JP4025181B2 - Document management device - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a document management apparatus and method for reusing created document data, and more particularly to a document management apparatus and method for reusing document data using a result of morphological analysis.
[0002]
[Prior art]
Document classification and management are indispensable items for improving work efficiency in various fields such as academic, technical or economic fields. For example, in the case of management of facilities such as buildings and plants, in particular, reports related to maintenance often describe countermeasures for the problems in addition to some problems that have occurred in the past. Therefore, if it is possible to search for a past report in which matters equivalent to the newly-occurring defect are described, it is possible to take action with reference to this report. For this reason, various inventions related to document classification and management have been made in order to efficiently search for a desired document.
[0003]
For example, an invention for searching a description related to a search condition from a document (see Patent Document 1), an invention for inputting a condition, an item, a threshold value, etc. in advance and searching for a document that meets these conditions (see Patent Document 2) The invention calculates the similarity between a search key and a search target document using the importance and relevance of words in the search key and the search target document, and searches the document based on these importance and relevance (See Patent Document 3).
[0004]
The applicant has not yet found prior art documents related to the present invention by the time of filing other than the prior art documents specified by the prior art document information described in this specification.
[0005]
[Patent Document 1]
JP 11-45254 A [Patent Document 2]
JP 2001-337991 A [Patent Document 3]
Japanese Patent Laid-Open No. 2000-124041
[Problems to be solved by the invention]
However, none of the above-described methods can be said to be a document searched in consideration of the context and meaning of the document, and a desired document or the like cannot be searched unless a search condition is input. It was. For example, in the invention described in Patent Document 1, a search condition must be input, and only a specific description is output as a search result. In the invention described in Patent Document 2, documents similar to these conditions cannot be searched unless conditions, items, and threshold values are input in advance. For urgent cases or those with little experience, it is difficult to express the characteristics of a document with a simple keyword when searching for a desired document. Documents generally reflect the intentions and ideas of the creator of the document in the context and word arrangement and composition, and it is not easy to characterize the entire document using only keywords. Absent. Therefore, in the conventional method as described above, it is difficult to easily search for a document in accordance with the context and meaning of the meaning of the document creator.
Furthermore, the invention described in Patent Document 3 has a fatal problem as a document search system in which no search result is obtained when there is no matching as a subgraph.
The present invention has been made to solve the problems as described above, to reflect the context or semantic content of the document, provide a document management equipment that the document to be similar can be extracted easily The purpose is to do.
[0007]
[Means for Solving the Problems]
In order to solve the above-described problems, a document management apparatus according to the present invention includes a first recording unit that records a plurality of first document data, a morpheme analysis unit that performs morphological analysis on the document data, and a new input Multiplicity calculation for determining the degree of overlap between the second document data and the first document data from the second document data morpheme and the first document data morpheme recorded in the first recording means Means, and extraction means for extracting document data having a degree of overlap equal to or greater than a predetermined value from the second document data from the first document data. According to this document management apparatus, simply by inputting document data, document data having a degree of overlap with the document data having a predetermined value or more is extracted.
[0008]
In the document management apparatus, the similarity calculation means for calculating the similarity between the two document data from the overlap and the threshold, and at least one of the overlap and the similarity between the plurality of first document data are recorded. A second recording unit, and the extracting unit may extract the first document data having a direct similarity to at least the second document data from the first document data. According to this document management apparatus, document data having a direct similarity with the input document data is extracted.
[0009]
In the document management apparatus, the extracting unit may extract the first document data having an arbitrary distance with respect to the similarity with the second document data.
In the document management apparatus, the duplication degree calculating means may include a part of a plurality of morphemes included in the first document data arranged in a predetermined order and a plurality of morphemes included in the second document data in a predetermined order. A part of the array may be compared with each other, and the degree of overlap may be calculated depending on whether or not they match.
Further, in the document management apparatus, the duplication degree calculating means includes a part of the plurality of morphemes of the first document data arranged in a predetermined order and a plurality of morphemes of the second document data in the predetermined order. If a part of the array is compared and these do not match, the number of morphemes contained in the part may be reduced and the comparison may be performed again.
[0010]
The document management apparatus may further include setting means for setting a threshold value.
[0011]
Further, document management method according to the reference example, a first step and, a morpheme of one document data, morphological analysis is a plurality of other document data each morpheme morphological analysis one of document data input And a second step of calculating the degree of duplication between one document and another document, and extracting document data having one document data and a degree of duplication greater than a predetermined value from other document data. These steps are included.
[0012]
In the document management method, the third step is a step of calculating similarity between one document data and another document data from the duplication degree and the threshold, and at least one document data directly from the other document data. A step of extracting document data having similarity.
The document management method may further include a step of extracting other document data having an arbitrary distance with respect to the similarity with the one document data.
[0013]
In the document management method, the second step includes a part of a plurality of morphemes of one document data arranged in a predetermined order and a part of a plurality of morphemes of other document data arranged in a predetermined order. And the degree of overlap may be calculated based on whether or not they match.
In the document management method, the second step includes a part of a plurality of morphemes of one document data arranged in a predetermined order and a part of a plurality of morphemes of other document data arranged in a predetermined order. If they do not match, the number of morphemes contained in a part may be reduced and the comparison may be performed again.
[0014]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
FIG. 1 is a block diagram showing the configuration of the document management apparatus according to this embodiment. The document management apparatus 1 according to the present embodiment includes an arithmetic device such as a CPU, a storage device such as a memory and an HDD, an I / F device that transmits / receives information to / from the outside, a CRT (Cathode Ray Tube), an LCD (Liquid Crystal). It comprises a computer equipped with a display device such as Display) or FED (Field Emission Display), and a program and an electronic dictionary installed in this computer. The hardware device is controlled by a program, that is, the hardware resource and the software cooperate, and the morphological analysis unit 12 that performs morphological analysis on the document data composed of character strings, A degree-of-duplication calculator 13 that calculates the degree of overlap by comparison, a degree-of-similarity calculator 14 that calculates the degree of similarity based on the degree of overlap and a threshold, and data that causes the display means to display the relationship between the document data are generated. A visualization unit 15, an extraction unit 16 that extracts document data, and a setting unit 17 that sets a threshold value are realized. The storage device has a document database (document DB) 18 that stores document data and the like.
[0015]
Here, the document data refers to a document, that is, data including a character string. This document data is supplied via a recording medium such as a flexible disk, or via various networks and communication lines such as a LAN (Local Area Network), a WAN (Wide Area Network), and the Internet, and the document management apparatus from the outside. The document data input to 1 is stored in the document DB 18 via the I / F 11.
[0016]
The morpheme analysis unit 12 performs morphological analysis on document data, more specifically, a character string included in the document data, and decomposes the character string included in the document data into morphemes including a word and a part of speech of the word. Here, FIG. 2 is a diagram showing a state of morphological analysis. For example, when a morphological analysis is performed on a character string “I like flowers”, as shown in FIG. 2, the words “I”, “ha”, “flower”, “ga”, “like”, and these words Can be broken down into morphemes composed of parts of speech. The morpheme analyzer 12 arranges the plurality of morphemes obtained in this way in a predetermined order. The morpheme array obtained for each document data is referred to as a feature vector for convenience.
[0017]
Here, the arrangement of morphemes in the feature vector may be in the order of appearance in the document data or in the order of appearance frequency. FIG. 3 is a diagram showing an arrangement of feature vectors, that is, morphemes. For example, as shown in FIG. 3, “Tomorrow, we will have a meeting at 1:00 pm at the Fujisawa Plant in Yamatake. Mr. Murakami and Mr. Kiso will be in charge of Yamatake. When character strings are decomposed into morphemes and arranged in the order of appearance, each morpheme is arranged according to the original arrangement of character strings. When the morphemes are arranged in the order of appearance, the morphemes are also arranged according to the word arrangement of the original document data, so that a feature vector that emphasizes the flow of the sentence is generated.
In the present embodiment, when generating feature vectors, morphemes are arranged in the order of appearance. However, in the case of arrangement in the order of appearance frequency, the morphemes are arranged in the order of morphemes having the highest appearance frequency in the character string. In the case of FIG. 3, since three words “Yamatake”, “No”, and “Fujisawa” appear three times each, they are arranged from morphemes including these three words. Note that morphemes having the same number of appearances are arranged in the order of appearance. When morphemes are arranged in the order of appearance frequency in this way, since morphemes are arranged from morphemes having a high appearance frequency, feature vectors in which importance is attached to the theme and content of the text are generated.
[0018]
The feature vector generated by the morphological analysis unit 12 described above is recorded in the document DB 18 in association with the document data. Therefore, each feature vector is generated and recorded for the document data (first document data) already recorded in the document DB 18.
[0019]
The degree-of-redundancy calculation unit 13 includes the feature vector of one document data (second document data) generated by the morpheme analysis unit 12 and other document data (first document data) stored in the document DB 18. The degree of overlap with the feature vector is calculated and recorded in the document DB 18 in the form of a matrix indicating the relationship between one document data and other document data, for example. This degree of duplication is a quantification of how much common morphemes exist between two documents. More strictly, when one document is viewed from another document, more than one document is compared. This refers to the ratio of morphemes that match morphemes obtained from other documents to all obtained morphemes. A method for calculating the degree of overlap will be described below.
[0020]
First, feature vectors are compared between two documents, and the number of common morphemes is counted. At this time, when comparing feature vectors, morphemes may be compared one by one, or may be compared every predetermined number. The comparison for every predetermined number means, in other words, a comparison in units of all or a part of N morphemes (N is a natural number of 2 or more) arranged in a predetermined order of feature vectors. For example, if the predetermined number is n (n is an arbitrary natural number of 2 or more and N or less), an array of n morphemes (hereinafter referred to as “nth-order partial feature vector”) among the morpheme arrays constituting the feature vector. Compare each other. If they match, n morphemes will match.
[0021]
When comparing feature vectors obtained from one document with feature vectors obtained from another document every n number of times, first, the first of the feature vectors consisting of N morphemes from one document To an nth morpheme from n to n, that is, an nth-order partial feature vector, and an nth-order feature vector consisting of M morphemes (M is a natural number greater than or equal to n) obtained from another document. Compare with the partial feature vector. In this case, if they match, n morphemes match. Similarly, the partial feature vector composed of the 2nd to n + 1th morphemes of the feature vector obtained from one document,..., And the other document for each of the partial feature vectors composed of the (N−n + 1) th to Nth morphemes The feature vectors obtained from the above are sequentially compared and collated.
[0022]
Thus, by comparing and collating in units of partial feature vectors, the commonality of context and expression can be reflected in the degree of duplication.
If there is no match between the two documents as a result of comparison and collation for n partial feature vectors, the order n of the partial feature vectors is reduced by 1, and (n-1) The next partial feature vector is compared and collated again according to the algorithm described above. The comparison and collation are repeated while decreasing the value of n until a matching partial feature vector is found or n = 1.
As described above, according to the present embodiment, the order of the partial feature vectors is changed in stages, and two documents are compared and collated. Therefore, in the case of matching at the maximum order, the context and semantic content of the document are considered. The result will be obtained. In addition, since even the lowest order can be compared, at least a search result at the same level as the search by keyword can be obtained. Thus, the document management apparatus 1 according to the present embodiment employs a very flexible search method.
[0023]
FIG. 4 is a diagram showing how feature vectors are compared. For example, as shown in FIG. 4, the feature vector (1) (N = 5) of one document having the character string “I like flowers” and another document having the character string “I like flowers” Consider the case where the feature vector {circle over (2)} (M = 3) is compared.
When comparing morphemes one by one (ie, n = 1), morphemes “flowers”, “common to two documents” are compared because each morpheme, ie, one word, is compared (corresponding to circled numbers 11 to 15). ”And“ like ”are matched (circle numbers 13, 14, 15), and the number of common morphemes is three.
[0024]
On the other hand, when comparing every predetermined number, for example, it is conceivable to perform comparison and collation on the whole feature vector (1) of one document (in this case, n = N = 5). In this case, since the entire feature vector (1) of one document does not exist in the feature vector (2) of another document, the order of the partial feature vectors extracted from the feature vector (1) of one document is n = 4. Are compared and collated (circled numbers 2, 3). When n = 3, the third-order partial feature vector {“I”, “ha”, “flower”}, {“ha”, “flower”, “ga”} obtained from the feature vector {circle around (1)} , {“Flower”, “ga”, “like”} are collated with the feature vector {circle around (2)} in this order (circled numbers 4, 5, 6). In this case, the partial feature vector {“flower”, “ga”, “like”} (circled number 6) matches the entire feature vector {circle around (2)} of other documents, and there are other matching feature vectors. Therefore, the comparison and collation are finished, and the common morpheme number is 3.
[0025]
Then, the number of morphemes common between the two counted documents is divided by the total number of morphemes obtained from one document, and this quotient becomes the overlap. For example, when the degree of overlap is calculated in the case of FIG. 4 described above, the feature vector {circle over (1)} has 5 morphemes, the feature vector {2} has 3 morphemes, and 3 common morphemes. When the feature vector (2) is compared from 1 ▼, the degree of overlap is 3/5, and when the feature vector (2) is compared with the feature vector (1), the degree of overlap is 3/3, that is, 1.
[0026]
In the present embodiment, a feature vector of each document is obtained by arranging morphemes in the order of appearance. Further, as a case of comparison for each predetermined number, it has been described that the initial value of the predetermined number n is set to n = N, and comparison and collation are performed. However, the initial value n can be arbitrarily set.
[0027]
The similarity calculation unit 14 calculates the similarity between two documents based on the degree of overlap calculated by the degree of overlap calculation unit 13 and records this in the document DB 18. The similarity means an index indicating whether or not the contents of two documents have commonality. Such similarity can be expressed as a binary relationship. In calculating the similarity, the similarity calculation unit 14 gives similarity 1 as two sentences are similar if the overlap calculated by the overlap calculation unit 13 is equal to or greater than a predetermined threshold, and does not reach the threshold. In the case, the similarity is given as 0 because it is not similar.
For example, in the case of the feature vector {circle around (1)} and the feature vector {circle around (2)} shown in FIG. 4, if the threshold is 0.7, the degree of overlap when the feature vector {circle around (1)} is compared with the feature vector {circle around (2)} is 3. Since / 5, that is, 0.6, the similarity is 0. Similarly, when the feature vector {circle over (2)} is compared with the feature vector {circle over (1)}, the degree of overlap is 1, so the degree of similarity is 1.
The similarity calculated in this way can be expressed as a binary relationship (graph) between two document data. The similarity between a plurality of document data can also be expressed as a feature vector as shown in FIG. 5, for example, as a matrix representing the similarity between document data.
[0028]
The visualization unit 15 generates visualization data indicating the relationship between one document data received by the I / F 11 and other document data stored in the document DB 18 based on the similarity recorded in the document DB 18. , And sent to the display device via the I / F 11. As the visualization data, for example, data such as a binary graph including a matrix or a node representing document data and a link connecting the nodes can be cited. Here, FIG. 5 is a diagram showing the degree of similarity in a matrix form, and FIG. 6 is a diagram showing a binomial graph. In FIG. 6, when the similarity between the document data corresponding to the node 6a and the node 6b is 1, as shown in FIG. 6A, the nodes 6a and 6b are connected by a link 7a. On the other hand, when the similarity of at least one of the document data corresponding to the node 6c and the node 6d is 0, the node 6c and the node 6d are not connected by a link as shown in FIG.
[0029]
Based on the similarity calculated by the similarity calculation unit 14, the extraction unit 16 extracts other document data similar to the one document data from the document DB 18, and sends the document data to the outside via the I / F 11. The extraction unit 16 may extract not only other document data similar to one document data but also other document data similar to the other document data.
[0030]
The setting unit 17 changes a threshold value used by the similarity calculation unit 14 to calculate the similarity. When the setting of the threshold value is changed by the setting unit 17 in this manner, the similarity value calculated by the similarity calculating unit 14 changes, so that the document data extracted by the extracting unit 16 also changes, and the extracting unit 16 further extracts the data. Document data also changes.
[0031]
A document DB (Data Base) 18 records one document data received from the outside via the I / F 11 and a feature vector generated from the document data by the morphological analysis unit 12 in association with each other. The document DB 18 records the degree of overlap and similarity between one document data and other document data.
[0032]
Next, a case where the document management apparatus 1 according to the present embodiment is applied to a report document such as building maintenance will be described.
7 is a diagram of a system to which the document management apparatus 1 according to the present embodiment is applied. FIG. 8 is a flowchart showing the operation of the document management apparatus 1 according to the present embodiment. FIG. 9 is a question when document data is visualized. It is a figure explaining the relationship between a matter and an answer matter.
Here, it can be considered that the report document contains two related items such as the situation and result, the question and the answer. For convenience, items corresponding to situations and questions are referred to as question items, and items corresponding to results and responses are referred to as answer items. Therefore, if a document having a degree of similarity with a document including a question item is extracted, it can be expected that the extracted document includes an answer item for the question item.
[0033]
In the system shown in FIG. 7, the document DB 18 of the document management apparatus 1 records a plurality of report documents created in the past as first document data and their feature vectors in association with each other. The degree of overlap or similarity between these document data is recorded.
The document management apparatus 1 is connected via a network 2 to a terminal device 3 of a user who performs maintenance or the like.
The network 2 includes various networks such as LAN, WAN, and the Internet, communication lines, and the like, connects the document management device 1 and the terminal device 3, and enables transmission and reception of various information between them. A plurality of terminal devices 3 can be connected to such a network 2.
The terminal device 3 comprises a known computer equipped with a display device, a PDA (Personal Digital Assistant), etc., and transmits and receives document data and visualization data such as reports via the network 2 to display the document data and visualization data. Display on the device.
[0034]
When the second document data such as a report is received from the terminal device 3 (step S801), the document management device 1 records the received document data in the document DB 18.
[0035]
Next, the document management apparatus 1 causes the morphological analysis unit 12 to generate a feature vector of the second document data received from the terminal apparatus 3 (step S802). The generated feature vector is recorded in the document DB 18 in association with the document data that is the basis of the feature vector.
As described above, the document data input from the terminal device 3 and the feature vector generated based on the document data are stored in the document DB 18 every time document data is input from the outside.
[0036]
When the feature vector of the second document data (one document data) input from the terminal device 3 is generated, the document management device 1 uses the redundancy calculation unit 13 to store the feature vector and a plurality of feature vectors stored in the document DB 18. The degree of overlap with each feature vector of the first document data (other document data) is calculated (step S803). This calculated degree of duplication may be recorded in the document DB 18. In this case, the degree of overlap between one document data and the other document data can be expressed in the form of a matrix, for example.
[0037]
When the degree of duplication is calculated, the document management apparatus 1 causes the degree-of-similarity calculation unit 14 to calculate the degree of similarity between one document data and each of a plurality of other document data based on the degree of duplication and a preset threshold value. (Step S804). The calculated similarity may be recorded in the document DB 18. In this case, the similarity between one document data and the other document data can be expressed in a form such as a matrix.
[0038]
When the similarity is calculated, the document management apparatus 1 causes the visualization unit 15 to create visualization data of the document data based on the similarity (step S805). As the visualization data, for example, a binomial graph as shown in FIG.
[0039]
In FIG. 9A, document data corresponding to nodes connected by links have a similarity of 1 to each other, and document data corresponding to nodes not connected by links are similar to each other or one of them. It means that the degree is 0. For example, since the node 6b is connected to the nodes 6a and 6c by the links 7a and 7b, it can be seen that the degree of similarity is 1 with the nodes 6a and 6c. On the other hand, since the node 6a and the node 6c are not connected by a link, the degree of similarity is 0 with respect to each other or one of them.
Here, if one newly inputted document data corresponds to the node 6d, it can be seen that the node 6d has a link, that is, a similarity, with the node 6a as shown by a dotted line in FIG. 9A.
[0040]
Visualization data includes a node connected to a large number of nodes or a set of nodes that are complexly connected to a plurality of nodes, together with representative values and keywords common to the document data of these nodes. You may make it display. Thereby, the user can confirm the characteristics of the node aggregate at a glance.
The visualization data can also be three-dimensional graphic data. Thereby, the state of connection of the nodes can be displayed in more detail in detail.
[0041]
Here, the document management apparatus 1 may send the created visualization data to the terminal apparatus 3 via the I / F 11 and the network 2 as necessary.
[0042]
Next, the document management apparatus 1 extracts other document data similar to the one document data (step S807). For example, as shown in FIG. 9, when one piece of document data represented by a node 6d is input, the document management apparatus 1 extracts document data represented by a node 6a similar to the node 6d from the document DB 18. .
[0043]
Further, the document management apparatus 1 may extract other document data having an arbitrary distance regarding the similarity. Here, the “distance related to similarity” means, for example, when the similarity of a plurality of document data is expressed by a binary graph, a path from one document data to another document data is weighted with the similarity It is the sum of the attached values. In the present embodiment, since the similarity is represented by “1” or “0”, for example, when one document data and other document data have a direct similarity, the distance is 1, 1 Although the document data does not have a direct similarity, the document data having a similarity with the other document data having the similarity with the one document data has a distance of 2 from the one document data. . In the present embodiment, with respect to the similarity in the extraction of document data, the distance is set to 2 and other similar to one document data (node 6d) and other document data (node 6a) having similarity. The document data (node 6 b ) may be extracted from the document DB 18. If the distance is further increased and the extraction target range is expanded, in FIG. 9, even when the document data represented by the node 6b does not have an answer item, the document management apparatus 1 has a node 6c having similarity to the node 6b. Can be extracted from the document DB 18 (including answer items).
By doing in this way, the document management apparatus 1 according to the present embodiment can be used even when the answer data is not included in the document data that is directly similar (related) to the document data including the input question data. Since it is possible to extract document data that is similar (related) indirectly, it is possible to effectively extract data related to past countermeasures in which a similar problem or the like has occurred from a wide range.
[0044]
When the document data is extracted, the document management device 1 sends the document data to the terminal device 3 via the I / F 11 and the network 2 (step S808).
[0045]
As described above, the document management apparatus 1 according to the present embodiment does not need to select a keyword or the like, and simply inputs document data including at least one of a question item such as a report and an answer item. Since document data similar to the document data is extracted, it is very convenient.
[0046]
In step S804, the threshold value may be changed freely and appropriately by the setting unit 17. For example, when the visualization data is displayed on the terminal device 3, if the similarity between the document data is difficult to grasp because there are too many or too few links, the visualization data can be easily viewed by changing the threshold appropriately. It is possible to easily grasp the similar relationship between data. Further, when there is too much or too little extracted answer data, an appropriate quantity of document data can be obtained by appropriately changing the threshold value.
[0047]
【The invention's effect】
As described above, according to the present invention, the degree of duplication between one newly input document data and other document data is calculated, the degree of similarity is calculated from the degree of duplication and the threshold value, and a predetermined value is obtained. Since other document data having the above similarity is extracted, similar document data can be extracted by a simple operation of inputting the document data without inputting a predetermined condition such as a keyword. .
Further, according to the present invention, the degree of duplication and the degree of similarity are calculated, and from these, it is determined whether or not the similarity of the documents is similar. Therefore, it is possible to extract a document having a similar context or sentence meaning. .
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration of a document management apparatus according to an embodiment.
FIG. 2 is a diagram showing a state of morphological analysis.
FIG. 3 is a diagram showing a state of arrangement of morphemes.
FIG. 4 is a diagram showing how feature vectors are compared.
FIG. 5 is a diagram showing similarity in matrix. FIG. 6 is a diagram representing a binomial graph.
FIG. 7 is a diagram of a system to which the document management apparatus 1 according to the present embodiment is applied.
FIG. 8 is a flowchart showing the operation of the document management apparatus 1 according to the present embodiment.
FIG. 9 is a diagram for explaining a relationship between a question item and an answer item when document data is visualized.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Document management apparatus, 2 ... Network, 3 ... Terminal device, 6a-6d ... Node, 7, 7a, 7b ... Link, 11 ... I / F, 12 ... Morphological analysis part, 13 ... Duplication degree calculation part, 14 ... Similarity calculation unit, 15 ... visualization unit, 16 ... extraction unit, 17 ... setting unit, 18 ... document DB.

Claims (4)

First recording means for recording a plurality of first document data;
Morphological analysis means for morphological analysis of document data;
From the newly input morpheme of the second document data and the morpheme of the first document data recorded in the first recording means, the second document data and the first document data are common. A degree-of-duplication calculating means for obtaining a degree of overlap obtained by dividing the number of morphemes by the number of morphemes included in each document data;
Extracting means for extracting, from the first document data, document data having a degree of overlap with the second document data that is equal to or greater than a predetermined value;
The multiplicity and indicates whether the index two document data from the predetermined threshold value are similar, one as the overlap degree of two document data in the case of more than the predetermined threshold value are similar, the A degree of similarity calculating means for calculating a degree of similarity that gives 0 when two document data are not similar when the degree of overlap does not reach the predetermined threshold ;
And records at least one of the overlapping degree of similarity between the plurality of the first document data, the similarity is recorded by combining the link to the first document data between Ru 1 der respect to one another A second recording means,
The extracting means includes the similarity between the second document data and one of the first document data and a plurality of the first document data recorded in the second recording means. Based on the link, the number of links from the plurality of first document data to the second document data is calculated as a distance related to the similarity, and the first document having a predetermined value for the distance is calculated. A document management apparatus characterized by extracting data. The document management apparatus according to claim 1.
The duplication degree calculating means includes a part of a plurality of morphemes included in the first document data arranged in a predetermined order and a part of a part of the plurality of morphemes included in the second document data arranged in the predetermined order. And a duplication degree is calculated based on whether or not they match each other . The document management apparatus according to claim 2, wherein
The duplication degree calculating means includes a part of a plurality of morphemes included in the first document data arranged in a predetermined order and a part of a part of the plurality of morphemes included in the second document data arranged in the predetermined order. And if they do not match, the number of morphemes contained in the part is reduced and the document management apparatus performs comparison again . The document management apparatus according to any one of claims 1 to 3,
Setting means for setting the threshold
A document management apparatus further comprising:

Images