JP3425784B2 - Document information retrieval device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information search system, and more particularly to a document information search apparatus in an information search system having a rich database.

[0002]

2. Description of the Related Art FIG. 28 is an explanatory view schematically showing a general information retrieval system. "Producing" means work by a producer (person). Generally, it refers to information quality improvement guidance, promotion of information collection activities with high reusability, integrated support for information retrieval, etc. However, here, it means only manual examination of information. The secondary information generally means information contained in units of keyword, abstract, book title, author name, classification number, and location. Further, the original information generally refers to the substance of the document information itself.

The left hand of the drawing shows the registration of documents, and digitized materials, paper materials, floppy materials, etc. are stored in the original information storage device or shelf by personal computer communication or mail. Alternatively, it is stored in the secondary information storage means through a producing operation. The right hand side of the drawing shows retrieval and request and access to literature information. The user operates the search system to search the required documents, and communicates with the personal computer or directly through the searcher / Q & A.
Access the organization to get the literature. It can be obtained by personal computer communication or mail.

[0004]

By the way, as a main method of the above information retrieval system, a method of referring to or analyzing a keyword in a user's search history and performing some processing has been proposed (for example, Japanese Patent Laid-Open No. 1-2
No. 33517, “Database Search Device”, applicant, Mitsubishi Electric).

However, this system is not so constructed as to automatically generate recommended documents which are considered effective for the user and provide them to the user. Further, conventionally, a recommended literature database is manually created. Furthermore, it is not configured to automatically teach the user what documents are supposed to be of interest to the user. An object of the present invention is to automatically provide recommended information as to what a future-proof document is, without human intervention, and further, it is speculated that the user may be interested even if the user does nothing. It is to teach the literature that is automatically.

[0006]

A first aspect of the present invention is an information search system, wherein a log database 13 for storing a search history of a user, a recommended document database 15 for storing recommended document information, and the log database. Database updating means 24 for searching the database of the information search system and generating or updating the recommended literature database 15 based on the information of the number of appearances and the keyword of the search extracted from the 13 latest information. It is characterized by

A second invention is an information retrieval system,
Log database 13 for storing user search history
Of the information system based on the personal recommendation document area 293 storing the recommended document information for each individual, the search keyword of the individual unit extracted from the latest information of the log database 13, and the information of the number of appearances thereof. The personal database updating means 292 for searching the database and generating or updating the personal recommendation document area 293 is provided.

[0008]

In the information retrieval system, recommended information indicating what documents have potential for the user is automatically provided without any human intervention, and even if the user does nothing.
Documents that are supposed to be of interest to the user are automatically taught.

[0009]

DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 is a block diagram of the first invention.
The description of this configuration will be made with reference to other drawings. Below, the database is “DB” and the keyword is “K
It is abbreviated as "W". An example of a search request input screen from the search system is shown in FIG. Specify a search key and request a document search. The case of FIG. 12 is a case where no KW is designated only by the search request.

In the server device 2, the log DB analysis unit 22 analyzes the log DB shown in FIG. 4, for example, and counts and classifies the KW and the number of appearances for each type of the second command. In the second command in the figure, AU is the author's name, TI
Indicates the title, JN indicates the source, and PY indicates the year of publication. The latest KW DB updating means 23 is the latest KW DB as shown in FIG.
In the configuration, the latest KW DB based on the above analysis results
To update.

The updating means 24 of the recommended literature DB is the author name (AU), title (TI), and DB in the latest KW DB shown in FIG.
For the source (JN), the year of publication (PY), and the keyword (KW), the document DB is searched by using a predetermined number of items (for example, the author's top 20 and the title's top 50). A search is performed, a hit is extracted, and the recommended document DB shown in FIG. 8 is updated.

The search means 25 searches the recommended document DB shown in FIG. 8 with the above search key and stores the document number of the hit document. However, if no search key is specified during the above-mentioned search request, all document numbers in the recommended document DB are stored. In the client device 4, the search result output means 46 causes the display device 6 to display the search result, as shown in FIG. That is, each element (author / editor, article name / book title, source, etc. is displayed in order. However, as shown in FIG. 13, if no search key is specified during the above search request, the author name is displayed. , KW with the highest number of appearances by title, title, etc.
The results are displayed in the order of the documents hit by.

The document requesting unit 41 designates any document from the above search results. In this case, as internal processing,
You will be requested by the reference number. The document reading means 28 of the server device 2 retrieves and retrieves the document corresponding to this document number from each document in the document DB. FIG. 2 is a process flowchart of the server device side of the first invention. When a search key is input from the search system and a document search is requested, the server device receives the search key (S1), analyzes the log DB, and counts and classifies the keywords and the number of appearances thereof for each type of the second command. (S2). Then, based on the above analysis result, the latest KW DB is updated (S3), and the latest KW DB is updated.
With respect to the author name, title, etc. in the above, the document DB is searched using the documents up to a predetermined upper number as search keys (S4).
Further, when the search key is designated, the recommended document DB is searched with the search key and the document number of the hit document is stored. On the other hand, if the search key is not specified, all the document numbers in the recommended document DB are stored (S5). Then, each element (author name / editor, article name / book title, source, etc.) of the document corresponding to the stored document number is searched from the recommended document DB and transmitted (S6).

FIG. 3 is a flowchart of the search request receiving process in the process of FIG. As shown in the figure, the search key is received from the client side (S1), and this search key is stored in the memory (S2). FIG. 4 is a configuration example of the log DB. Log DB has a well-known information retrieval system
It is a log that records when and what you tried to search. The second command is AU (author name), T
It is a command that means a type such as I (title), JN (source), PY (announcement year), and the like. The first command is SEA
These are commands, AND, OR, etc. that mean search execution (abbreviation of SEARCH).

FIG. 5 is a flow chart of the log DB analysis process of the process of FIG. First, open the log DB (S
1) In the opened log DB, the keyword and the number of appearances of the keyword are counted for each type (AU, JN, etc.) of the second command and stored in the memory (S2). Then, the log DB is closed (S3). As described above, FIG. 6 is an example of the structure of the latest KW DB, where AU is the author name, TI is the title, JN is the source, and PY is the year of publication.

FIG. 7 is a process flowchart of updating the latest KW DB in the process of FIG. The latest KW DB is opened (S1), and each category (AU, T) in the latest KW DB is opened.
I, JN, PY, KW) and the data of the number of appearances of the keyword are initialized (S2). Next, the keywords stored in the memory in the previous log analysis process and the number of occurrences thereof are compared with the type (AU, TI, JN, PY, K) of the second command.
Each category (A in the latest KW DB corresponding to W)
(U, TI, JN, PY, KW) are set in the storage area for keywords and the number of appearances in descending order of the number of appearances (S3). Then, the DB of the latest KW is closed (S4).

FIG. 8 shows an example of the structure of the recommended document DB, as described above. FIG. 9 is a flowchart of the recommended document DB update process of the process of FIG. First, the DB of the latest KW is opened (S1), and then, for each category (AU, TI, JN, PY) in the DB of the latest KW, the keywords whose appearance numbers are up to the following top numbers are used as search keys. It is stored in the memory (S2). As an example, top 20 for AU (author name), top 50 for TI (title), top 20 for JN (source), top 10 for PY (announcement year),
And Then, the latest KW DB is closed (S3,
The document DB is opened (S4). With the search key that is the keyword with the highest number of occurrences stored in the memory further ahead,
The document DB is searched and the hit document data is stored in the memory (S5).

Then, the document DB is closed (S6),
The recommended document DB is opened (S7), and the recommended document DB is initialized (S8). Further, the document data stored in the previous memory is set in the recommended document DB (S9), and the recommended document D is set.
B is closed (S10). FIG. 10 is an example of the search request input screen as described above. In this case, a search keyword is specified.

FIG. 11 is an example of the search result output screen as described above. FIG. 12 is another example of the search request input screen as described above. In this case, the search keyword is not specified. FIG. 13 is another example of the search result output screen, as described above. FIG. 14 is a flowchart of the search process of the process of FIG. First, recommended literature DB
Is opened (S1), and then it is determined whether the search key stored in the memory in the previous search request receiving process is empty (S1).
2). If it is not empty, the recommended document DB is searched with this search key and the hit document data is stored in the memory (S3). If it is empty, all the document data in the recommended document DB is stored in the memory (S4). The fact that this search key is "empty" means that the client side, that is, the user side, has not specified any search key.

FIG. 15 is a flowchart of the search result transmitting process of the process of FIG. The document data stored in the memory in the previous search processing is transmitted to the client side (S1).
FIG. 16 is a block diagram of the second invention. First, as shown in FIG. 25, access commands to the search system,
Enter the personal ID (yyyyyyy), password (???????), etc., and request the server to connect.

The connection request receiving means 290 uses the personal ID (yyy
yyyy), password (???????), etc. are received. Further, the log DB analysis unit 22 follows the received personal ID as shown in FIG.
The log DB as shown in is analyzed, and the keywords and the number of appearances thereof are counted and classified for each type of the second command corresponding to the individual ID.

The individual KW DB updating means 291 updates the individual KW DB shown in FIG. 21 based on the above analysis result. The recommended document generation means 292 searches the document DB for the author name, title, source, and year of publication of the latest KW DB using the following top numbers as search keys, and extracts hits. They are saved in the recommended document generation area (293) as recommended documents. That is, the author name (A
U): Top 20; Title (TI): Top 50; Source (JN): Top 20; Publication Year (PY): Top 10.

The search result output means 46 displays the author name (A
U), the title (TI), the source (JN), and the year of publication (PY), the results are displayed on the display device 6 in the order of documents hit by the keywords that appear most frequently. FIG. 17 is a processing flowchart of the server device side of the second invention. First, an access command, a personal ID, a password, etc. are input, and a connection request is made to the server device (S1). Next, according to the received personal ID, the log DB is analyzed and the second corresponding to the personal ID
The keywords and the number of appearances thereof are counted and classified for each command type (S2). Next, the DB of the individual KW is updated based on the above analysis result (S3).

Further, as a process of generating recommended documents, the author's name (AU), title (TI), source (JN), and year of publication (PY) in the DB of the latest KW are each up to the following upper ranks. Search the document DB using the item as a search key (S
4). Finally, the search result is transmitted (S5). FIG. 18 is a flowchart of the connection request process of FIG. Receive the personal ID, password, etc. from the client side (S
1) The personal ID, password, etc. are stored in the memory (S2).

FIG. 19 is a block diagram showing an example of the log DB shown in FIG. Similarly to the above, the second command has the following meanings. That is, author name (AU), title (TI), source (J
N) and the year of publication (PY). FIG. 20 shows the log D of FIG.
It is a B analysis processing flowchart. First, the log DB is opened (S1), and then the type of the second command in the opened log DB corresponding to the personal ID and password stored in the memory in the previous connection request reception process. (AU, TI, JN, PY, KW) The keywords and the number of appearances thereof are counted and stored in the memory (S
2). Then, the log DB is closed (S3). The log DB and the second command are the same as those described above, and will not be described.

FIG. 21 shows an example of the DB structure of the personal KW shown in FIG. The DB of the individual KW is a DB that stores the keywords and the number of appearances of each category (AU, TI, JN, PY, KW) for each category (AU, TI, JN, PY, KW) in the descending order of the number of appearances, as the analysis result of the DB. FIG. 22 is a flowchart of the DB update process for the individual KW shown in FIG. First, the personal KW DB is opened (S1), and then the keyword and number of occurrences data for each category (AU, TI, JN, PY, KW) in the latest KW DB is initialized. (S
2). Next, the keywords stored in the memory in the log DB analysis process described above and the number of occurrences of the keywords are compared with each category (in the DB of the individual KW for the second command type (AU, TI, JN, PY, KW)). AU, TI, JN, PY, K
The keyword and the number of appearances for each W) are set in the storage area in descending order of the number of appearances (S3). Then, the DB of the individual KW is closed (S4).

FIG. 23 shows an example of the structure of a recommended document temporarily generated in the recommended document generating area 293. Recommended literature DB
Is a collection of only the documents including the key with the highest number of appearances (frequency of use) in the DB of the individual KW. FIG. 24 shows FIG.
8 is a flowchart of a recommended document generation process shown in FIG. First, the personal KW DB is opened (S1), and each category (AU, TI, JN, PY, K) in the personal KW DB is opened.
W) is stored in the memory with the keywords having the following highest numbers of occurrences as search keys (S).
2). That is, as an example, the author name (AU) is the top 20, the title (TI) is the top 50, and the source (JN) is the top 2
Up to 0, and the top 10 in the announcement year (PY). Then, the DB of the individual KW is closed (S3). Next, the document DB is opened (S4). The document DB is a DB that is generally handled by a known information search system in units of items such as document number, author / editor, article name / book title, and the like.

Next, the document DB is searched with the search key stored in the previous memory, which is a keyword with the highest number of appearances,
The hit document data (recommended document) is stored in the memory (S5). Then, the document DB is closed (S
6). FIG. 25 is an example of the input screen for the personal ID and the like at the time of connection, as described above. In the case of the second invention, no search keyword is designated.

FIG. 26 is an example of the search result output screen as described above. FIG. 27 is a flowchart of the search result transmission process shown in FIG. The document data (recommended document) previously stored in the memory is transmitted to the client side (S1).

[0030]

As described above, according to the present invention,
It automatically provides recommendation information as to what documents have future potential without human intervention, and even if the user does not do anything, it is possible to automatically teach documents that are assumed to be of interest to the user. effective.

[Brief description of drawings]

FIG. 1 is a block diagram of a first invention.

FIG. 2 is a processing flowchart on the server device side of the first invention.

FIG. 3 is a flowchart of a search request receiving process in the process of FIG.

FIG. 4 is a configuration example of a log DB.

5 is a flowchart of a log DB analysis process of the process of FIG.

FIG. 6 is a configuration example of a latest KW DB.

FIG. 7 is a process flowchart of updating the latest KW DB in the process of FIG. 2;

FIG. 8 is a configuration example of a recommended document DB.

FIG. 9 is a flowchart of a recommended document DB update process of the process of FIG. 2;

FIG. 10 is an example of a search request input screen.

FIG. 11 is an example of a search result output screen.

FIG. 12 is another example of a search request input screen.

FIG. 13 is another example of a search result output screen.

FIG. 14 is a flowchart of a search process of the process of FIG.

15 is a flowchart of a search result transmission process of FIG. 2 process.

FIG. 16 is a block configuration diagram of a second invention.

FIG. 17 is a processing flowchart on the server device side of the second invention.

18 is a flowchart of the connection request process of FIG.

19 is a block diagram of an example of a log DB shown in FIG.

20 is a flow chart of the log DB analysis process of FIG.

FIG. 21 is an example of a DB structure of an individual KW shown in FIG.

22 is a flowchart of a DB update process for the individual KW shown in FIG.

FIG. 23 is a configuration example of a recommended document.

FIG. 24 is a flowchart of a recommended document generation process shown in FIG.

FIG. 25 is an example of an input screen for an individual ID and the like when connecting.

FIG. 26 is an example of a search result output screen.

FIG. 27 is a flowchart of the search result transmission process shown in FIG.

FIG. 28 is an explanatory diagram schematically showing a general information search system.

[Explanation of symbols]

1, 3 ... Disk device 2 ... Server device 4 ... Client device 5 ... Input device 6 ... Display device 7 ... Printing device 21 ... Search request receiving means 22 ... Log DB analysis means 23 ... The latest KW DB update means 24 ... Means for updating recommended literature DB 25 ... Search method 26 ... Search result transmitting means 27 ... Literature request receiving means 28 ... Literature reading means 29 ... Literature transmitting means 31 ... Acquired documents 41 ... Literature requesting means 42 ... Literature receiving means 43 ... Literature output means 44 ... Search request means 45 ... Search result receiving means 46. Search result output means 290 ... Connection request receiving means 291 ... Individual KW DB updating means 292 ... Means for generating recommended literature 293 ... Personal recommendation literature area 294 ... Recommended literature

─────────────────────────────────────────────────── ─── Continuation of the front page (56) Reference JP-A-4-1972751 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) G06F 17/30 JISST file (JOIS)

Claims (2)

(57) [Claims] 1. A log database (1) for storing a search history of a user in an information search system.
3) and a recommended document database (1 for storing recommended document information)
5), and based on the information of the search keyword and the number of appearances extracted from the latest information of the log database (13), the database of the information search system is searched, and the recommended literature database (15) is searched. A document information retrieval device comprising: a database updating unit (24) for generating or updating. 2. A log database (1) for storing a search history of a user in an information search system.
3) and a personal recommended literature area (2) for storing recommended literature information for each individual.
93), based on the information of the search keyword and the number of appearances of the individual unit extracted from the latest information of the log database (13), the database of the information system is searched, and the personal recommendation document area (293) ) Is provided with a personal database updating means (292) for generating or updating