KR101426765B1 - System and method for supplying collaboration partner search service - Google Patents

Abstract

A system and method for providing a collaborative partner search service is disclosed. According to the present invention, the keyword is extracted from the table value of the analysis target data for the high quality national R & D information provided by the NTIS, and the expertise information of the person is derived based on the relational expression set for the extracted keyword, Based on the attribute information and the link information, derives the collaborative relationship between the person and the person as a knowledge map, determines the personality expertise information on the specific topic input by the keyword, analyzes the network relation, It is possible to provide more accurate and specialized services than the list type service provided by the expert recommendation service and to provide the researcher with easier and higher quality service than when searching for a collaborative relationship or searching for an expert .

Description

SYSTEM AND METHOD FOR PROVIDING COLLABORATION PARTNER SEARCH SERVICE

The present invention relates to a system and method for providing a cooperative partner search service, and more particularly, to a system and method for providing a cooperative partner search service by determining expertise information of a specific subject field from analysis target data of national R & D information, And more particularly, to a system and a method for providing a cooperative partner search service according to a search result.

Recently, social network analysis is becoming more active. In response to these trends, there is a growing demand among researchers and scientists for easy understanding of the status of knowledge group formation in specialized fields.

In Korea, there is not a wide variety of network analysis or service attempts among researchers or scientists. There is also a growing interest in how to visualize relationships between information to intuitively express complexity of information.

The National Science and Technology Knowledge Information Service (NTIS) has been conducting research on the R & D information - R & D information, participating personnel, achievements (papers, patents, commercialization information, technology transfer etc.) To provide various services to researchers, policy makers, and government departments. From 2010 onwards, efforts are being made to provide national R & D information from the supplier center to the consumer center through the upgrading project.

Based on the national R & D information provided by the NTIS, there is a need for a separate logic to analyze the expertise information and the network relation of the person, and to provide a service recommending experts and collaboration groups in a specific field.

SUMMARY OF THE INVENTION Accordingly, the present invention has been made to solve the above problems, and an object of the present invention is to provide a method and apparatus for analyzing a table value of analysis target data provided from outside by using a morphological analysis technique, And an indexing unit for extracting expertise information possessed by a person on a keyword basis, based on the extracted keywords, and setting an order of expertise information, And the node information indicating the collaborative relationship between the attribute information of the expertise information and the person, from the ranking person for the expertise information of the analyzing unit, and the collaboration between the source and the target based on the derived attribute information and the node information A knowledge map generating unit for deriving a relation as a knowledge map, The present invention provides a system and a method for providing a collaborative partner search service including an interface unit for visually displaying and displaying each key word corresponding to each key word. And the like.

In order to achieve the above object, according to a first aspect of the present invention,

An indexing unit for analyzing a keyword of a table value of analysis target data provided from the outside through a morpheme analysis technique that is already defined, calculating a DF (Document Frequency) value already defined from the analyzed keyword,

An analyzing unit for deriving expertise information of a person on a keyword basis by keywords based on the keyword extracted by the indexing unit, setting an order of expertise information and storing the information in an analysis DB,

From the ranking personality information of the analysis unit, the node information indicating the collaboration information between the attribute information and the personality information is derived from the personality, and the collaboration relation between the source and the target is derived as the knowledge map based on the derived attribute information and the node information A knowledge map generating unit,

And an interface unit for visually displaying the knowledge map of the knowledge map generating unit corresponding to each keyword.

Preferably, the indexing unit includes:

Analyzes the respective columns of the table for the analysis target data on the basis of the morpheme analysis technique

Generate DF (Document Frequency) values from previously defined relational expressions for the analyzed keywords,

Generates a distribution map of DF values of keywords based on the generated DF (Document Frequency) value,

Extracting a keyword having a distribution of the DF values of the generated keywords within a predetermined range.

Preferably, the extracted keyword is a keyword whose distribution of DF values of the keywords is 4 <= sum (DF) <= 10,000.

Preferably, the morpheme analysis technique further comprises:

And is an N-gram method utilized in the fields of information retrieval and natural language processing.

Preferably,

A count of Paper Information of i-th Person including a keyword extracted from the indexing unit, a count of paper information of i-th Person in performance information, The total score of each task, the total score of the articles, and the sum of the achievements of the articles are given weight, and the expertise information of the person for each keyword (for example, Expertise Point) is derived,

And stores the expertise information of the derived person in the analysis DB in correspondence with each keyword.

Preferably, the expertise information possessed by the person is characterized by satisfying the following expression.

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2

KP i = Expertise Point of i-th Person having Keyword K

PR = Count on Project Information of i-th Person

RI = Count of Paper Information of i-th Person

HP = Count of Paper in Human Resource Information of i-th Person

α = Weight of Project Information

β = Weight of Paper Information

γ = Weight of Paper in Human Resource Information

i = Number of Person having Keyword K

λ = Weights for cases where the person's performance is not SCI

Preferably, the knowledge map generating unit includes:

Based on the expertise information possessed by the person in the analysis section, attribute information indicating the person name, the number of the tasks, the number of the achievements, the list of the belonging organization, the task and the achievement, the institution ID to which the person belongs, However,

The link information indicating the collaboration relationship between the person and the person including the ID of the link, the source of the link, the target, the number of collaborative tasks,

And derives a collaboration relationship between the source and the target as a knowledge map based on the linkage information and link information of the derived person.

Preferably, the interface unit includes:

And displaying the matched keyword and the knowledge map of the knowledge map generating unit by visualizing the input keyword and the keyword matching stored in the analysis DB.

A second aspect of the present invention to achieve the above object

An indexing step of extracting a keyword from the analysis target DB table through a morphological analysis technique, computing a DF (Document Frequency) value from the extracted keyword,

An analysis step of deriving expertise information of a person on a keyword-by-keyword basis with respect to the keyword extracted in the indexing step, setting a rank of expertise information and storing the information in an analysis DB,

A knowledge map generation step of generating a knowledge map of a collaboration relationship between a person and a person by calculating a collaboration relationship based on a predefined relation for a ranking person of the personality of the person in the analysis step,

And an interface step of visualizing and displaying the knowledge map of the knowledge map generating step for each keyword.

Preferably, the indexing step comprises:

Extracts keywords based on the morphological analysis technique for each column of the analysis target table

Generate Document Frequency (DF) values from the previously defined relational expressions for the extracted keywords,

Generates a distribution map of DF values of keywords based on the generated DF (Document Frequency) value,

Extracting a keyword having a distribution of the DF values of the generated keywords within a predetermined range.

Preferably, the extracted keyword is a keyword whose distribution of DF values of each keyword is within 4 <= sum (DF) <= 10,000.

Preferably, the morpheme analysis technique further comprises:

And is an N-gram method utilized in the fields of information retrieval and natural language processing.

Preferably, the analyzing step comprises:

A count of Paper Information of i-th Person including a keyword extracted from the indexing step, a count of paper information of i-th Person in performance information, The total score of each task, the total score of the articles, and the sum of the achievements of the articles are given weight, and the expertise information of the person for each keyword (for example, Expertise Point) is derived,

And stores the expertise information of the derived person in the analysis DB in correspondence with each keyword.

Preferably, the expertise information of the person

The following formula is satisfied.

여기서, α+β+γ= 1, if(HP(p) = SCI) then λ=0.8 else λ = 0.2

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2

KP i = Expertise Point of i-th Person having Keyword K

PR = Count on Project Information of i-th Person

RI = Count of Paper Information of i-th Person

HP = Count of Paper in Human Resource Information of i-th Person

α = Weight of Project Information

β = Weight of Paper Information

γ = Weight of Paper in Human Resource Information

i = Number of Person having Keyword K

λ = Weights for cases where the person's performance is not SCI

Preferably, the knowledge map generation step includes:

In the analysis step, based on the expertise information possessed by the person, the attribute information indicating the person's name, the number of the tasks, the number of the achievements, the list of the affiliated institutions, the assignments and the achievements, the institution ID to which the person belongs, Respectively,

The link information indicating the collaboration relationship between the person and the person including the ID of the link, the source of the link, the target, the number of collaborative tasks,

And derives a collaboration relationship between the source and the target as a knowledge map based on the linkage information and link information of the derived person.

Advantageously,

And displaying the matched keyword and the knowledge map derived from the knowledge map generation step in a visualized manner.

As described above, the system and method for providing the cooperative partner search service according to the present invention extract keywords from the table values of the analysis target data for the high-quality national R & D information provided by the NTIS, And derives the collaboration relationship between the person and the person as a knowledge map based on the attribute information and the link information with respect to the expert information possessed by the derived person based on the knowledge of the person, By analyzing the information and analyzing the network relation and providing it in the form of a knowledge map, it is possible to provide a more accurate and specialized service than the list type service provided in the expert recommendation service, Easier and more quality service than when you are looking for an expert To be able to offer.

In addition, it is possible to search for experts on a specific subject based on the tasks and performance information provided by NTIS, rather than searching only the primary information including characteristics information of a thesis or a person in terms of expert search, In addition, the effect of expanding the network data among the people, expanding the performance related tools, and providing a process of analyzing the collaboration relationship among the organizations can be obtained.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate preferred embodiments of the invention and, together with the description of the invention given below, serve to further understand the technical idea of the invention. And should not be construed as limiting.
FIG. 1 is a block diagram illustrating a system for providing a cooperative partner search service according to an embodiment of the present invention. Referring to FIG.
FIG. 2 is an exemplary diagram showing the analysis target table shown in FIG. 1. FIG.
FIG. 3 is an exemplary diagram showing table values for the vestige information shown in FIG. 1. FIG.
4 is an exemplary diagram showing a table value for the attribute information shown in FIG.
FIG. 5 is an exemplary diagram showing table values for the node information shown in FIG. 1. FIG.
FIG. 6 is an exemplary diagram showing the knowledge map shown in FIG. 1. FIG.
FIG. 7 is an exemplary diagram illustrating a collaboration partner search according to an embodiment of the present invention.
8 is a flowchart illustrating a process of providing a cooperative partner search service according to another embodiment of the present invention.

For a better understanding of the present invention and its operational advantages and the objects attained by the practice of the present invention, reference should be made to the accompanying drawings and the accompanying drawings which illustrate preferred embodiments of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the preferred embodiments of the present invention with reference to the accompanying drawings. Like reference symbols in the drawings denote like elements.

FIG. 1 is a diagram showing a configuration of a system for providing a cooperative partner search service according to an embodiment of the present invention, FIG. 2 is an example of an analysis target table shown in FIG. 1, 3 is an exemplary diagram showing a table value showing attribute information shown in FIG. 1, and FIG. 4 is an exemplary diagram showing a table value showing node information shown in FIG. FIG. 5 is an exemplary view showing the knowledge map shown in FIG. 1, and FIG. 6 is an exemplary diagram illustrating a collaboration partner search according to an exemplary embodiment of the present invention.

A system for providing a cooperative partner search service according to an embodiment of the present invention includes a keyword analyzing unit 11, a DF generating unit 13, a DF distribution generating unit 15, and a keyword extracting unit 17, A knowledge map generating unit 30 including an input unit 10, an analyzing unit 20, an attribute information deriving unit 31, a node information deriving unit 33 and a knowledge map deriving unit 35, 40).

Here, the indexing unit 10 is configured to analyze a keyword from an analysis target DB table through a morphological analysis technique, to calculate a document frequency (DF) value from the analyzed keyword, and to extract the keyword.

That is, the indexing unit 10 receives the analysis target DB table value shown in FIG. 2 and analyzes keywords based on the morpheme analysis technique on each column of the analysis target table in the keyword analyzing unit 11, DF analysis unit 13, and the DF analysis unit 13 generates a DF (Document Frequency) value from the relational expression already defined for the analyzed keyword. Here, the keyword analysis may be generated by an N-gram method utilized in the fields of information retrieval and natural language processing.

The process of deriving the DF value based on the relational expression already determined for the keyword is similar to or the same as the process of deriving the DF value from the existing keyword, and thus a detailed description thereof will be omitted.

The generated DF (Document Frequency) value is provided to the DF distribution generating unit 15, and the DF distribution generating unit 15 generates a distribution map of the DF values of the keywords based on the DF value. At this time, only keywords having a distribution of the DF values for the generated keywords within a predetermined range are output from the keyword extracting unit 17.

That is, the word extracting unit 17 outputs only the keywords belonging to the distribution diagram of the DF values for the keywords within 4 <= sum (DF) <= 10,000.

The keyword extracted by the keyword extracting unit 17 is provided to the analyzing unit 20.

The analyzer 20 may derive expertise information of a person on a keyword basis from keywords extracted from the indexing unit, set a ranking for expertise information, and store the information in an analysis DB.

That is, the expertise information possessed by the person with respect to each of the keywords may be obtained by a combination of a Count of Project Information (i-th Person) including the keyword extracted from the indexing portion and a Count of Paper The total number of articles, the total number of articles, the total number of articles, and the total of the articles. The weight (weight) (Expertise Point) of the person is derived for each keyword, and the following equation is satisfied.

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2

KP i = Expertise Point of i-th Person having Keyword K

PR = Count on Project Information of i-th Person

RI = Count of Paper Information of i-th Person

HP = Count of Paper in Human Resource Information of i-th Person

α = Weight of Project Information

β = Weight of Paper Information

γ = Weight of Paper in Human Resource Information

i = Number of Person having Keyword K

λ = The performance of the manuscripts input by the person is the weight for the SCI class and other papers.

That is, each sum is weighted by α = 0.5, β = 0.3, and γ = 0.2, and scores for expertise information are calculated.

In case of the performance of the above person, a weight value of 0.8 is applied to SCI class paper, and a weight of 0.2 is applied to other papers.

The expertise information of the derived person is stored in the analysis DB in correspondence with each keyword. The table value of the expertise information for the keyword is as shown in FIG.

The expertise information is provided to the knowledge map generator 30.

The knowledge map generation unit 30 is configured to derive a collaboration relationship between a character and a person from a ranking person for the expertise information of the analysis unit 20 to generate a knowledge map that is a collaboration relationship between a source and a target.

That is, the background color map generation unit 30 generates a list of the name, the number of tasks, the number of the performance, the affiliated organization, the task and the performance based on the expertise information of the person of the analysis unit through the attribute information derivation unit 31, The attribute information indicating the expertise information of the person including the institution ID and the institution name to which the person belongs is derived. The table values for the attribute information are as shown in FIG.

Meanwhile, the knowledge map generation unit 30 generates a knowledge map including a link ID, a source of a link, a target, a number of collaborative tasks, and a collaborative performance number through a node information derivation unit 33 Link information is derived. The table values for the node information are as shown in FIG.

Then, the knowledge map deriving unit 35 derives the collaboration relationship between the source and the target as a knowledge map by receiving the linkage information and link information of the derived person. At this time, the knowledge map is as shown in FIG.

The knowledge map, which is a collaboration relationship between the source and the target of the knowledge map generating unit 30, is visualized through the interface unit 40, and then displayed on the screen as shown in FIG.

 That is, the interface unit 40 visualizes and displays the keyword, the matched keyword, and the knowledge map of the knowledge map generation unit 30 stored in the analysis DB.

On the other hand, it extracts keywords from high-quality national R & D information provided by NTIS, derives expertise information of the person based on the relational expression set for the extracted keywords, and generates attribute information and link information for expertise information of the derived person A series of processes for deriving a collaboration map between a source and a target as a knowledge map and providing a collaboration partner search service will be described with reference to FIG.

FIG. 8 is a flowchart illustrating an operation of a system for providing a cooperative partner search service shown in FIG. 1. Referring to FIG. 8, a method for providing a cooperative partner search service according to another embodiment of the present invention will be described.

First, the analysis target table is provided through the step (101) of the indexing unit (100), and each column of the table is analyzed on the basis of the morpheme analysis technique. Then, A document frequency (DF) value is generated from the relational expression.

Then, a distribution map of DF values of the keywords is generated based on the generated DF (Document Frequency) value of the step (103), and a keyword in which the distribution of the DF values of the generated keywords is within a preset predetermined range is extracted (Steps 105 and 107).

Then, the extracted keyword is provided to the analysis unit 20 in step 109.

That is, in step 109, the Count of Project Information of the i-th Person including the extracted keyword, the Count of Paper Information of the i-th Person in the performance information, Weights are assigned to the total of the manuscript's papers, the total number of manuscripts, the total number of papers, (Experiment Point) (Step 111), and stores the expertise information of the person derived through the step 111 in the analysis DB in correspondence with each keyword.

At this time, the expertise information possessed by the person satisfies the following equation.

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2

KP i = Expertise Point of i-th Person having Keyword K

PR = Count on Project Information of i-th Person

RI = Count of Paper Information of i-th Person

HP = Count of Paper in Human Resource Information of i-th Person

α = Weight of Project Information

β = Weight of Paper Information

γ = Weight of Paper in Human Resource Information

i = Number of Person having Keyword K

λ = The performance of the manuscripts input by the person is the weight for the SCI class and other papers.

The expertise information of the person derived from the analysis unit 20 is provided to the knowledge map generation unit 30.

In step 113, the knowledge map generating unit 30 generates a knowledge map based on the expert information of the person in the analyzing unit, such as the name of the person, the number of the tasks, the number of the achievements, the list of the affiliated institutions, , Attribute information indicating expertise information of the person including the institution name is derived.

In step 115, the knowledge map generating unit 30 generates link information indicating a collaboration relationship between the person and the person including the ID of the link, the source of the link, the target, the number of collaborative tasks, In step 117, the collaboration relationship between the source and the target is derived as a knowledge map based on the derived information and link information of the derived person.

Then, the collaborative relationship between the source and the target of the knowledge map is provided to the interface unit through step 117. The interface unit 40 compares the input keyword with the keyword stored in the analysis DB, The knowledge map of the generation unit is displayed by being displayed.

According to an embodiment of the present invention, a keyword is extracted from a table value of analysis target data for high-quality national R & D information provided by NTIS, and the derived personality is derived based on the relational expression set for the extracted keyword, Based on the attribute information and the link information for the specialty information, the knowledge relation between the person and the person is derived as the knowledge map. Then, the expert information of the person on the specific topic inputted by the keyword is determined, It is possible to provide more accurate and specialized service than the list service provided by the expert recommendation service and to provide the researcher with easier and higher quality service than searching for a collaborative relationship or finding an expert .

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Extracts keywords from the table values of the analyzed data for the high quality national R & D information provided by NTIS, derives the expertise information of the person based on the relation set up for the extracted keywords, Based on the link information, we derive the collaborative relationship between the person and the person as knowledge map, determine the personality expertise information about the specific topic entered by the keyword, analyze the network relation and provide it as knowledge map form, Provide more accurate and specialized services than the list type service provided, and provide researchers with search services for collaborative partners that can provide easier and higher quality services than searching for collaborative relationships or searching for experts The accuracy of the operation of the system and method for And reliability, and further, the performance efficiency can be remarkably improved. The present invention is industrially applicable because it is not only a possibility of commercialization or sales of the applied interactive broadcasting system, but also can be practically carried out clearly.

Claims (16)

An indexing unit for analyzing a keyword of a table value of analysis target data provided from the outside through a morpheme analysis technique that is already defined, calculating a DF (Document Frequency) value already defined from the analyzed keyword,
An analyzing unit for deriving expertise information of a person on a keyword basis by keywords based on the keyword extracted by the indexing unit, setting an order of expertise information and storing the information in an analysis DB,
A rank of the expertise information of the analysis unit, a node information indicating the collaboration information between the person and the attribute information about the expertise information is derived from the person, and the collaboration relationship between the source and the target is derived as the knowledge map based on the derived attribute information and the node information A knowledge map generating unit,
And an interface unit for visually displaying the knowledge map of the knowledge map generating unit corresponding to each key word,
The analyzing unit,
A count of Paper Information of i-th Person including a keyword extracted from the indexing unit, a count of paper information of i-th Person in performance information, The total score of each task, the total score of the articles, and the sum of the achievements of the articles are given weight, and the expertise information of the person for each keyword (for example, Expertise Point) is derived,
And stores the expertise information of the derived person in the analysis DB corresponding to each keyword. The apparatus according to claim 1,
Analyzes the respective columns of the table for the analysis target data on the basis of the morpheme analysis technique
Generate DF (Document Frequency) values from previously defined relational expressions for the analyzed keywords,
Generates a distribution map of DF values of keywords based on the generated DF (Document Frequency) value,
And extracts a keyword whose distribution of the DF values of the generated keywords is within a preset predetermined range. 3. The system as claimed in claim 2, wherein the extracted keyword is a keyword having a distribution of DF values of keywords within 4 < = sum (DF) < = 10,000. 3. The method according to claim 2,
Wherein the N-gram method is utilized in an information search and a natural language processing field. delete 5. The system according to claim 4, wherein the expertise information of the person satisfies the following equation.

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2
KP i = Expertise Point of i-th Person having Keyword K
PR = Count on Project Information of i-th Person
RI = Count of Paper Information of i-th Person
HP = Count of Paper in Human Resource Information of i-th Person
α = Weight of Project Information
β = Weight of Paper Information
γ = Weight of Paper in Human Resource Information
i = Number of Person having Keyword K
λ = The performance of the manuscripts input by the person is the weight for the SCI class and other papers.
The information processing apparatus according to claim 1,
Based on the expertise information possessed by the person in the analysis section, attribute information indicating the person name, the number of the tasks, the number of the achievements, the list of the belonging organization, the task and the achievement, the institution ID to which the person belongs, However,
The link information indicating the collaboration relationship between the person and the person including the ID of the link, the source of the link, the target, the number of collaborative tasks,
And derives a collaboration relationship between the source and the target as a knowledge map based on the linkage information and link information of the derived person. The apparatus according to claim 1,
And displaying the matched keyword and the knowledge map of the knowledge map generating unit in a visualized manner by matching the input keyword with the keyword stored in the analysis DB, and displaying the matched keyword and the knowledge map of the knowledge map generating unit. An indexing step of extracting a keyword from the analysis target DB table through a morphological analysis technique, computing a DF (Document Frequency) value from the extracted keyword,
An analysis step of deriving expertise information of a person on a keyword-by-keyword basis with respect to the keyword extracted in the indexing step, setting a rank of expertise information and storing the information in an analysis DB,
A knowledge map generation step of generating a knowledge map of a collaboration relationship between a person and a person by calculating a collaboration relationship based on a predefined relation for a ranking person of the personality of the person in the analysis step,
And an interface step of visualizing and displaying knowledge maps of the knowledge map generating step for each keyword,
Wherein the analyzing step comprises:
A count of Paper Information of i-th Person including a keyword extracted from the indexing step, a count of paper information of i-th Person in performance information, The total score of each task, the total score of the articles, and the sum of the achievements of the articles are given weight, and the expertise information of the person for each keyword (for example, Expertise Point) is derived,
And storing the expertise information of the derived person in the analysis DB in correspondence with each keyword. 10. The method of claim 9,
Extracts keywords based on the morphological analysis technique for each column of the analysis target table
Generate Document Frequency (DF) values from the previously defined relational expressions for the extracted keywords,
Generates a distribution map of DF values of keywords based on the generated DF (Document Frequency) value,
And extracting a keyword having a distribution of DF values of the generated keywords within a predetermined range. 11. The method as claimed in claim 10, wherein the extracted keyword is a keyword having a distribution of DF values of keywords of 4 < = sum (DF) < = 10,000. 12. The method of claim 11,
Wherein the N-gram method is utilized in an information search and a natural language processing field. delete 13. The method of claim 12, wherein the expertise information of the person satisfies the following equation.

If (HP (p) = SCI) then λ = 0.8 else λ = 0.2
KP i = Expertise Point of i-th Person having Keyword K
PR = Count on Project Information of i-th Person
RI = Count of Paper Information of i-th Person
HP = Count of Paper in Human Resource Information of i-th Person
α = Weight of Project Information
β = Weight of Paper Information
γ = Weight of Paper in Human Resource Information
i = Number of Person having Keyword K
λ = The performance of the manuscripts input by the person is the weight for the SCI class and other papers.
11. The method of claim 10,
A list of the affiliated organizations, the tasks and the achievements, the institution IDs of the persons, and the expertise information of the person including the institution name, based on the expertise information of the person created in the analysis step Deriving the attribute information,
The link information indicating the collaboration relationship between the person and the person including the ID of the link, the source of the link, the target, the number of collaborative tasks,
And deriving a collaboration relationship between the source and the target as a knowledge map based on the linkage information and link information of the derived person. 11. The method according to claim 10,
And displaying the keyword and the matched keyword stored in the analysis DB, and the knowledge map derived in the knowledge map generation step, and displaying the displayed knowledge map.

Images