively associated with the target conversation history document and the conversation history document, in the edge ID column  16 A of the relevant record of the inter-document relation information  16 , thereby registering these conversation history documents in the inter-document relation information  16 . 
     Furthermore, the conversation history document relation calculation unit  31  judges whether or not the execution of the processing from step SP 1  to step SP 3  has been completed by executing the processing on all the conversation history documents, which are included in the conversation history document information  15 , as the target conversation history document (SP 4 ). 
     Then, when a negative result is obtained in this judgment, the conversation history document relation calculation unit  31  notifies the conversation history document management unit  30  to that effect. Subsequently, the processing from step SP 1  to step SP 4  is repeated by sequentially switching the conversation history document, which is read by the conversation history document management unit  30  to the work area  14  for the memory  3  in step SP 1 , to another unprocessed conversation history document. 
     When an affirmative result is eventually obtained in step SP 4  by completing the execution of the processing from step SP 1  to step SP 3  by executing the processing on all the conversation history documents included in the conversation history document information  15  as the target conversation history document, the relation structure creation unit  32  for the inter-document relation management unit  12  is notified to that effect. Then, the relation structure creation unit  32  which has received this notification creates the graph  20  described earlier with reference to  FIG. 5  as the relation structure between the conversation history documents on the basis of the inter-document relation information  16  stored in the storage apparatus  4  and notifies the relation structure analysis unit  33  to that effect (SP 5 ). 
     Then, the relation structure analysis unit  33  which has received this notification classifies the relevant conversation history documents into a plurality of clusters by executing feature value analysis (so-called clustering) of character strings on each conversation history document corresponding to each of two nodes  21  connected via each edge  22  by referring to the graph  20  created in step SP 5 . Then, the relation structure analysis unit  33  stores the cluster number of a cluster, to which the conversation history document associated with the node  21  connected to the end point side of the relevant edge  22 , in the cluster number column  16 D ( FIG. 8 ) of each record of the inter-document relation information  16  on the basis of this clustering results and then notifies the representative document extraction unit  38  for the search execution unit  13  to that effect (SP 6 ). 
     After receiving such notification from the relation structure analysis unit  33 , the representative document extraction unit  38  extracts a representative document for each cluster created in step SP 6  (SP 7 ). In a case of this embodiment, the representative document extraction unit  38  extracts a conversation history document corresponding to a node  21  which has a largest number of connected edges  22  within the relevant cluster (the node  21  whose degree centrality by centrality analysis is the highest), with respect to each cluster as a representative document for the relevant cluster. 
     The representative document for each cluster is extracted as a result of the above-described processing and then this inter-conversation-history-document relation creation processing is terminated. 
     (1-4) Conversation History Document Search Processing 
     Meanwhile,  FIG. 11  illustrates a specific processing sequence for conversation history document search processing executed by the search execution unit  13  in association with the aforementioned conversation history document search function. This conversation history document search processing is executed upon receipt of a search instruction from the user. 
     Practically, after receiving a request to display a search text input screen from the user via the input device  7 , the search text acceptance and analysis unit  34  ( FIG. 1 ) for the search execution unit  13  displays a search text input screen  50  as illustrated in  FIG. 12  on the display device  8  (SP 10 ). 
     This search text input screen  50  is a screen for the user to input an inquiry text from the customer as a search text and is configured by including an inquiry text input field  51  and a search button  52 . Then, on the search text input screen  50 , the user can issue an instruction to the search apparatus  1  to execute a search of the inquiry text as the search text by inputting the inquiry text from the customer to the inquiry text input field  51  by utilizing the input device  7 , and then clicking the search button  52 . 
     Then, after the inquiry text is input to the search text input screen  50  and the search button  52  is clicked as described above, the search text acceptance and analysis unit  34  incorporates the inquiry text as the search text and stores data of the incorporated search text in the work area  14  for the memory  3  (SP 11 ). Then, the search text acceptance and analysis unit  34  notifies the similar document search unit  36  for the document search unit  35  to that effect. 
     After receiving such notification from the search text acceptance and analysis unit  34 , the similar document search unit  36  searches for the conversation history documents including an inquiry text of the content similar to the search text whose data is stored in the work area  14  for the memory  3 , by means of the concept search and notifies the similar cluster detection unit  37  ( FIG. 1 ) of the search results (SP 12 ). 
     Furthermore, after receiving such notification from the similar document search unit  36 , the similar cluster detection unit  37  acquires the cluster ID of a cluster, to which the conversation history documents detected in step SP 12  belong, from the inter-document relation information  16  ( FIG. 1 ) (SP 13 ) and notifies the representative document extraction unit  38  of the acquired cluster ID (SP 14 ). 
     After receiving such cluster ID from the similar cluster detection unit  37 , the representative document extraction unit  38  acquires the representative document for the cluster, to which the relevant cluster ID is assigned, from among the representative documents of the respective clusters extracted in step SP 7  of the inter-conversation-history-document relation creation processing described earlier with reference to  FIG. 9 . Then, the representative document extraction unit  38  displays the search result screen indicating a link to a screen displaying the acquired representative document and links to respective screens displaying some conversation history documents whose inquiry content is similar to that of the relevant representative document, on the display device  8  (SP 14 ). 
     Subsequently, this conversation history document search processing is terminated. 
     (1-5) Advantageous Effects of this Embodiment 
     The search apparatus  1  according to this embodiment as described above: classifies conversation history documents into a plurality of clusters according to the content of the conversation history documents; extracts a conversation history document, which has a largest relation number with other conversation history documents within the relevant cluster, with respect to each cluster as a representative document for the relevant cluster; and searches for the conversation history documents having a relation with the inquiry text upon search of the conversation history documents similar to the inquiry text and presents the representative document for the cluster, to which the conversation history documents detected by the search belong, as the search result to the user. 
     Therefore, this search apparatus  1  can present the conversation history document of the inquiry content deeply related to the inquiry text without depending on, for example, the expression form of the inquiry content of the inquiry text, thereby making it possible to enhance the response accuracy of the search result. Accordingly, an effective response to the inquiry from the customer can be presented in a short period of time by using this search apparatus  1 . 
     (2) Second Embodiment 
     Referring to  FIG. 1 , the reference numeral  60  represents a search apparatus according to a second embodiment as a whole. This search apparatus  60  is configured in the same manner as the search apparatus  1  according to the first embodiment, except that a weight according to strength of the relation between the conversation history documents respectively associated with two nodes  21  connected by the relevant edge  22  is set to each edge  22  of the graph  20  ( FIG. 5 ) created by the inter-conversation-history-document relation creation processing and this weight is used to extract a representative document for each category of the conversation history documents. 
     Practically, in a case of the search apparatus  60  according to this embodiment, inter-document relation information  61  is provided with a weight column  61 E in addition to an edge ID column  61 A, a start node ID column  61 B, an end node ID column  61 C, and a cluster number column  61 D as illustrated in  FIG. 13 . Then, while the same information as the information stored in the edge ID column  16 A, the start node ID column  16 B, the end node ID column  160 , and the cluster number column  16 D of the inter-document relation information  16  according to the first embodiment is stored respectively in the edge ID column  61 A, the start node ID column  61 B, the end node ID column  61 C, and the cluster number column  61 D, the weight assigned to the relevant edge  22  ( FIG. 5 ) is stored in the weight column  61 E. 
     In a case of this embodiment, the weight set to an edge  22  is set as 1/10 of the similarity score between the conversation history documents respectively associated with two nodes  21  connected by the relevant edge  22 . However, the weight of each edge can be set by any means other than this. For example, when the user prepares a response to an inquiry from the customer, the weight of each edge  22  may be set by: storing the user&#39;s history lookup process, that is, in which order the user refers to the conversation history documents when referring to the plurality of conversation history documents other than the representative document presented by the search apparatus  60 ; and setting a high weight to the edge  22  between the conversation history documents which are often followed (that is, to both of which the reference is often made together). In this case, the number of times the reference is made to the conversation history documents together is counted and the count value is set as the weight as it is. 
       FIG. 14  illustrates a specific processing sequence for inter-conversation-history-document relation creation processing according to this embodiment executed by using the inter-document relation information  61  having the aforementioned structure. This inter-conversation-history-document relation creation processing is started regularly or upon receipt of a processing execution instruction from the user in the same manner as the inter-conversation-history-document relation creation processing ( FIG. 9 ) according to the first embodiment. 
     Then, regarding the inter-conversation-history-document relation creation processing in this embodiment, processing from step SP 20  to step SP 22  is executed in the same manner as from step SP 1  to step SP 3  of the inter-conversation-history-document relation creation processing according to the first embodiment. 
     Subsequently, the conversation history document relation calculation unit  31  calculates weight W according to the expression indicated below where S represents a value of the similarity score between the conversation history documents respectively associated with two nodes  21  connected by the relevant edge  22  with respect to each edge  22  registered in the inter-document relation information  61  in step SP 22 .
 
[Math. 1]
 
 W=S/ 10  (1)
 
Then, the conversation history document relation calculation unit  31  stores the calculated weight of each edge  22  in the weight column  61 E of the relevant record of the inter-document relation information  61  (SP 23 ).
 
     Subsequently, regarding the inter-conversation-history-document relation creation processing in this embodiment, processing from step SP 24  to step SP 26  is executed in the same manner as from step SP 4  to step SP 6  of the inter-conversation-history-document relation creation processing according to the first embodiment. 
     Furthermore, a representative document extraction unit  64  for a document search unit  63  of a search execution unit  62  then extracts a document which becomes a center of each cluster created in step SP 26  as a representative document (SP 27 ). In a case of this embodiment, the representative document extraction unit  64  extracts a conversation history document corresponding to a node  21  which has the largest average value of the weight set to each connected edge within the relevant cluster, as a representative document for the relevant cluster with respect to each cluster. 
     As a result of the above-described processing, the representative document for each cluster is extracted in consideration of the weight of the edge  22 . Subsequently, this inter-conversation-history-document relation creation processing is terminated. 
     Regarding the search apparatus  60  according to this embodiment as described above, the weight is set to each edge  22  of the graph  20  according to the strength of the relation between the conversation history documents associated respectively with two nodes  21  connected by the relevant edge  22 . Accordingly, this search apparatus  60  can represent the relation between the conversation history documents more precisely and, therefore, can further enhance the accuracy of the search result more than the search apparatus  1  according to the first embodiment. 
     (3) Third Embodiment 
     The second embodiment has explained about setting the weight to each edge  22  ( FIG. 5 ) of the graph  20  ( FIG. 5 ) created on the basis of the inter-document relation information  16 . Contrarily, in this embodiment, a weight according to reference frequency of the relevant conversation history document is set to each conversation history document and degree centrality of each conversation history document as calculated by means of centrality analysis is corrected on the basis of the weight of each relevant conversation history document, and then a representative document is extracted on the basis of the corrected degree centrality of each conversation history document. By doing so, the representative document which matches the actual situation can be extracted. 
       FIG. 15  in which the same reference numerals as those in  FIG. 1  are assigned to parts corresponding to those in  FIG. 1  illustrates the configuration of a search apparatus  70  according to this embodiment. The search apparatus  70  according to this embodiment is configured in the same manner as the search apparatus  1  according to the first embodiment, except that reference history information  72  is stored, in addition to the conversation history document information  15  and the inter-document relation information  16 , in the storage apparatus  4 , a document search unit  74  for a search execution unit  73  stored in the memory  3  is provided with a reference history registration unit  75 , and a representative document extraction unit  76  for the document search unit  74  has a different function. 
       FIG. 16  illustrate a configuration example of the reference history information  72 . The reference history information  72  is information used to manage the frequency of reference made by the user with respect to each conversation history document and has a table structure composed of a document ID column  72 A and a reference frequency column  72 B. Then, the document ID column  72 A stores an identifier of each conversation history document (hereinafter referred to as the document ID) and the reference frequency column  72 B stores the reference frequency of the relevant conversation history document up to present. This reference frequency is counted up by one by the reference history registration unit  75  for the document search unit  74  every time the reference is made to the relevant conversation history document. 
     Meanwhile,  FIG. 17  illustrates a processing sequence for inter-conversation-history-document relation creation processing executed by the search apparatus  70  according to this embodiment. Regarding this inter-conversation-history-document relation creation processing, processing from step SP 30  to step SP 35  is executed in the same manner as from step SP 1  to step SP 6  of the inter-conversation-history-document relation creation processing according to the first embodiment described earlier with reference to  FIG. 9 . 
     Subsequently, the representative document extraction unit  76  calculates degree centrality of each conversation history document for each cluster created in step SP 35  (SP 36 ). Moreover, after normalizing (by means of division by the total number of nodes) the degree centrality of each conversation history document as calculated in step SP 36  for each cluster, the representative document extraction unit  76  corrects the normalized degree centrality of each conversation history document on the basis of the reference history information  72  and extracts a representative document for the relevant cluster on the basis of the correction result (SP 37 ). 
     Specifically speaking, the representative document extraction unit  76  calculates a corrected value C′ of the normalized degree centrality of each conversation history document according to the expression indicated below, where C represents the normalized degree centrality of the conversation history document as calculated in step SP 36 . R represents the reference frequency of the relevant conversation history document which can be obtained from the reference history information  72 , and TR represents a total value of the reference frequencies of all conversation history documents to which the relevant conversation history document belongs.
 
[Math. 2]
 
 C=C×{ 1+( R/TR )}  (2)
 
Then, the representative document extraction unit  76  extracts a conversation history document which has the largest corrected value C′ of the normalized degree centrality within the cluster, as the representative document for the relevant cluster.
 
     For example, when values of the normalized degree centrality of the respective conversation history documents as calculated in step SP 36  with respect to a certain cluster are as indicated in  FIG. 18  and a total value of the reference frequencies of the cluster to which these conversation history documents belong is 100 times, the values of the normalized degree centrality of these conversation history documents are corrected respectively in step SP 37  as indicated in  FIG. 19 . Accordingly, although the normalized degree centrality of a conversation history document whose document ID is “20140231_0232” is the highest in a stage before the correction ( FIG. 18 ), the normalized degree centrality of a conversation history document whose document ID is “20131005_0002” becomes the highest in a stage after the correction ( FIG. 19 ). Thus, in step SP 37 , this conversation history document whose document ID is “20131005_0002” is extracted as a representative document for this cluster. 
       FIG. 20  illustrates a processing sequence for conversation history document search processing executed by the search apparatus  70  according to this embodiment. Regarding this conversation history document search processing, processing from step SP 40  to step SP 44  is executed in the same manner as the processing from step SP 10  to step SP 14  of the conversation history document search processing according to the first embodiment as described earlier with reference to  FIG. 11 . As a result, a search result screen including respective links to screens respectively displaying some conversation history documents (including the representative document) whose content is similar to the inquiry text input to the search text input screen  50  ( FIG. 12 ) is displayed on the display device  8  ( FIG. 15 ) as described earlier. 
     Subsequently, the reference history registration unit  75  for the document search unit  74  monitors the user&#39;s operation on the search result screen; and when any one of the links to the screens displaying the conversation history documents is clicked on the relevant search result screen, the reference history registration unit  75  updates the reference history information  72  to increase the reference frequency of the relevant conversation history document by one (SP 45 ). 
     Subsequently, when this search result screen is closed, this conversation history document search processing is terminated. 
     The search apparatus  70  according to this embodiment as described above: sets the weight according to the reference frequency of each conversation history document to that conversation history document; corrects the degree centrality of each conversation history document, which is calculated by means of the centrality analysis, on the basis of the weight of the relevant conversation history document; and then extracts the representative document on the basis of the corrected degree centrality of each conversation history document. Therefore, this search apparatus  70  can extract the representative document which matches the actual situation and, therefore, can further enhance the accuracy of the search result more than the search apparatus  1  according to the first embodiment. 
     (4) Fourth Embodiment 
     With the search apparatus  1  according to the first embodiment, search targets of the concept search (targets for comparison of character components with the search text) for the conversation history document search processing ( FIG. 11 ) are all the conversation history documents. However, when all the conversation history documents are used as the search targets of the concept search in this way, there is a possibility that processing time may become long depending on the number of the conversation history documents. 
     Thus, in this embodiment, only representative documents of respective categories of the conversation history documents are used as the search targets of the concept search for such conversation history document search processing. By doing so, it is possible to reduce the number of the conversation history documents used as the search targets and increase the speed of the search. 
       FIG. 21  in which the same reference numerals as in  FIG. 1  are assigned to parts corresponding to those in  FIG. 1  illustrates the configuration of a search apparatus  80  according to this embodiment. This search apparatus  80  is configured in the same manner as the search apparatus  1  according to the first embodiment, except that a document search unit  82  for a search execution unit  81  stored in the memory  3  is configured of only a similar document search unit  83  and a representative document extraction unit  38 . 
       FIG. 22  illustrates a specific processing sequence for conversation history document search processing according to this embodiment. This conversation history document search processing is executed upon receipt of a search instruction from the user. 
     Then, regarding the conversation history document search processing according to this embodiment, processing in step SP 50  and step SP 51  is executed in the same manner as the processing in step SP 10  and step SP 11  of the conversation history document search processing ( FIG. 11 ) according to the first embodiment. 
     Next, the similar document search unit  83  searches for a representative document similar to the search text whose data is stored in the work area  14  for the memory  3 , by means of the concept search (SP 52 ). Incidentally, as for a concept search method used under this circumstance, for example, it is possible to apply a search method of applying a condition like a WHERE phrase of SQL (Structured Query Language) as well as a method of creating a set of documents consisting of only representative documents in advance and performing the search targeted on only such set of documents. 
     Subsequently, the similar document search unit  83  displays a search result screen indicating a link to a screen displaying the representative document detected by the search and links to respective screens displaying some other conversation history documents whose inquiry content is similar to that of the relevant representative document, on the display device  8  (SP 53 ). Then, this conversation history document search processing is terminated. 
     The search apparatus  80  according to this embodiment as described above executes the concept search targeted on only the representative document for each category of the conversation history documents during the conversation history document search processing. Therefore, this search apparatus  80  can reduce the number of the conversation history documents used as the search targets and, therefore, further increase the search speed more than the search apparatus  1  according to the first embodiment. 
     (5) Other Embodiments 
     Incidentally, the aforementioned first to fourth embodiments have described the case where the graph created by the relation structure analysis unit  33  for the inter-document relation management unit  12  is the undirected graph; however, the present invention is not limited to this example and the graph created by the relation structure analysis unit  33  may be, for example, a directed graph  90  as illustrated in  FIG. 23 . In this case, a direction of an edge  92  is a direction towards a node  91  corresponding to a conversation history document detected by the search from the node  91  corresponding to a conversation history document which is a search source; and as for degree centrality of each node  91 , in-degree centrality counting the number of edges  92  directed towards the present node  91  may be applied. 
     Furthermore, the aforementioned first to fourth embodiments have described the case where the degree centrality is used as a method for extracting a representative document for each cluster of the conversation history documents; however, the present invention is not limited to this example and the representative document for each cluster may be extracted by using, for example, centrality other than the degree centrality, such as proximity centrality indicating a point which is close to any points in a graph or medium centrality indicating how important role this has in a graph. In this case, a conversation history document which has a high calculated value representing the relation with other conversation history documents within the relevant cluster may be extracted as the representative document for each cluster. 
     Furthermore, the aforementioned first to fourth embodiments have described the case where information of each cluster of the conversation history documents is stored in a so-called relational database; however, the present invention is not limited to this example and the information of each cluster may be directly retained in, for example, a graph database. However, when each conversation history document is classified into a plurality of clusters, such classification can be conducted at a higher speed by storing the information of each cluster in the relational database. 
     Furthermore, the aforementioned first to fourth embodiments have described the case where the inter-conversation-history-document relation creation processing is executed regularly or irregularly upon receipt of the processing execution instruction from the user; however, the present invention is not limited to this example and, for example, the inter-conversation-history-document relation creation processing may be executed every time a conversation history document is added; and other wide variety of timings can be applied as timing to execute the inter-conversation-history-document relation creation processing. 
     Furthermore, the aforementioned first to fourth embodiments have described the case where the conversation history document classification unit  19  and the search execution units  13 ,  62 ,  73  and  81  are created as separate programs; however, the present invention is not limited to this example and they may be configured as one program. 
     Furthermore, the aforementioned second embodiment has described the case where the weight set to an edge  22  is 1/10 of the similarity score; however, the present invention is not limited to this example and other wide variety of setting methods can be applied as a method of setting the weight of the edge  22 . 
     Furthermore, the aforementioned third embodiment has described the case where the number of times a conversation history document is browsed is counted as the reference frequency of the relevant conversation history document; however, the present invention is not limited to this example and the number of times a conversation history document is actually copied and pasted and used as a response to an inquiry (the number of times it is copied) may be counted as the reference frequency of the relevant conversation history document. As a result of doing so, for example, when a conversation history document other than the representative document is used as a response to a new inquiry more often than the representative document, the reference frequency of the relevant conversation history document increases and the relevant conversation history document will be extracted as a next representative document. Therefore, it is possible to provide the user with information (the search results for new inquiries from customers) that matches the actual situation more closely. 
     Furthermore, instead of automatic counting of the number of times the conversation history documents are used for responses to inquiries from customers by the search apparatus  70 , for example, check boxes may be provided on the search result screen by associating the check boxes respectively with a link to a screen displaying a representative screen, which is displayed on the search result screen, and links to screens displaying other conversation history documents, and the user may check the check box(es) corresponding to the conversation history document(s) which is actually used for an inquiry from a customer, and the number of checks in the check boxes may be counted for each conversation history document. 
     Furthermore, the aforementioned third embodiment has described the case where after the degree centrality of each conversation history document is normalized, the normalized degree centrality of each conversation history document is corrected on the basis of the reference history information  72  and a representative document for the relevant each cluster is extracted on the basis of the corrected result; however, the present invention is not limited to this example and the degree centrality of each conversation history document may be corrected on the basis of the reference history information  72  without normalizing the degree centrality of each conversation history document, and a representative document for the relevant each cluster may be extracted on the basis of the corrected result. 
     INDUSTRIAL APPLICABILITY 
     The present invention can be applied to a wide variety of search apparatuses for searching documents. 
     REFERENCE SIGNS LIST 
     
         
           1 ,  60 ,  70  and  80  search apparatus 
           2  CPU 
           3  memory 
           4  storage apparatus 
           7  input device 
           8  display device 
           11  document management unit 
           12  inter-document relation management unit 
           13 ,  62 ,  73  and  81  search execution unit 
           15  conversation history document information 
           16  and  61  inter-document relation information 
           19  conversation history document classification unit 
           20  and  90  graph 
           21  and  91  node 
           22  and  92  edge 
           23  cluster 
           30  conversation history document management unit 
           24 A to  24 D conversation history documents 
           25 A and  25 B inquiry text 
           31  conversation history document relation calculation unit 
           32  relation structure creation unit 
           33  relation structure analysis unit 
           34  search text acceptance and analysis unit 
           35 ,  74  and  82  document search unit 
           36  and  83  similar document search unit 
           37  similar cluster detection unit 
           38 ,  64  and  76  representative document extraction unit 
           40  similarity score list 
           50  search text input screen 
           72  reference history information 
           75  reference history registration unit