JP2000242662A - Data base preparation device and data base retrieval device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically prepare an index without affecting an index reference by automatically generating a relation degree between words included in data based on the index reference supplied to a data base and performing appropriate weighting to the relation degree further. SOLUTION: A word relation degree map preparation device 7 checks the appearing frequency of the word used by the index reference 4 for the data 1, defines that the simultaneously appearing words are related to each other, calculates the relation degree corresponding to a prescribed calculation formula and obtains a word relation degree map 8. A word importance degree imparting device 10 divides the number of the words appearing in an extract by the number of the words appearing in a title and performs weighting to the words in the title for the portion of the value. An index preparation device 6 temporarily corrects the relation degree of the word relation degree map 8 by weighting and generates the index 9 to the data 1 by using the corrected relation degree.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a database creation apparatus for automatically creating an index of a database, and a database search apparatus for searching a created database while narrowing a category.

[0002]

2. Description of the Related Art FIG. 13 is a block diagram showing a conventional similarity search apparatus. This search device stores a database 17 storing data represented by pairs of attributes and attribute values.
And a first search unit 15 for narrowing down the data amount from the data in the database 17 using a first index before searching for similar data, a similarity range between attribute values and a similarity A second range for calculating a range and performing a similar search on the data searched for by the first search unit 15 based on the second index
A search unit 16, an inference processing unit 14 including a first index changing unit that changes a first index according to a similarity range, and a similarity value of the first index are set, and based on the similarity value. , A first index generator 18 that determines the level of the first index, an input device 11, an output device 12, and an input / output controller 13.

This search device sets a similarity value of a first index, determines a level of the first index based on the similarity value, and sets a similarity based on a range of similarity between attribute values and a second index. The degree is calculated, the first index is changed according to the calculated similarity range, and a similarity search is performed. Such a search device is disclosed, for example, in Japanese Patent Laid-Open Publication No. Hei 6-176072.

FIG. 14 is a block diagram showing a conventional information retrieval apparatus. The search device includes an arithmetic unit 19 for inner product of an index keyword matrix from a keyword memory 24 and a search vector corrected in accordance with the degree of correlation, a partial linearizer 20 for converting the result to a partial linear form, Computing unit 21 for multiplying the output vector of unit 20 and keyword matrix X, normalizing unit 22 for normalizing each element of the product result to 0 and 1, and before feeding back once to computing unit 19 A controller 23 that compares the output vector of the normalizer 22 with the output vector of the normalizer 22, a readout unit 26 that reads out desired data from the database 25 based on the corresponding address of the position of the output vector of the corrected partial linearizer 20,
And a display 27 for displaying the desired data.

[0005] This search device has a configuration shown in FIG. 15. The keyword input from the keyword input unit 28 is stored in the storage unit 33, and the keyword is converted into a numerical vector by the arithmetic unit 24, and the keyword correlation is obtained. Table 3
Referring to FIG. 5, a more relevant keyword corresponding to the degree of correlation is found, and the converter 29 converts the first input keyword into the found keyword. Then, the search device searches the database 36 using the keyword as a new search condition using the search device 30, reads the database 36 using the read device 31, and displays the result selected via the selector 37 on the display device 32. Such a search device,
For example, it is disclosed in JP-A-8-87508.

[0006]

However, in the above-described similarity search apparatus, there is a problem that data must be stored in a state of a pair of an attribute and an attribute value in order to obtain a similarity in word units. Problems such as the importance of attributes, because it is necessary to set goals and samples based on the intuition and experience of the user, or goals and samples that reflect the intentions of the user, resulting in different results depending on the user There is a point.

In the information retrieval apparatus described above, an input keyword is converted into another keyword using a correlation table of keywords indicating a general degree of correlation calculated by some method. Even in the case of data, if the character string is the same word, the correlation becomes the same, and there is a problem that an appropriate result according to the field cannot be obtained. Modifying the correlation table to avoid this would affect the entire keyword space,
Performance is not always improved for all searches.

The present invention has been made to solve the above problems, and automatically generates the degree of relevance between words included in data based on an index criterion given to a database, and furthermore, the degree of relevance. A database creation device that automatically creates an index without affecting index criteria by performing appropriate weighting on a database, and a database search device that searches the database while narrowing down categories. Aim.

[0009]

In order to achieve the above object, the present invention provides a data input device for inputting data to a database, and an index for inputting an index reference defining a configuration serving as an index reference. The reference reading device and, for the input data, check the appearance frequency of the word used in the index criterion, calculate the degree of relevance for words that appear simultaneously, based on the degree of relevance and the index criterion. A word relevance map creating apparatus for creating a word relevance map, and the number of words appearing in a first document of input data is found in a second document serving as a summary or heading of the first document Divided by the number of words to be assigned, and a word importance assigning device for weighting words in the second document by the obtained value, and a word importance assigning device. Using the weighting obtained, the relevance of the word relevance map created by the word relevance map creating device is temporarily corrected, and the corrected word relevance map is used to correct the input data. An index creation device for creating an index.

[0010] According to the present invention, when data is input by the data input device and the index criterion is input by the index criterion reading device, the word relevance map creator sets the input data on the index criterion. The appearance frequency of the used word is checked, the relevance is calculated for the words that appear at the same time, and a word relevance map is created based on the relevance and the index criterion. Also,
The word importance assigning device is obtained by dividing the number of words appearing in the first document of the input data by the number of words appearing in the second document serving as a summary or heading of the first document. The words in the second document are weighted by the value.
Further, the index creation device temporarily corrects the relevance of the word relevance map using the weighting, and generates an index for the input data using the corrected word relevance map.

Further, the present invention provides a data input device for inputting data to a database, an index reference reading device for inputting an index reference defining a configuration serving as an index reference, and an input device for inputting data. Checking the frequency of appearance of words used in the index criterion, calculating the relevance of words that appear simultaneously, and creating a word relevance map based on the relevance and the index criterion Device and the number of words appearing in the first document of the input data
A word importance assigning device that divides by the number of words appearing in the second document serving as the summary or heading of the document and weights the words in the second document by the obtained value;
Using the weighting obtained by the word importance assigning device, the relevance of the word relevance map created by the word relevance map creating device is temporarily corrected, and the corrected word relevance map is used. An index creation device that creates an index for the input data, a data search device that performs a search based on the index created by the index creation device, and a result display device that displays the search result. It is characterized by doing.

[0012] According to the present invention, when data is input by the data input device and the index criterion is input by the index criterion reading device, the word relevance map creator sets the input data based on the index criterion. The appearance frequency of the used word is checked, the relevance is calculated for the words that appear at the same time, and a word relevance map is created based on the relevance and the index criterion. Also,
The word importance assigning device is obtained by dividing the number of words appearing in the first document of the input data by the number of words appearing in the second document serving as a summary or heading of the first document. The words in the second document are weighted by the value.
Further, the index creation device temporarily corrects the relevance of the word relevance map using the weighting, and generates an index for the input data using the corrected word relevance map. Then, the data search device performs a search based on the created index, and the result display device displays the search result.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of a database creation device and a database search device according to the present invention will be described below in detail with reference to the accompanying drawings.

Embodiment 1 FIG. 1 is a block diagram showing an example of a database creation device according to the present invention. The database creation device includes a database 3, a data input device 2 for inputting data 1 to the database 3 from outside the system, an index reference reading device 5 for inputting an index reference 4 from outside the system, and the input data 1 A word relevance map creating device 7 for creating a word relevance map 8 based on the index criterion 4;
An index creation device 6 that creates an index 9 based on the input data 1, the word relevance map 8, and the index criterion 4, and a word importance assignment device 10 that calculates the importance of a word when creating an index. .

The database 3, the word relevance map 8 and the index 9 are stored in a storage device such as a hard disk. The data input device 2, the index reference reading device 5, the index creating device 6, the word relevance map creating device 7, and the word importance assigning device 10 are, for example, a data input program, an index reference reading program, This is realized by executing an index creation program, a word relevance map creation program, and a word importance assignment program.

The index criterion 4 indicates the structure of the index. As an example, FIG. 2 shows an index criterion 100 for a medical book database. The medical book index standard 100 has, for example, a title "medicine" at the top layer, and three titles "basic medicine", "internal medicine" and "surgery" under one layer,
In addition, one layer below “Basic Medicine” has “Anatomy” and “Physiology”, and one layer below “Internal Medicine” has “Circulatory System” and “Gastrointestinal System”. The index is configured to form a tree structure such as “local surgery” and “orthopedics” at the lower layer. Like the medical book index standard 100, the index standard 4 is also configured to form a tree structure.

In the following description, the medical book index standard 100 will be specifically described as an example. However, it is needless to say that the present invention is not limited to the medical book database and the medical book index standard 100. .

The word relevance map 8 is based on the index criterion 4
5 is a map in which the degree of association between words is added to the hierarchical relationship of. The word relevance map creator 7 checks the frequency of appearance of words used in the medical data index standard 100 for the input data 1 and performs a predetermined calculation based on the frequency, as shown in FIG. Word Relevance Map 1
Get 04. The method by which the word relevance map creating device 7 creates the word relevance map 8 will be specifically described using the data 101 shown in FIG. 4 as an example.

For example, the data 101 is composed of three documents, and the title of the document 1 is "talk of the cardiovascular system", and the abstract is "... the scariest cardiovascular disease is anatomically narrow. Heart disease and heart failure ... ". The title of Document 2 is "Concurrent cardiovascular disease and hernia", and the abstract is "... anatomically, cardiovascular .... for hernia, consult a surgeon." ... ". Document 3 is titled “Digestive and Cardiovascular”
The abstract states, "... If you don't masticate well, you can cause inflammation in your digestive tract and vomiting. If you vomit, it puts a burden on your heart, and if you have a circulatory disease such as angina,
... ".

When only words are extracted from these documents 1 to 3, a word string 102 shown in FIG. 4 is obtained.
That is, the word string 102 is “Cardiovascular” for the title in Document 1 and “Cardiovascular, anatomy, angina, heart failure” for the abstract, and “Cardiovascular” for the title in Document 2 , Hernia ”and“ anatomy, circulatory, hernia, and surgery ”for the abstract. In Document 3,“ Gastrointestinal and circulatory ”for the title and“ chewing,
Digestive, vomiting, heart, angina, circulatory.

Assuming that words appearing simultaneously in one document are related to each other, they are set as a co-occurring word set 103, and processing is performed on all data. For the co-occurrence word set 103, the degree of relevance is defined using, for example, the ratio of co-occurrence to the total number of appearances, as in the following equation (1). However, the total appearance frequency of a certain word (referred to as “KW1”) is N1, the total appearance frequency of another certain word (referred to as “KW2”) is N2, and “KW1” and “KW2” appear simultaneously. Let the co-occurrence frequency be N12 and let the degree of association between “KW1” and “KW2” be μ12.

Μ12 = N12 / (N1 + N2-N12) (1)

For example, referring to the document 1 described above, the co-occurring word set 103 includes “circulatory organ, anatomy”, “circulatory organ, angina”, and “circulatory organ” as shown in FIG. ,
Heart failure "," anatomy, angina "," anatomy, heart failure ",
"Angina pectoris, heart failure", ... For example, for the co-occurrence set of “circulatory organ, angina”, according to the above formula (1), (the co-occurrence frequency of “circulatory organ” and “angina”)
/ {(Total frequency of "circulatory") + (total frequency of "angina")-(co-frequency of "circulatory" and "angina")}
Find the value of

Then, the word relevance map 104 shown in FIG. 3 is obtained by assigning the value, that is, the relevance 105 to the hierarchical relation of the index standard 100. Various formulas other than the above formula (1) can be applied to the formula for calculating the degree of association between words, such as calculating the co-occurrence ratio by weighting the word according to the number of times the word appears in one document.

If the data to be indexed includes, for example, a title, an abstract, and a body, the word importance assigning apparatus 10 determines whether the word appearing in the title, the word appearing in the abstract, and the word appearing in the body are different from each other. Is appropriately weighted according to the value of In other words, in general, an abstract is a summary of the text in brief, and a title is an aggregate of the abstract, but it is assumed that the volume of the title, the abstract, and the content to be expressed is the same, and the title, abstract, The value of the word that appears in the text and the value are quantified and weighted. The method of determining weights by the word importance assigning apparatus 10 will be specifically described with reference to FIG. 6 using the data 108 shown in FIG. 5 as an example.

The data shown in FIG. 5 has a title and an abstract. First, the number of appearing words is counted for each of the title and the abstract (step S in FIG. 6).
1, S2). For example, for document 1, the words included in the abstract are four: "circulatory", "angina", "heart failure", and "anatomy". On the other hand, the word included in the title is one of "circulatory organs". Therefore, it is considered that the word included in the title has four times the value of the word included in the abstract. Therefore, the word of the title is weighted by four times the word of the abstract (step S3 in FIG. 6). This is performed for each data.

For example, Document 2 contains two words “Hernia” and “Circulatory” in the title and four words “Anatomy”, “Circulatory”, “Hernia” and “Surgical” in the abstract.
Title words are weighted twice as much. Document 3 also contains two words, “chewing” and “circulatory organ” in the title, and “abstract”, “digestive organ”, “vomiting”, “anatomy”, “physiology” in the abstract.
And six words "circulatory organ", so the title word is weighted three times. By such weighting, for example, in the example shown in FIG. 7, the relevance, which is originally 0.2, is 0.8 for document 1, 0.4 for document 2, and 0.6 for document 3 And the value of the "circulatory organ" is different for each data, that is, for document 1, document 2, and document 3 (step S4 in FIG. 6).

The same applies to a case where weighting is performed between an abstract and a text, and a case where weighting is performed between items of other document data. FIG. 8 shows an example of weighting between the abstract and the text. In the data 110 shown in FIG. 8, for example, when the same word repeatedly appears in the text, the number of appearances is taken into account. Also, simply adding the number of appearances causes a difference in the amount of target documents and books, so it is desirable to normalize them. In other words, the amount of text in the body varies depending on the document or book, and in general, the larger the amount of text, the more words appear. Therefore, it is preferable to perform normalization so as to perform weighting on a fixed document amount such as per page or per 1000 characters or a unit document amount.

Next, the flow of the index creation process will be described. When the data 1 is input to the database 3 by the data input device 2 and the index reference 100 shown in FIG. 2, for example, is input by the index reference reading device 5, the word relevance map creating device 7 And a word relevance map 8 as shown in FIG.
Create

Thereafter, the index creation device 6 creates an index 9 for the index creation target data based on the index criterion 4 and the word relevance map 8 according to, for example, a flowchart shown in FIG. That is,
First, a node (word) of a classification item included in each document is picked up and mapped to a word relevance map (step S11). As an example, FIG.
6 shows a state in which appearing words are marked in the word relevance map 8 for the document 3 of No. 6; In the example shown, the markings are the corresponding words: "digestive", "circulatory",
The words "chewing", "vomiting", "heart" and "angina" are indicated by bold underlined letters.

Subsequently, weighting is performed by the word importance assigning device 10, and the word relevance map 8 is temporarily corrected (step S12). In the example shown in FIG. 7, in the case of document 3, two words “digestive organ” and “circulatory organ” appear in the title, whereas the number of words in the abstract is six. Only at the time of processing, as for “digestive organ” and “circulatory organ”, the relevance of the word relevance map 8 is temporarily tripled to be 0.6 (0.2 × 3), respectively.

Subsequently, words appearing in the index creation target data are checked as terminal words, and classification evaluation values are calculated so as to trace from each terminal word to the root node ("medicine") (step S13). This is because, by calculating and evaluating each word mapped to the word relevance map 8 according to a certain calculation procedure, it is possible to determine what position in the entire classification system without evaluating the word alone at the mapped position. In order to take into account that

That is, for example, in the example shown in FIG.
The classification item "heart" does not simply mean the word "heart", but means the concept of "heart" regarding "circulatory organ" regarding "medicine" regarding "medicine". In order to reflect this, for example, from the terminal node node of “heart” to the root node “medicine”, the mapped classification items are traced back in order, and the relevance in the middle is added, and the obtained relevance is obtained. Is obtained by dividing the total number by the number of traversed layers to obtain an average value, which is used as a classification judgment evaluation value.

In the example shown in FIG. 7, in the case of document 3, "heart"
The degree of relevance between the circulatory organ and the next higher level is 0.9, and the degree of relevance between the circulatory organ and the upper level internal medicine is originally 0.2, Is temporarily 0.6, and the degree of relevance between “internal medicine” and the next higher level of “medicine” is 0.3. Therefore, the classification judgment evaluation value of the word “heart” is 0.6 by adding 0.9, 0.6, and 0.3 and dividing the sum by 3. That is, the degree to which document 3 is classified as “heart” is 0.6.

However, in the document 3 shown in FIG.
And the words "circulatory organ" appear, but the words "internal medicine" and "medicine" on the way back to the root node do not appear. In this way, if a word that has not been mapped up to the root node “medicine” appears and is interrupted, the relevance of the word relevance map 8 is not added as it is, and the following step S14 is performed. Perform processing.

That is, for example, referring to the example shown in FIG. 7, if the document 1 is traced from the terminal node “angina” to the upper layer, the word “heart” does not appear in the document 1. Therefore, the average value of the degree of association of the lower nodes of “heart” is obtained. Specifically, the average value 0.5 ((0.5 + 0.5) / of the relevance 0.5 of “angina” which is a lower node of “heart” and the relevance 0.5 of “heart failure” Find 2). Then, the product of the average value and the value 0.9 of the degree of relevance of the “heart” to the “circulatory organ” is calculated, and the value of the product is set to 0.
45 (0.9 × 0.5) is added as the provisional association degree (step S14).

Although "the degree to which the document 3 is classified as" heart "is 0.6" earlier, by performing the processing of step S14, the classification judgment table of the word "heart" of the document 3 is obtained. The value is actually 0.56 ((0.9 + 0.0.9) because the provisional relevance of “internal medicine” to “medicine” is 0.18 ((0.6 + 0.6) /2×0.3). 6 + 0.
18) / 3).

The above step S13 and step 14 when the node is interrupted are repeated for all terminal words nodes (step S15). For example, in the case of the data shown in FIG. 7, for document 3, “chewing”, “digestion”, “vomiting”, “heart”, “angina”, and “heart failure”, all of which are on their way to the root node Evaluate the node. The calculation formula and the calculation result of the classification judgment table value are shown for each of “chewing”, “digestion”, “vomiting”, “heart”, “angina”, and “heart failure”. In the calculation formula, a value surrounded by "" and "" is a provisional degree of association, and is added in order from a lower node to an upper node.
Note that “physiology” and “basic medicine” are omitted.

"Chewing": (0.7+ "0.23" +
“0.14” + “0.09”) / 4 = 0.29 “vomiting”: (0.8+ “0.23” + “0.14”)
+ “0.09”) / 4 = 0.32 “digestion”: (“0.23” + “0.14” + “0.
09 ”) / 3 = 0.15“ Angina pectoris ”: (0.5 + 0.9 + 0.6 +“ 0.1
8 ″) / 4 = 0.55 “heart failure”: (0.5 + 0.9 + 0.6 + “0.1
8 ") / 4 = 0.55" heart ": (0.9 + 0.6 +" 0.18 ") / 3
= 0.56 "circulatory organ": (0.6+ "0.18") / 2 = 0.3
9 "digestive organ": (0.6+ "0.18") / 2 = 0.3
9

After the evaluation of all the classification items that are possible as the classification destinations of the index creation target data is completed as described above, the item having the highest evaluation among them is determined as the classification destination and classified (step S16). ). In the example shown in FIG. 7, the classification item of “heart” has the highest evaluation value (0.5
6), the classification destination is determined to be “heart”. Then, after returning the word relevance map 8 temporarily corrected by the weighting to the initial value (step S17), the same processing is repeated for all the documents for which the index is to be created (step S18).

As a method of evaluating the classification destination of the document, a method of normalizing and adding the number of layers and the size of the map can be applied. For example, in consideration of the hierarchy in the word relevance map 8 of words appearing in a document, the relevance may be uniformly weighted for a deep layer (that is, a lower layer). By doing so, even if the number of levels in the classification system is very large, it is possible to avoid the arithmetic mean of the relevance being excessively low and being classified as an item of a higher-level word that appeared by accident. it can.

According to the first embodiment, index criterion 4
Automatically generates the degree of relevance between words contained in the data and weights the degree of relevance appropriately. Can be automatically created without affecting index criterion 4 without specially specifying.

The data for which an index is to be created is not limited to a document, but may be any data stored in a database and capable of recognizing words.
For example, the index creation target data may be data of a web page on the Internet including a tag corresponding to a control code.

Embodiment 2 FIG. 10 is a block diagram showing an example of the database search device according to the present invention. This database search device is different from the database creation device of the first embodiment shown in FIG.
And a result display device 3 for displaying a search result based on the index created in step 3.
9 is added. Therefore, the data input device 2, the database 3, the index reference reading device 5, the index creating device 6, the word relevance map creating device 7, and the word importance assigning device 10, which constitute the database creating device,
The index criterion 4 and the word relevance map 8 are the same as those in the first embodiment, and a description thereof will be omitted.

The data search device 38 is realized in a computer system by executing, for example, a data search program. The data search device 38
For example, an interface for displaying an index menu 111 created based on the index standard 100 as shown in FIG. 2 on the result display device 39 and allowing the user to overlook the interface, and for selecting an appropriate item from the menu, For example, a mouse cursor 112 is provided. Therefore, although not shown, a pointing device such as a mouse or a keyboard is connected to the data search device 38 as an input device. The result display device is, for example, a cathode ray tube or a liquid crystal display device which is a display device of a computer system.

When performing a search on the index created by the index creation device 6, the searcher moves the mouse cursor 112 to the menu displayed on the result display device 39 and designates an appropriate item. Then, by making a selection, the index can be searched, and the target book can be searched.

As shown in FIG. 12, the index criterion is divided for each node, and “internal medicine” is designated from the “medical” node 114 with the mouse cursor 112 to open the “internal medicine” node 115. And the mouse cursor 11
The target book may be searched by instructing “circulatory organ” in step 2 to open the “circulatory organ” node 116 and finally selecting “lymph gland” with the mouse cursor 112. A menu-like interface that narrows down the classification items one after another with respect to such a tree-structured index criterion allows an effective search.

[0048]

As described above, according to the present invention, when data is input by the data input device and the index criterion is input by the index criterion reading device, the word relevance map creating device is For data, the frequency of occurrence of words used in the index criterion is checked, the relevance of words that appear at the same time is calculated, and a word relevance map is created based on the relevance and the index criterion. Further, the word importance assigning device determines the number of words appearing in the first document of the input data by the first number.
Is divided by the number of words that appear in the second document, which is the summary or headline of the document, and the words in the second document are weighted by the obtained value. Further, the index creation device temporarily corrects the relevance of the word relevance map using the weighting, and generates an index for the input data using the corrected word relevance map. Therefore, even if the index creator does not particularly specify a sample or a typical example, the index can be automatically created in word units without affecting the index criterion.

According to the next invention, when data is input by the data input device and the index criterion is input by the index criterion reading device, the word relevance map creator generates the index criterion for the input data. The frequency of occurrence of the word used in (1) is checked, the relevance is calculated for words that appear simultaneously, and a word relevance map is created based on the relevance and the index criterion. Further, the word importance assigning device divides the number of words appearing in the first document of the input data by the number of words appearing in the second document which is a summary or a headline of the first document, and obtains the obtained result. The words in the second document are weighted by the value obtained. Further, the index creation device temporarily corrects the relevance of the word relevance map using the weighting, and generates an index for the input data using the corrected word relevance map. Then, the data search device performs a search based on the created index, and the result display device displays the search result. Therefore, the index can be efficiently searched, and the target data can be searched.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an example of a database creation device according to the present invention.

FIG. 2 is a system diagram showing a configuration example of an index standard used in the database creation device.

FIG. 3 is a schematic diagram showing an example of a word relevance map created by the database creation device.

FIG. 4 is an explanatory diagram for explaining a method of creating a word relevance map.

FIG. 5 is an explanatory diagram for explaining a method of weighting a word relevance map.

FIG. 6 is a flowchart illustrating an example of a method for determining a weight.

FIG. 7 is a schematic diagram showing an example of a weighted word relevance map.

FIG. 8 is a schematic diagram showing an example of weighting between an abstract and a text.

FIG. 9 is a flowchart illustrating an example of an index creation method.

FIG. 10 is a block diagram showing an example of a database search device according to the present invention.

FIG. 11 is a schematic diagram showing an example of a search menu used in the database search device.

FIG. 12 is a schematic diagram showing another example of a search menu used in the database search device.

FIG. 13 is a block diagram showing a conventional database search device.

FIG. 14 is a block diagram showing a conventional database search device.

FIG. 15 is a block diagram showing a conventional database search device.

[Explanation of symbols]

1 data, 2 data input devices, 3 databases,
4 Index standard, 5 Index standard reading device, 6
Index creation device, 7 word relevance map creation device, 8 word relevance map, 9 index, 10 word importance giving device.

 ────────────────────────────────────────────────── ─── Continued on the front page (72) Inventor Satoshi Tanaka 2-3-2 Marunouchi, Chiyoda-ku, Tokyo Mitsubishi Electric Corporation F-term (reference) 5B075 ND03 PP02 PP03 PP13 PP22 PQ02 PQ38 PR04 PR06 PR08 QM08 5B082 EA05

Claims (2)

[Claims] A data input device for inputting data to a database; an index reference reading device for inputting an index reference defining a configuration serving as an index reference; and an index for input data. A word relevance map creating device that examines the frequency of appearance of words used in the standard, calculates the relevance of words that appear at the same time, and creates a word relevance map based on the relevance and the index criterion, The number of words that appear in the first document of the input data is
Divide by the number of words that appear in the second document, which is the summary or headline of the first document, and obtain the second document by the obtained value.
And a word importance assigning device that assigns weights to the words in the document, and using the weights obtained by the word importance assigning device, to associate the word relevance map created by the word relevance map creating device with the weight. An index creation device for temporarily adjusting a degree and using the corrected word relevance map to create an index for the input data. 2. A data input device for inputting data to a database, an index reference reading device for inputting an index reference defining a configuration serving as an index reference, and an index for input data. A word relevance map creating device that examines the frequency of appearance of words used in the standard, calculates the relevance of words that appear at the same time, and creates a word relevance map based on the relevance and the index criterion, The number of words that appear in the first document of the input data is
Divide by the number of words that appear in the second document, which is the summary or headline of the first document, and obtain the second document by the obtained value.
And a word importance assigning device that assigns weights to the words in the document, and using the weights obtained by the word importance assigning device, to associate the word relevance map created by the word relevance map creating device with the weight. An index creation device that creates an index for the input data using the corrected word relevance map, and performs a search based on the index created by the index creation device A database search device comprising: a data search device; and a result display device for displaying the search result.