WO2013147237A1 - Expert evaluation device - Google Patents

Expert evaluation device Download PDF

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Publication number
WO2013147237A1
WO2013147237A1 PCT/JP2013/059703 JP2013059703W WO2013147237A1 WO 2013147237 A1 WO2013147237 A1 WO 2013147237A1 JP 2013059703 W JP2013059703 W JP 2013059703W WO 2013147237 A1 WO2013147237 A1 WO 2013147237A1
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evaluation
information
expert
relative
ranking
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PCT/JP2013/059703
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French (fr)
Japanese (ja)
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兵衛 冨田
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株式会社メディカルリサーチアンドテクノロジー
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Publication of WO2013147237A1 publication Critical patent/WO2013147237A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions

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  • the present invention relates to an expert evaluation apparatus that evaluates an expert based on relative information.
  • an expert management apparatus and an expert management method for accumulating and disclosing expert evaluation information via an information communication line such as the Internet are known.
  • information for evaluating the number of surgeons' surgical experience, surgical skills, medical knowledge, person evaluation, academic society activities, research achievements, etc. is accumulated (for example, Patent Document 1).
  • Patent Document 1 information for evaluating the number of surgeons' surgical experience, surgical skills, medical knowledge, person evaluation, academic society activities, research achievements, etc. is accumulated (for example, Patent Document 1).
  • Patent Document 1 In such an expert management apparatus, it is possible to present the degree of expert evaluation based on the accumulated evaluation information.
  • an object of the present invention is to provide an apparatus that can perform an objective ranking in a specialized field of an expert based on relative evaluation information and can perform highly reliable evaluation.
  • An expert evaluation device that accepts and manages the posting of evaluation information among experts in a community of experts in a predetermined specialized field, and is an expert that is subject to evaluation from the poster's terminal.
  • Evaluation information storage means for storing the evaluation information in response to accepting posting of evaluation information including evaluation field information indicating a field and relative evaluation information with other experts other than the expert, Ranking information that reads the evaluation field information and relative evaluation information included in the posted evaluation information and generates expert ranking information based on the relative evaluation information for each specialized field indicated by the evaluation field information
  • An expert evaluation device comprising: a generating means.
  • the expert evaluation device of (1) Since the expert evaluation device of (1) generates ranking information based on relative evaluation information, each contributor only performs a relative evaluation of some experts among the experts to be evaluated. It is possible to generate ranking information of experts with high credibility by generating a single piece of ranking information by combining the relative evaluation information.
  • the ranking information generating means generates the ranking information with a higher weight on the relative evaluation information as the ranking of the poster in the evaluation field information included in the evaluation information is higher.
  • Expert evaluation device generates the ranking information with a higher weight on the relative evaluation information as the ranking of the poster in the evaluation field information included in the evaluation information is higher.
  • the expert evaluation device can generate ranking information with higher weight as the poster's ranking is higher by relative evaluation by the poster. Thereby, ranking information of experts with higher credibility can be generated by placing more importance on the relative evaluation information by contributors with higher credibility in each specialized field.
  • the evaluation information storage means accepts and stores the relative evaluation information as text, and the ranking information generation means reads the text of the relative evaluation information, and performs a plurality of ranking targets by character string analysis.
  • the expert evaluation device according to (1) or (2), wherein the ranking information is generated by determining an expert and a relative evaluation of the plurality of experts.
  • the expert evaluation device (3) reads out the relative evaluation information from the text, so as long as there is a comment for the expert, it is possible to generate ranking information, reducing the burden on the poster, and having higher credibility. Expert ranking information can be generated.
  • the ranking information generation means is larger in the relative evaluation information.
  • the expert evaluation device according to any one of (1) to (3), wherein the ranking information is generated with weighting.
  • the expert evaluation device in (4) reflects the relative evaluation between the contributor himself and other experts who are considered to know the evaluation of his / her skill etc. accurately in the ranking information. Therefore, it is possible to generate ranking information of experts with higher credibility.
  • the objective ranking in the specialized field of the expert is performed to enable highly reliable evaluation.
  • it is a screen image which an expert evaluation apparatus outputs, Comprising: It is an image figure of the input screen for posting evaluation information.
  • FIG. 1 is a conceptual diagram showing a configuration of an expert evaluation information management system 5 including an expert evaluation apparatus 1 according to this embodiment.
  • the expert evaluation information management system 5 is configured by connecting an expert evaluation apparatus 1, a contributor terminal 2, and a viewer terminal 3 via a communication network 4.
  • the expert evaluation information management system 5 having such a configuration is a service for managing a community of experts, and a job introduction service for introducing a part-time job or a job change opportunity to a specialist. Providing users with a service that manages the evaluation of experts in employment placement services.
  • the expert evaluation device 1 is a server that manages the service
  • the contributor terminal 2 and the viewer terminal 3 are terminal devices used by users who use the service.
  • a terminal device used by a contributor who performs expert evaluation is referred to as a contributor terminal 2
  • a terminal device used by a viewer who browses the evaluation performed by the contributor is referred to as a browser terminal 3.
  • the contributor since the experts post and browse the evaluations, the contributor may become a viewer at another opportunity. In other words, the poster terminal 2 may become the browser terminal 3 at another opportunity.
  • the expert evaluation apparatus 1 when the expert evaluation apparatus 1 receives an evaluation of another expert from the poster terminal 2, the expert evaluation apparatus 1 ranks the experts based on the evaluation.
  • the evaluation of other experts may include the contents of “working years ⁇ years” that can be ranked quantitatively, such as “working years are ⁇ years ...”. Some of them contain only contents that are not suitable for quantitative ranking, such as “I can leave my teacher with confidence” and “I have good skills”.
  • the expert evaluation device 1 performs ranking of experts based on evaluations freely posted by contributors.
  • the expert evaluation device 1 ranks the experts based on the relative evaluation included in the evaluation posted by the poster.
  • the relative evaluation used for ranking it is good to use a suitable thing suitably, for example, evaluations, such as "I do not take a close to Mr. A", are mentioned.
  • the experts who have posted such evaluations will be ranked in the same order as teacher A.
  • achieving this invention is demonstrated.
  • FIG. 2 is a block diagram showing a hardware configuration of the expert evaluation device 1 according to an embodiment of the present invention.
  • the expert evaluation apparatus 1 is, for example, a general computer. As shown in FIG. 2, the control unit 11, the storage unit 12, the input unit 13, the output unit 14, and the communication unit 15 are connected to the bus 16. Connected and configured.
  • the control unit 11 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.
  • the CPU calls a program stored in the storage unit 12, ROM, recording medium or the like into a work memory area on the RAM and executes it.
  • the ROM permanently holds a computer boot program, a program such as BIOS, data, and the like.
  • the RAM temporarily holds the loaded program and data, and includes a work area used by the CPU to perform various processes described later.
  • the storage unit 12 is, for example, an HDD (Hard Disk Drive), and stores a program executed by the control unit 11, data necessary for program execution, an OS (Operating System), a database, and the like. These program codes are read by the control unit 11 as necessary, transferred to the RAM, and read and executed by the CPU.
  • HDD Hard Disk Drive
  • OS Operating System
  • the input unit 13 is, for example, an input device such as a keyboard, a mouse, a touch panel, a pointing device such as a tablet, or a numeric keypad, and outputs input operation control information to the control unit 11.
  • the output unit 14 includes a display unit, and includes, for example, a display device such as a liquid crystal panel or a CRT monitor, and a logic circuit (video adapter or the like) for executing display processing in cooperation with the display device.
  • the communication unit 15 controls communication performed between the poster terminal 2 and the viewer terminal 3 via a communication network including the Internet.
  • the bus 16 is a path that mediates input / output of control signals, data signals, and the like between the devices.
  • FIG. 3 is a functional block diagram showing a functional configuration for executing the expert evaluation process among the functional configurations of the expert evaluation apparatus 1 as described above.
  • the expert evaluation process refers to a series of processes in which a relative evaluation is extracted from the evaluation received from the poster terminal 2 and ranking information data is generated based on the relative evaluation.
  • the control unit 11 When executing the ranking information generating process, the control unit 11 functions as the evaluation information receiving unit 41 and the ranking information generating unit 42. Further, in one area of the storage unit 12, an evaluation information DB 51, a doctor information DB 52, and an evaluation dictionary DB 53 are provided.
  • the evaluation information receiving means 41 receives an evaluation posting for an expert to be evaluated (hereinafter sometimes referred to as an “evaluation target person”) from the poster terminal 2 via the communication unit 15. For example, the evaluation information receiving means 41 submits the evaluation including the evaluation input in the input fields V1 and V2 of the input screen via the input screen of the evaluation posting shown in FIGS. Is accepted from the poster terminal 2. Further, when the evaluation information receiving unit 41 receives an evaluation posting from the poster terminal 2, the evaluation information receiving unit 41 stores the received evaluation in the evaluation information DB 51.
  • the evaluation information DB 51 functions as an evaluation information storage unit and stores evaluation information including an evaluation received from a poster. More specifically, when the evaluation information receiving unit 41 receives an evaluation post, the evaluation information DB 51 associates an evaluation information ID for identifying each post with a doctor ID for identifying the expert of the evaluation target, evaluation The doctor ID for identifying the contributor who made the posting, the evaluation field indicating the specialized field in which the contributor evaluated the evaluation target person, the evaluation contents for the evaluation target person by the contributor, and the like are stored. Further, the evaluation information DB 51 stores the score as a score in the specialized field of the evaluation target person when the later-described ranking information generation unit 42 calculates the score based on the evaluation content.
  • the identification information corresponding to the doctor ID in the doctor information DB 52 is used for the doctor ID of the evaluation subject and the doctor ID of the poster.
  • the doctor information DB 52 stores information on an expert (doctor) who uses a service provided by the expert evaluation device 1. That is, the doctor information DB 52 stores a doctor's profile or the like specified by the doctor ID in association with the doctor ID that identifies the expert. As the profile of the doctor, in addition to the name of the doctor and the department where he works, various information such as specialized fields and creditworthiness will be adopted. Here, since it is difficult for an expert such as a doctor with a high level of expertise to perform an appropriate evaluation for those who are not knowledgeable, in this embodiment, the experts evaluate each other. It is allowed to meet each other.
  • the reliability for the specialized field is set for each expert, and the evaluation target person is ranked based on the reliability.
  • an evaluation posted by an expert with high reliability in a certain specialized field is more reliable than an evaluation posted by an expert with low reliability in the specialized field. Therefore, a high weight is applied to a score calculated from an evaluation performed by an expert with high reliability, and a low weight is applied to a score calculated from an evaluation performed by an expert with low reliability.
  • the reliability with respect to the expert can be set as appropriate.
  • the reliability may be determined based on the rankings accumulated in the past, or may be determined based on the number of posts in the past evaluation or the quality of posts. Also good. In other words, the higher the ranking, the higher the level of reliability, the higher the number of posts, the higher the level of reliability, and the higher the quality of the posts, the higher the level of reliability. It is good as well. Therefore, it is preferable that the reliability for each expert is updated at a necessary timing as appropriate. In FIG. 7, only one reliability is set for one expert, but the reliability may be set for each specialized field.
  • the ranking information generation means 42 generates ranking information indicating the ranking of the evaluation subject in the specialized field based on the evaluation contents stored in the evaluation information DB 51.
  • the ranking information is generated by calculating a score based on the evaluation contents, weighting the calculated score, and ranking based on the weighted score. Therefore, the ranking information generation unit 42 includes an evaluation information extraction unit 421, a relative evaluation extraction unit 422, a score calculation unit 423, a weight determination unit 424, and a score totaling unit 425.
  • Evaluation information extraction means 421 operates every predetermined period and extracts evaluation information from the evaluation information DB 51.
  • the evaluation information extracted from the evaluation information DB 51 includes at least the doctor ID of the evaluation subject, the doctor ID of the poster, the evaluation field, and the evaluation content.
  • the evaluation content and the evaluation field are supplied to the relative evaluation extraction unit 422 and the score calculation unit 423, and are used to calculate a score based on the posted evaluation content.
  • the doctor ID of the poster and the evaluation field are supplied to the weighting determination unit 424 and used for weighting the calculated score.
  • the evaluation subject's doctor ID and the evaluation field are supplied to the score counting means 425 and used to generate ranking information for each specialized field.
  • the relative evaluation extraction unit 422 extracts words (hereinafter referred to as “relative evaluation words”) used for relative evaluation from the evaluation contents supplied from the evaluation information extraction unit 421.
  • the relative evaluation word is extracted by determining a predetermined phrase unit included in the evaluation contents and searching the evaluation dictionary DB 53 for each determined phrase unit.
  • the relative evaluation extraction unit 422 performs a word processing such as morphological analysis on the evaluation content, thereby extracting a suitable unit word from the evaluation content.
  • the relative evaluation extraction unit 422 extracts the relative evaluation word by searching the evaluation dictionary DB 53 based on the extracted word.
  • the unit of words extracted by language processing can be set appropriately as a unit suitable for evaluation. A simple word composed of one morpheme may be used as a unit, and a synthetic word composed of a plurality of morphemes may be used as a unit. Also good.
  • the evaluation dictionary DB 53 stores relative evaluation information for relatively evaluating the evaluation target person based on the evaluation contents posted by the poster.
  • the relative evaluation information refers to various pieces of information set for each relative evaluation word.
  • the word type and content indicating the type of the relative evaluation word are used.
  • words that are suitable for scoring such as “Excellent” and “Delighted”, “From me”, “From teacher A”, “In this field”, etc. In this way, words that are not suitable for scoring but serve as criteria for scoring are also included.
  • “Word type” is information for distinguishing these, and in the present embodiment, a word indicating a reference target when scoring an evaluation target person is referred to as a “reference point word” and is evaluated for the reference. A word used for adding the subject's score is called an “evaluation word”.
  • the score calculation reference point is defined as the content for the “reference point word”, and the score for each word is defined for the “evaluation word”. As shown in FIG. 9, as a reference point, the score of another expert, the average value of the expert's score in the evaluated specialty field, etc. can be adopted.
  • the evaluation dictionary DB 53 is preferably updated at a necessary timing as appropriate.
  • the relative evaluation extraction unit 422 searches the evaluation dictionary DB 53 using the word extracted from the evaluation content as a key, and specifies the relative evaluation word from the words constituting the evaluation content. Then, the relative evaluation extraction unit 422 supplies the specified relative evaluation word to the score calculation unit 423 together with the relative evaluation information set in the relative evaluation word.
  • the score calculation unit 423 calculates a score for the evaluation content based on the relative evaluation information and supplies it to the weight determination unit 424. Specifically, the score calculation unit 423 obtains a reference score serving as a reference based on the relative evaluation word of the word type “reference point word”, and is defined as the relative evaluation word of the word type “evaluation word”. The score for the evaluation content is calculated by adding the score obtained. For example, when “I” or “I want to learn” is extracted as a relative evaluation word from the evaluation content of the evaluation information ID “0001” stored in the evaluation information DB 51, the score calculation means 423 uses the word type “reference point word”. From the relative evaluation word “I” of “”, the score of the poster in the evaluation field is acquired as a reference score. Thereafter, the score “+10” corresponding to the relative evaluation word “I want to apprentice” of the word type “evaluation word” is added to the reference score, and the score of the evaluation target person in the evaluation field is calculated.
  • the weighting determination unit 424 acquires the reliability of the poster from the doctor information DB 52 based on the doctor ID and the evaluation field of the poster supplied from the evaluation information extraction unit 421, and the evaluation subject's evaluation based on the acquired reliability. Correct the score.
  • the reliability set for the poster may be weighted. That is, when the reference point is the poster's score, the reliability is set to “1.0” by weighting the reliability “0.9” set for the poster, and the score may be corrected. Good. If the weight determination means 424 correct
  • the score totaling unit 425 operates after the weighting determination unit 424 calculates scores for all evaluation contents, and totals the scores stored in the evaluation information DB 51 for each evaluation target person and each evaluation field, and specializes in that field. Generate house ranking information.
  • FIG. 8 shows a screen in which the top first expert (doctor) is shown for each specialized field based on the ranking information generated.
  • the ranking information display area V3 summary information about the first expert in each specialized field is displayed.
  • the evaluation information receiving means 41 receives an evaluation post from the poster terminal 2 (step S1).
  • the evaluation information accepting unit 41 accepts information including at least text information indicating a doctor ID of a person to be evaluated, a doctor ID of a poster, an evaluation field, and evaluation contents.
  • the evaluation information receiving means 41 stores the information received in step S1 in the evaluation information DB 51 (step S2).
  • the ranking information generating means 42 determines whether a predetermined period has elapsed.
  • the predetermined period can be set as appropriate, such as the degree of accumulation of evaluation information or a preset time. If the predetermined period has elapsed, the process proceeds to step S4. If the predetermined period has not elapsed, the process returns to step S1 to accept an evaluation posting. In step S4, the ranking information generation unit 42 executes ranking information generation processing described later, and ends the expert evaluation processing.
  • the evaluation information extraction unit 421 extracts evaluation information from the evaluation information DB 51 (step S41). Subsequently, the relative evaluation extraction unit 422 analyzes the evaluation contents of the extracted evaluation information and extracts a relative evaluation word. Further, the relative evaluation extraction unit 422 extracts relative evaluation information corresponding to the extracted relative evaluation word (step S42).
  • the score calculation unit 423 calculates the score of the evaluation information based on the extracted relative evaluation information (step S43). That is, the score calculation unit 423 acquires a reference score based on the relative evaluation word of the word type “reference point word”, and uses the score set in the relative evaluation word of the word type “evaluation word” with respect to the reference score. to add.
  • the weight determination means 424 extracts the poster's reliability from the doctor information DB 52 and corrects the score calculated in step 43 based on the reliability (step S44). Thereafter, the weighting determination unit 424 stores the corrected score in the evaluation information DB 51 (step S45), and determines whether scoring for all evaluation contents has been completed (step S46). At this time, if scoring has been completed, the process proceeds to step S47, and if not yet completed, the process returns to step S41, and scoring is performed on evaluation contents that have not been completed.
  • step S47 the score totaling unit 425 totals the scores stored in the evaluation information DB 51 for each evaluation target person and each evaluation field, and generates ranking information of experts in the field based on the total score. Thereby, the ranking information generation process ends.
  • expert ranking information is generated from the evaluation posted by the poster.
  • the expert evaluation apparatus 1 scores experts relatively based on the relative evaluation words stored in the evaluation dictionary DB 53. That is, scoring is performed with different reference points according to the content of the evaluation posted by the poster. As a result, even if the word types “evaluation words” suitable for scoring are the same, different scores are calculated when the reference points are different. For example, in the evaluations performed by expert A such as “Better than me”, “Excellent in this field”, “Better than teacher B”, the evaluation words are all “Excellent” and the same, Since the reference points are different, different scores can be calculated. Accordingly, it is possible to appropriately score a free post made by the poster, and it is possible to generate ranking information of experts with high credibility.
  • the expert evaluation apparatus 1 to which the present invention is applied has been described with the subject expert as a doctor. If necessary, specialists, for example, in addition to doctors, pharmacists, nurses, lawyers, patent attorneys, judicial scriveners, real estate appraisers, certified public accountants, tax accountants, professionals with national qualifications, consultants, Various types of professionals that do not require qualifications such as system engineers can be employed.
  • the series of processes described above can be executed by hardware or can be executed by software.
  • the functional configuration of FIG. 3 is merely an example, and is not particularly limited. That is, it is sufficient that the expert evaluation apparatus 1 has a function capable of executing the above-described series of processes as a whole, and what functional blocks are used to realize this function is limited to the example of FIG. Not.
  • one functional block may be configured by hardware alone, software alone, or a combination thereof.
  • a program constituting the software is installed on a computer or the like from a network or a recording medium.
  • the computer may be a computer incorporated in dedicated hardware. Further, the computer may be a computer capable of executing various functions by installing various programs, for example, a general-purpose personal computer.
  • the recording medium including such a program is not only configured by the media input / output unit 17 of FIG. 2 distributed separately from the apparatus main body in order to provide the program to the administrator, but is also preinstalled in the apparatus main body. It is composed of a recording medium provided to the user in a state.
  • the media provided to the media input / output unit 17 is composed of, for example, a magnetic disk (including a floppy disk), an optical disk, a magneto-optical disk, or the like.
  • the optical disk is composed of, for example, a CD-ROM (Compact Disk-Read Only Memory), a DVD (Digital Versatile Disk), or the like.
  • the magneto-optical disk is constituted by an MD (Mini-Disk) or the like.
  • the recording medium provided to the user in a state of being preliminarily incorporated in the apparatus main body includes, for example, a hard disk included in the storage unit 12 in FIG.
  • the step of describing the program recorded on the recording medium is not limited to the processing performed in time series along the order, but is not necessarily performed in time series, either in parallel or individually.
  • the process to be executed is also included.
  • the term “system” means an overall apparatus configured by a plurality of devices, a plurality of means, and the like.

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Abstract

An object of the present invention is to perform an objective ranking in a field of expertise of an expert based on relative information data so as to enable a credible evaluation. An expert evaluation device, which receives and manages the contribution of mutual expert evaluation data in an expert community in a predetermined field of expertise, comprises: storage means which stores evaluation data according to the reception of the contribution of evaluation data including evaluation field data showing the field of expertise subject to the evaluation from a terminal of a contributor with respect to the expert and relative evaluation data with another expert other than said expert; evaluation data extraction means reading the evaluation field data and the relative evaluation data included in the contribution of the stored evaluation data; and ranking generation means generating expert ranking data based on the relative evaluation data in each field of expertise shown by the evaluation field data.

Description

専門家評価装置Expert evaluation device
　本発明は、専門家を相対情報に基づいて評価する専門家評価装置に関する。 The present invention relates to an expert evaluation apparatus that evaluates an expert based on relative information.
　従来、インターネット等の情報通信回線を介して、専門家の評価情報を集積して公開する専門家管理装置、専門家管理方法が知られている。このような専門家管理装置においては、例えば、医師の手術経験数、手術の技量、診療知識、人物評価、学会活動、研究業績等について評価する情報が蓄積される（例えば、特許文献１）。このような専門家管理装置においては、集積した評価情報に基づいて、専門家の評価度合いを提示することが可能である。 Conventionally, an expert management apparatus and an expert management method for accumulating and disclosing expert evaluation information via an information communication line such as the Internet are known. In such an expert management apparatus, for example, information for evaluating the number of surgeons' surgical experience, surgical skills, medical knowledge, person evaluation, academic society activities, research achievements, etc. is accumulated (for example, Patent Document 1). In such an expert management apparatus, it is possible to present the degree of expert evaluation based on the accumulated evaluation information.
特開２００４－３５５６１３号公報JP 2004-355613 A
　しかしながら、このような評価を行う場合に、例えば手術経験数等のように、数字によって客観的に評価できるものによって評価を行うことは容易であるが、手術の技量等について評価する場合には、その技量を示す尺度となる基準がないために客観的な評価を行うことができず、信憑性の低い評価情報しか提供することができなかった。 However, when performing such an evaluation, for example, the number of surgical experiences, etc., such as the number of surgical experiences, it is easy to evaluate by what can be objectively evaluated by numbers, Since there is no standard that can be used as a measure for the skill level, objective evaluation cannot be performed, and only evaluation information with low credibility can be provided.
　そこで、本発明は、相対評価情報に基づいて、専門家の専門分野における客観的なランキングを行い、信憑性の高い評価を可能とする装置の提供を目的とする。 Therefore, an object of the present invention is to provide an apparatus that can perform an objective ranking in a specialized field of an expert based on relative evaluation information and can perform highly reliable evaluation.
　（１）　所定の専門分野における専門家のコミュニティにおいて専門家相互の評価情報の投稿を受け付けて管理する専門家評価装置であって、投稿者の端末から、専門家について、評価の対象となる専門分野を示す評価分野情報と、当該専門家以外の他の専門家との相対評価情報と、を含む評価情報の投稿を受け付けたことに応じて、当該評価情報を記憶する評価情報記憶手段と、前記記憶した評価情報の投稿に含まれる評価分野情報と相対評価情報とを読み出して、前記評価分野情報が示す専門分野毎に、前記相対評価情報に基づいて専門家のランキング情報を生成するランキング情報生成手段と、を備える専門家評価装置。 (1) An expert evaluation device that accepts and manages the posting of evaluation information among experts in a community of experts in a predetermined specialized field, and is an expert that is subject to evaluation from the poster's terminal. Evaluation information storage means for storing the evaluation information in response to accepting posting of evaluation information including evaluation field information indicating a field and relative evaluation information with other experts other than the expert, Ranking information that reads the evaluation field information and relative evaluation information included in the posted evaluation information and generates expert ranking information based on the relative evaluation information for each specialized field indicated by the evaluation field information An expert evaluation device comprising: a generating means.
　（１）の専門家評価装置は、相対評価情報に基づいてランキング情報を生成するため、夫々の投稿者が評価対象の専門家のうち一部の専門家同士の相対評価を行うだけで、これらの相対評価情報を組み合わせてひとつのまとまったランキング情報を生成することにより、信憑性の高い専門家のランキング情報を生成することができる。 Since the expert evaluation device of (1) generates ranking information based on relative evaluation information, each contributor only performs a relative evaluation of some experts among the experts to be evaluated. It is possible to generate ranking information of experts with high credibility by generating a single piece of ranking information by combining the relative evaluation information.
　（２）　前記ランキング情報生成手段は、前記評価情報が含む評価分野情報における投稿者のランキングが高いほど、前記相対評価情報により大きな重みを付けて前記ランキング情報を生成する、（１）に記載の専門家評価装置。 (2) The ranking information generating means generates the ranking information with a higher weight on the relative evaluation information as the ranking of the poster in the evaluation field information included in the evaluation information is higher. Expert evaluation device.
　（２）の専門家評価装置は、投稿者のランキングが高いほど当該投稿者による相対評価により大きな重みを付けてランキング情報を生成することができる。これにより、夫々の専門分野において信用度のより高い投稿者による相対評価情報をより重視してランキングすることによって、より信憑性の高い専門家のランキング情報を生成することができる。 (2) The expert evaluation device can generate ranking information with higher weight as the poster's ranking is higher by relative evaluation by the poster. Thereby, ranking information of experts with higher credibility can be generated by placing more importance on the relative evaluation information by contributors with higher credibility in each specialized field.
　（３）　前記評価情報記憶手段は、前記相対評価情報をテキストとして受け付けて記憶し、前記ランキング情報生成手段は、前記相対評価情報のテキストを読み出して、文字列解析によりランキングの対象となる複数の専門家と当該複数の専門家の相対的な評価とを判定して前記ランキング情報を生成する、（１）又は（２）に記載の専門家評価装置。 (3) The evaluation information storage means accepts and stores the relative evaluation information as text, and the ranking information generation means reads the text of the relative evaluation information, and performs a plurality of ranking targets by character string analysis. The expert evaluation device according to (1) or (2), wherein the ranking information is generated by determining an expert and a relative evaluation of the plurality of experts.
　（３）の専門家評価装置は、テキストから相対評価情報を読み出すため、専門家に対するコメントさえあれば、ランキング情報を生成することができ、投稿者の負担を低減しつつ、より信憑性の高い専門家のランキング情報を生成することができる。 The expert evaluation device (3) reads out the relative evaluation information from the text, so as long as there is a comment for the expert, it is possible to generate ranking information, reducing the burden on the poster, and having higher credibility. Expert ranking information can be generated.
　（４）　前記ランキング情報生成手段は、相対評価情報が、当該相対評価情報を含む評価情報を投稿した投稿者を、評価の対象とする専門家の一として含む場合に、当該相対評価情報により大きな重みを付けて前記ランキング情報を生成する、（１）乃至（３）のいずれかに記載の専門家評価装置。 (4) If the relative evaluation information includes a contributor who posted the evaluation information including the relative evaluation information as one of the experts to be evaluated, the ranking information generation means is larger in the relative evaluation information. The expert evaluation device according to any one of (1) to (3), wherein the ranking information is generated with weighting.
　（４）の専門家評価装置は、自らの技量等の評価を正確に知っていると考えられる投稿者自身と他の専門家との相対的な評価をより大きな重みを付けてランキング情報に反映できるため、より信憑性の高い専門家のランキング情報を生成することができる。 The expert evaluation device in (4) reflects the relative evaluation between the contributor himself and other experts who are considered to know the evaluation of his / her skill etc. accurately in the ranking information. Therefore, it is possible to generate ranking information of experts with higher credibility.
　本発明によれば、相対評価情報に基づいて、専門家の専門分野における客観的なランキングを行い、信憑性の高い評価を可能とする According to the present invention, based on the relative evaluation information, the objective ranking in the specialized field of the expert is performed to enable highly reliable evaluation.
本発明の一実施形態による専門家評価情報管理システムの構成を示す概念図である。It is a conceptual diagram which shows the structure of the expert evaluation information management system by one Embodiment of this invention. 本発明の一実施形態に係る専門家評価装置のハードウェアの構成を示すブロック図である。It is a block diagram which shows the hardware constitutions of the expert evaluation apparatus which concerns on one Embodiment of this invention. 本発明の一実施形態に係る専門家評価装置の機能的構成を示す機能ブロック図である。It is a functional block diagram which shows the functional structure of the expert evaluation apparatus which concerns on one Embodiment of this invention. 本発明の一実施形態において、専門家評価装置が出力する画面イメージであって、評価情報を投稿するための入力画面のイメージ図である。In one Embodiment of this invention, it is a screen image which an expert evaluation apparatus outputs, Comprising: It is an image figure of the input screen for posting evaluation information. 本発明の一実施形態において、専門家評価装置が出力する画面イメージであって、評価情報を投稿するための入力画面のイメージ図である。In one Embodiment of this invention, it is a screen image which an expert evaluation apparatus outputs, Comprising: It is an image figure of the input screen for posting evaluation information. 図３の機能ブロック図のうち、評価情報ＤＢのデータ構造を示す図である。It is a figure which shows the data structure of evaluation information DB among the functional block diagrams of FIG. 図３の機能ブロック図のうち、医師情報ＤＢのデータ構造を示す図である。It is a figure which shows the data structure of doctor information DB among the functional block diagrams of FIG. 本発明の一実施形態において、専門家評価装置が出力する画面イメージであって、ランキング情報を表示している画面のイメージ図である。In one Embodiment of this invention, it is a screen image which an expert evaluation apparatus outputs, Comprising: It is an image figure of the screen which is displaying ranking information. 図３の機能ブロック図のうち、評価辞書ＤＢのデータ構造を示す図である。It is a figure which shows the data structure of evaluation dictionary DB among the functional block diagrams of FIG. 図３の機能的構成を有する図２の専門家評価装置が実行する専門家評価処理の流れを説明するフローチャートである。It is a flowchart explaining the flow of the expert evaluation process which the expert evaluation apparatus of FIG. 2 which has the functional structure of FIG. 3 performs. 図３の機能的構成を有する図２の専門家評価装置が実行する専門家評価処理のうち、ランキング情報生成処理の流れを説明するフローチャートである。It is a flowchart explaining the flow of a ranking information generation process among the expert evaluation processes which the expert evaluation apparatus of FIG. 2 which has the functional structure of FIG. 3 performs.
　以下、図面を参照して、本発明の一の実施形態について説明する。図１は、本実施形態による専門家評価装置１を含む専門家評価情報管理システム５の構成を示す概念図である。
　専門家評価情報管理システム５は、専門家評価装置１と、投稿者端末２と、閲覧者端末３と、が通信ネットワーク４を介して接続されて構成される。このような構成の専門家評価情報管理システム５は、専門家のコミュニティを管理するサービス、専門家にアルバイトや転職の機会を紹介する職業紹介サービスであり、より詳細には、当該コミュニティ管理サービスや職業紹介サービスにおいて専門家の評価を管理するサービスを利用者に対して提供する。 Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a conceptual diagram showing a configuration of an expert evaluation information management system 5 including an expert evaluation apparatus 1 according to this embodiment.
The expert evaluation information management system 5 is configured by connecting an expert evaluation apparatus 1, a contributor terminal 2, and a viewer terminal 3 via a communication network 4. The expert evaluation information management system 5 having such a configuration is a service for managing a community of experts, and a job introduction service for introducing a part-time job or a job change opportunity to a specialist. Providing users with a service that manages the evaluation of experts in employment placement services.
　ここで、専門家評価装置１は、上記サービスを管理するサーバであり、投稿者端末２及び閲覧者端末３は、上記サービスを利用する利用者が用いる端末装置である。本実施形態では、専門家の評価を行う投稿者が用いる端末装置を投稿者端末２と呼び、当該投稿者が行った評価を閲覧する閲覧者が用いる端末装置を閲覧者端末３と呼ぶ。なお、本実施形態では、専門家同士が評価の投稿及び閲覧を行うこととしているため、投稿者は別の機会では閲覧者となることがある。言い換えると、投稿者端末２は別の機会では閲覧者端末３となることがある。 Here, the expert evaluation device 1 is a server that manages the service, and the contributor terminal 2 and the viewer terminal 3 are terminal devices used by users who use the service. In the present embodiment, a terminal device used by a contributor who performs expert evaluation is referred to as a contributor terminal 2, and a terminal device used by a viewer who browses the evaluation performed by the contributor is referred to as a browser terminal 3. In the present embodiment, since the experts post and browse the evaluations, the contributor may become a viewer at another opportunity. In other words, the poster terminal 2 may become the browser terminal 3 at another opportunity.
　このような専門家評価情報管理システム５では、専門家評価装置１は、投稿者端末２から他の専門家の評価を受け付けると、当該評価に基づいて専門家をランキングする。ここで、他の専門家の評価には、「勤務年数○年であり・・・」のように定量的にランキングを行うことの可能な内容「勤務年数○年」が含まれていることもあれば、「先生になら安心して任せられる」、「腕が良い」等のように定量的なランキングに適さない内容しか含まれていないものもある。特に、他の専門家の評価を広く受け付けるためには、投稿者の自由な評価投稿を認める必要があり、結果として定量的なランキングに適さない評価が増えてしまう。
　この点、本実施形態の専門家評価装置１は、投稿者から自由に投稿される評価に基づいて専門家のランキングを行う。詳細は後述するが、専門家評価装置１は、投稿者から投稿された評価に含まれる相対評価に基づいて専門家のランキングを行う。なお、ランキングに用いる相対評価については、適宜好適なものを用いることとしてよく、一例としては「Ａ先生に引けをとらない」等という評価が挙げられる。このような評価が投稿された専門家については、Ａ先生と同程度の順位付けが行われることになる。
　以下、本発明を実現するための専門家評価装置１の具体的な構成について説明する。 In such an expert evaluation information management system 5, when the expert evaluation apparatus 1 receives an evaluation of another expert from the poster terminal 2, the expert evaluation apparatus 1 ranks the experts based on the evaluation. Here, the evaluation of other experts may include the contents of “working years ○ years” that can be ranked quantitatively, such as “working years are ○ years ...”. Some of them contain only contents that are not suitable for quantitative ranking, such as “I can leave my teacher with confidence” and “I have good skills”. In particular, in order to widely accept the evaluations of other experts, it is necessary to allow the posters to submit free evaluations, resulting in an increase in evaluations that are not suitable for quantitative ranking.
In this regard, the expert evaluation device 1 according to the present embodiment performs ranking of experts based on evaluations freely posted by contributors. Although details will be described later, the expert evaluation device 1 ranks the experts based on the relative evaluation included in the evaluation posted by the poster. In addition, about the relative evaluation used for ranking, it is good to use a suitable thing suitably, for example, evaluations, such as "I do not take a close to Mr. A", are mentioned. The experts who have posted such evaluations will be ranked in the same order as teacher A.
Hereinafter, the specific structure of the expert evaluation apparatus 1 for implement | achieving this invention is demonstrated.
　図２は、本発明の一実施形態に係る専門家評価装置１のハードウェアの構成を示すブロック図である。
　専門家評価装置１は、例えば一般的なコンピュータであり、図２に示すように、制御部１１と、記憶部１２と、入力部１３と、出力部１４と、通信部１５とがバス１６を介して接続されて構成される。 FIG. 2 is a block diagram showing a hardware configuration of the expert evaluation device 1 according to an embodiment of the present invention.
The expert evaluation apparatus 1 is, for example, a general computer. As shown in FIG. 2, the control unit 11, the storage unit 12, the input unit 13, the output unit 14, and the communication unit 15 are connected to the bus 16. Connected and configured.
　制御部１１は、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｓｅｓｓ　Ｍｅｍｏｒｙ）等により構成される。
　ＣＰＵは、記憶部１２、ＲＯＭ、記録媒体等に格納されるプログラムをＲＡＭ上のワークメモリ領域に呼び出して実行する。ＲＯＭは、コンピュータのブートプログラムやＢＩＯＳ等のプログラム、データ等を恒久的に保持する。ＲＡＭは、ロードしたプログラムやデータを一時的に保持すると共に、ＣＰＵが後述する各種処理を行うために使用するワークエリアを備える。 The control unit 11 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.
The CPU calls a program stored in the storage unit 12, ROM, recording medium or the like into a work memory area on the RAM and executes it. The ROM permanently holds a computer boot program, a program such as BIOS, data, and the like. The RAM temporarily holds the loaded program and data, and includes a work area used by the CPU to perform various processes described later.
　記憶部１２は、例えば、ＨＤＤ（ハードディスクドライブ）であり、制御部１１が実行するプログラムや、プログラム実行に必要なデータ、ＯＳ（オペレーティング・システム）、データベース等が格納されている。これらのプログラムコードは、制御部１１により必要に応じて読み出されてＲＡＭに移され、ＣＰＵに読み出されて実行される。 The storage unit 12 is, for example, an HDD (Hard Disk Drive), and stores a program executed by the control unit 11, data necessary for program execution, an OS (Operating System), a database, and the like. These program codes are read by the control unit 11 as necessary, transferred to the RAM, and read and executed by the CPU.
　入力部１３は、例えば、キーボード、マウス、タッチパネル、タブレット等のポインティング・デバイス、テンキー等の入力装置であり、入力された操作制御情報を制御部１１へ出力する。
　出力部１４は、表示部を含み、例えば液晶パネル、ＣＲＴモニタ等のディスプレイ装置と、ディスプレイ装置と連携して表示処理を実行するための論理回路（ビデオアダプタ等）で構成される。
　通信部１５は、インターネットを含む通信ネットワークを介して投稿者端末２及び閲覧者端末３等との間で行う通信を制御する。
　バス１６は、各装置間の制御信号、データ信号等の入出力を媒介する経路である。 The input unit 13 is, for example, an input device such as a keyboard, a mouse, a touch panel, a pointing device such as a tablet, or a numeric keypad, and outputs input operation control information to the control unit 11.
The output unit 14 includes a display unit, and includes, for example, a display device such as a liquid crystal panel or a CRT monitor, and a logic circuit (video adapter or the like) for executing display processing in cooperation with the display device.
The communication unit 15 controls communication performed between the poster terminal 2 and the viewer terminal 3 via a communication network including the Internet.
The bus 16 is a path that mediates input / output of control signals, data signals, and the like between the devices.
　図３は、このような専門家評価装置１の機能的構成のうち、専門家評価処理を実行するための機能的構成を示す機能ブロック図である。ここで、専門家評価処理とは、投稿者端末２から受け付けた評価から相対評価を抽出して、当該相対評価に基づいてランキング情報のデータを生成する一連の処理をいう。 FIG. 3 is a functional block diagram showing a functional configuration for executing the expert evaluation process among the functional configurations of the expert evaluation apparatus 1 as described above. Here, the expert evaluation process refers to a series of processes in which a relative evaluation is extracted from the evaluation received from the poster terminal 2 and ranking information data is generated based on the relative evaluation.
　ランキング情報生成処理を実行する場合には、制御部１１は、評価情報受付手段４１及びランキング情報生成手段４２として機能する。また、記憶部１２の一領域には、評価情報ＤＢ５１、医師情報ＤＢ５２、及び評価辞書ＤＢ５３が設けられる。 When executing the ranking information generating process, the control unit 11 functions as the evaluation information receiving unit 41 and the ranking information generating unit 42. Further, in one area of the storage unit 12, an evaluation information DB 51, a doctor information DB 52, and an evaluation dictionary DB 53 are provided.
　評価情報受付手段４１は、評価対象の専門家（以下「評価対象者」と呼ぶことがある）に対する評価の投稿を通信部１５を介して投稿者端末２から受け付ける。例えば、評価情報受付手段４１は、投稿者端末２に提供された図４，５に示す評価投稿の入力画面を介して、即ち当該入力画面の入力欄Ｖ１、Ｖ２に入力された評価を含む投稿を投稿者端末２から受け付ける。
　また、評価情報受付手段４１は、投稿者端末２から評価の投稿を受け付けると、受け付けた評価を評価情報ＤＢ５１に記憶する。 The evaluation information receiving means 41 receives an evaluation posting for an expert to be evaluated (hereinafter sometimes referred to as an “evaluation target person”) from the poster terminal 2 via the communication unit 15. For example, the evaluation information receiving means 41 submits the evaluation including the evaluation input in the input fields V1 and V2 of the input screen via the input screen of the evaluation posting shown in FIGS. Is accepted from the poster terminal 2.
Further, when the evaluation information receiving unit 41 receives an evaluation posting from the poster terminal 2, the evaluation information receiving unit 41 stores the received evaluation in the evaluation information DB 51.
　ここで、図６を参照して、評価情報ＤＢ５１について説明する。評価情報ＤＢ５１は、評価情報記憶手段として機能し、投稿者から受け付けた評価を含む評価情報を記憶する。より詳細には、評価情報ＤＢ５１は、評価情報受付手段４１が評価の投稿を受け付けると、個々の投稿を識別する評価情報ＩＤに対応付けて、評価対象者の専門家を識別する医師ＩＤ、評価の投稿を行った投稿者を識別する医師ＩＤ、投稿者が評価対象者を評価した専門分野を示す評価分野、及び投稿者による評価対象者に対する評価内容等を記憶する。また、評価情報ＤＢ５１は、後述のランキング情報生成手段４２が評価内容に基づきスコアを算出すると、評価対象者の専門分野におけるスコアとして当該スコアを記憶する。
　なお、評価対象者の医師ＩＤ及び投稿者の医師ＩＤは、医師情報ＤＢ５２の医師ＩＤと対応する識別情報が用いられている。 Here, the evaluation information DB 51 will be described with reference to FIG. The evaluation information DB 51 functions as an evaluation information storage unit and stores evaluation information including an evaluation received from a poster. More specifically, when the evaluation information receiving unit 41 receives an evaluation post, the evaluation information DB 51 associates an evaluation information ID for identifying each post with a doctor ID for identifying the expert of the evaluation target, evaluation The doctor ID for identifying the contributor who made the posting, the evaluation field indicating the specialized field in which the contributor evaluated the evaluation target person, the evaluation contents for the evaluation target person by the contributor, and the like are stored. Further, the evaluation information DB 51 stores the score as a score in the specialized field of the evaluation target person when the later-described ranking information generation unit 42 calculates the score based on the evaluation content.
The identification information corresponding to the doctor ID in the doctor information DB 52 is used for the doctor ID of the evaluation subject and the doctor ID of the poster.
　続いて、図７を参照して、医師情報ＤＢ５２について説明する。医師情報ＤＢ５２は、専門家評価装置１が提供するサービスを利用する専門家（医師）に関する情報を記憶する。即ち、医師情報ＤＢ５２は、専門家を識別する医師ＩＤに対応付けて、当該医師ＩＤが特定する医師のプロフィール等を記憶する。医師のプロフィールとしては、医師の氏名や勤務する診療科に加え、専門分野や信用度等の各種情報を採用することとしている。
　ここで、高度な専門性を有する医師のような専門家に対しては知識の乏しい専門家以外の者は適切な評価を行うことが困難なため、本実施形態では、専門家同士が評価をし合うことを許容している。この点、同じ専門家であっても、経験年数等により知識水準が異なることが予想される。そこで、本実施形態では、専門家毎に専門分野に対する信頼度を設定し、当該信頼度に基づいて評価対象者に対する順位付けを行うこととしている。詳細については後述するが、例えば、ある専門分野に対して信頼度の高い専門家が投稿した評価は、当該専門分野に対して信頼度の低い専門家が投稿した評価よりも信頼性が高い。そのため、信頼度の高い専門家が行った評価から算出されたスコアに対しては高い重み付けを行い、信頼度の低い専門家が行った評価から算出されたスコアに対しては低い重み付けを行う。
　なお、専門家に対する信頼度は、適宜設定することができ、例えば、過去に集計した順位に基づいて決定することとしてもよく、過去の評価の投稿数や投稿の質に基づいて決定することとしてもよい。即ち、順位が高い専門家ほど信頼度を高くしてもよく、また、投稿数が多い専門家ほど信頼度を高くしてもよく、また、投稿の質が高い専門家ほど信頼度を高くすることとしてもよい。そのため、専門家毎の信頼度は、適宜必要なタイミングで更新されることが好ましい。また、図７では、１人の専門家に対して信頼度を１つのみ設定することとしているが、専門分野毎に信頼度を設定することとしてもよい。 Next, the doctor information DB 52 will be described with reference to FIG. The doctor information DB 52 stores information on an expert (doctor) who uses a service provided by the expert evaluation device 1. That is, the doctor information DB 52 stores a doctor's profile or the like specified by the doctor ID in association with the doctor ID that identifies the expert. As the profile of the doctor, in addition to the name of the doctor and the department where he works, various information such as specialized fields and creditworthiness will be adopted.
Here, since it is difficult for an expert such as a doctor with a high level of expertise to perform an appropriate evaluation for those who are not knowledgeable, in this embodiment, the experts evaluate each other. It is allowed to meet each other. In this regard, even for the same expert, the level of knowledge is expected to vary depending on the years of experience. Therefore, in this embodiment, the reliability for the specialized field is set for each expert, and the evaluation target person is ranked based on the reliability. Although details will be described later, for example, an evaluation posted by an expert with high reliability in a certain specialized field is more reliable than an evaluation posted by an expert with low reliability in the specialized field. Therefore, a high weight is applied to a score calculated from an evaluation performed by an expert with high reliability, and a low weight is applied to a score calculated from an evaluation performed by an expert with low reliability.
In addition, the reliability with respect to the expert can be set as appropriate. For example, the reliability may be determined based on the rankings accumulated in the past, or may be determined based on the number of posts in the past evaluation or the quality of posts. Also good. In other words, the higher the ranking, the higher the level of reliability, the higher the number of posts, the higher the level of reliability, and the higher the quality of the posts, the higher the level of reliability. It is good as well. Therefore, it is preferable that the reliability for each expert is updated at a necessary timing as appropriate. In FIG. 7, only one reliability is set for one expert, but the reliability may be set for each specialized field.
　図３に戻り、ランキング情報生成手段４２は、評価情報ＤＢ５１に記憶された評価内容に基づいて評価対象者の専門分野におけるランキングを示すランキング情報を生成する。なお、詳細については後述するが、ランキング情報の生成は、評価内容に基づくスコアの算出、算出したスコアに対する重み付け、重み付け後のスコアに基づく順位付けにより行われる。そのため、ランキング情報生成手段４２は、評価情報抽出手段４２１と、相対評価抽出手段４２２と、スコア算出手段４２３と、重み付け決定手段４２４と、スコア集計手段４２５と、を含んで構成される。 3, the ranking information generation means 42 generates ranking information indicating the ranking of the evaluation subject in the specialized field based on the evaluation contents stored in the evaluation information DB 51. Although details will be described later, the ranking information is generated by calculating a score based on the evaluation contents, weighting the calculated score, and ranking based on the weighted score. Therefore, the ranking information generation unit 42 includes an evaluation information extraction unit 421, a relative evaluation extraction unit 422, a score calculation unit 423, a weight determination unit 424, and a score totaling unit 425.
　評価情報抽出手段４２１は、所定の期間毎に動作して評価情報ＤＢ５１から評価情報を抽出する。なお、評価情報ＤＢ５１から抽出する評価情報には、少なくとも評価対象者の医師ＩＤ、投稿者の医師ＩＤ、評価分野、及び評価内容を含むものとする。ここで、評価内容及び評価分野は、相対評価抽出手段４２２、スコア算出手段４２３に供給され、投稿された評価内容に基づくスコアの算出に用いられる。また、投稿者の医師ＩＤ及び評価分野は、重み付け決定手段４２４に供給され、算出したスコアに対する重み付けに用いられる。また、評価対象者の医師ＩＤ及び評価分野は、スコア集計手段４２５に供給され、専門分野毎のランキング情報の生成に用いられる。 Evaluation information extraction means 421 operates every predetermined period and extracts evaluation information from the evaluation information DB 51. The evaluation information extracted from the evaluation information DB 51 includes at least the doctor ID of the evaluation subject, the doctor ID of the poster, the evaluation field, and the evaluation content. Here, the evaluation content and the evaluation field are supplied to the relative evaluation extraction unit 422 and the score calculation unit 423, and are used to calculate a score based on the posted evaluation content. Further, the doctor ID of the poster and the evaluation field are supplied to the weighting determination unit 424 and used for weighting the calculated score. The evaluation subject's doctor ID and the evaluation field are supplied to the score counting means 425 and used to generate ranking information for each specialized field.
　相対評価抽出手段４２２は、評価情報抽出手段４２１から供給された評価内容から相対評価に用いるワード（以下、「相対評価用ワード」と呼ぶ）を抽出する。
　相対評価用ワードの抽出は、評価内容に含まれる所定の語句単位の判別を行い、判別した語句単位毎に、評価辞書ＤＢ５３を検索することで行う。具体的には、相対評価抽出手段４２２は、評価内容に対して形態素解析等の言語処理を行うことで、評価内容から好適な単位の単語を抽出する。そして、相対評価抽出手段４２２は、抽出した単語に基づいて評価辞書ＤＢ５３を検索することで、相対評価ワードを抽出する。なお、言語処理により抽出する単語の単位は、評価に好適な単位を適宜設定することができ、１つの形態素からなる単純語を単位としてもよく、また、複数の形態素からなる合成語を単位としてもよい。 The relative evaluation extraction unit 422 extracts words (hereinafter referred to as “relative evaluation words”) used for relative evaluation from the evaluation contents supplied from the evaluation information extraction unit 421.
The relative evaluation word is extracted by determining a predetermined phrase unit included in the evaluation contents and searching the evaluation dictionary DB 53 for each determined phrase unit. Specifically, the relative evaluation extraction unit 422 performs a word processing such as morphological analysis on the evaluation content, thereby extracting a suitable unit word from the evaluation content. Then, the relative evaluation extraction unit 422 extracts the relative evaluation word by searching the evaluation dictionary DB 53 based on the extracted word. The unit of words extracted by language processing can be set appropriately as a unit suitable for evaluation. A simple word composed of one morpheme may be used as a unit, and a synthetic word composed of a plurality of morphemes may be used as a unit. Also good.
　ここで、図９を参照して、評価辞書ＤＢ５３について説明する。評価辞書ＤＢ５３は、投稿者が投稿した評価内容に基づいて評価対象者を相対的に評価するための相対評価情報を記憶する。相対評価情報とは、相対評価用ワード毎に設定された各種情報をいい、本実施形態では、相対評価用ワードの種別を示すワード種別及び内容を用いる。
　なお、評価内容の中には、「優れている」「喜ばれている」等のようにスコア付けに適したワードもある一方で、「私より」「Ａ先生より」「この分野において」等のようにスコア付けには適さないがスコア付けの基準となるワードも含まれる。「ワード種別」とは、これらを区別する情報であり、本実施形態では、評価対象者のスコア付けを行う際に基準となる対象を示すワードを「基準点ワード」と呼び、当該基準に対する評価対象者のスコアの加算に用いるワードを「評価ワード」と呼ぶ。そして、「基準点ワード」に対してはスコア算出の基準点が内容として規定され、「評価ワード」に対してはワード毎のスコアが規定されている。図９に示すように、基準点としては、他の専門家のスコアや評価した専門分野における専門家のスコアの平均値等を採用することができる。
　なお、適切なスコア付けを可能にするため、評価辞書ＤＢ５３は、適宜必要なタイミングで更新されることが好ましい。 Here, the evaluation dictionary DB 53 will be described with reference to FIG. The evaluation dictionary DB 53 stores relative evaluation information for relatively evaluating the evaluation target person based on the evaluation contents posted by the poster. The relative evaluation information refers to various pieces of information set for each relative evaluation word. In the present embodiment, the word type and content indicating the type of the relative evaluation word are used.
In addition, while there are some words that are suitable for scoring, such as “Excellent” and “Delighted”, “From me”, “From teacher A”, “In this field”, etc. In this way, words that are not suitable for scoring but serve as criteria for scoring are also included. “Word type” is information for distinguishing these, and in the present embodiment, a word indicating a reference target when scoring an evaluation target person is referred to as a “reference point word” and is evaluated for the reference. A word used for adding the subject's score is called an “evaluation word”. The score calculation reference point is defined as the content for the “reference point word”, and the score for each word is defined for the “evaluation word”. As shown in FIG. 9, as a reference point, the score of another expert, the average value of the expert's score in the evaluated specialty field, etc. can be adopted.
In order to enable appropriate scoring, the evaluation dictionary DB 53 is preferably updated at a necessary timing as appropriate.
　図３に戻り、相対評価抽出手段４２２は、評価内容から抽出した単語をキーとして評価辞書ＤＢ５３を検索し、評価内容を構成する単語から相対評価用ワードを特定する。そして、相対評価抽出手段４２２は、特定した相対評価ワードを、当該相対評価ワードに設定された相対評価情報と共にスコア算出手段４２３に供給する。 3, the relative evaluation extraction unit 422 searches the evaluation dictionary DB 53 using the word extracted from the evaluation content as a key, and specifies the relative evaluation word from the words constituting the evaluation content. Then, the relative evaluation extraction unit 422 supplies the specified relative evaluation word to the score calculation unit 423 together with the relative evaluation information set in the relative evaluation word.
　スコア算出手段４２３は、相対評価情報に基づいて評価内容に対するスコアを算出して重み付け決定手段４２４に供給する。
　具体的には、スコア算出手段４２３は、ワード種別「基準点ワード」の相対評価ワードに基づいて基準となる基準スコアを取得し、当該基準スコアにワード種別「評価ワード」の相対評価ワードに規定されたスコアを加算することで、評価内容に対するスコアを算出する。
　例えば、評価情報ＤＢ５１に記憶された評価情報ＩＤ「０００１」の評価内容から、「私」「見習いたい」が相対評価用ワードとして抽出された場合、スコア算出手段４２３は、ワード種別「基準点ワード」の相対評価ワード「私」から、評価分野における投稿者のスコアを基準スコアとして取得する。その後、当該基準スコアに対してワード種別「評価ワード」の相対評価ワード「見習いたい」に対応するスコア「＋１０」を加算し、評価分野における評価対象者のスコアを算出する。 The score calculation unit 423 calculates a score for the evaluation content based on the relative evaluation information and supplies it to the weight determination unit 424.
Specifically, the score calculation unit 423 obtains a reference score serving as a reference based on the relative evaluation word of the word type “reference point word”, and is defined as the relative evaluation word of the word type “evaluation word”. The score for the evaluation content is calculated by adding the score obtained.
For example, when “I” or “I want to learn” is extracted as a relative evaluation word from the evaluation content of the evaluation information ID “0001” stored in the evaluation information DB 51, the score calculation means 423 uses the word type “reference point word”. From the relative evaluation word “I” of “”, the score of the poster in the evaluation field is acquired as a reference score. Thereafter, the score “+10” corresponding to the relative evaluation word “I want to apprentice” of the word type “evaluation word” is added to the reference score, and the score of the evaluation target person in the evaluation field is calculated.
　重み付け決定手段４２４は、評価情報抽出手段４２１から供給された投稿者の医師ＩＤ及び評価分野に基づいて医師情報ＤＢ５２から投稿者の信頼度を取得し、取得した信頼度に基づいて評価対象者のスコアを補正する。信頼度に基づく補正は、任意に行うことができ、例えば、算出したスコアに信頼度を乗算する。即ち、信頼度「０．９」の投稿者が投稿した評価内容のスコアが「３０」である場合には、重み付け決定手段４２４は「２７（＝３０×０．９）」とスコアを補正する。
　なお、信頼度に基づくスコアの補正は、スコア算出手段４２３によるスコア算出時の基準点に応じて異ならせることとしてもよい。一例として、投稿者のスコアを基準にスコア算出手段４２３がスコアを算出した場合には、投稿者に設定された信頼度に重み付けを行うこととしてもよい。即ち、基準点が投稿者のスコアである場合には当該投稿者に設定された信頼度「０．９」に対して重み付けし「１．０」とした上で、スコアを補正することとしてもよい。
　重み付け決定手段４２４は、信頼度に基づいてスコアを補正すると、補正したスコアを評価情報ＤＢ５１に記憶する。 The weighting determination unit 424 acquires the reliability of the poster from the doctor information DB 52 based on the doctor ID and the evaluation field of the poster supplied from the evaluation information extraction unit 421, and the evaluation subject's evaluation based on the acquired reliability. Correct the score. The correction based on the reliability can be arbitrarily performed. For example, the calculated score is multiplied by the reliability. That is, when the score of the evaluation content posted by the contributor having the reliability of “0.9” is “30”, the weight determination unit 424 corrects the score to “27 (= 30 × 0.9)”. .
Note that the correction of the score based on the reliability may be made different according to the reference point when the score calculation unit 423 calculates the score. As an example, when the score calculation unit 423 calculates the score based on the poster's score, the reliability set for the poster may be weighted. That is, when the reference point is the poster's score, the reliability is set to “1.0” by weighting the reliability “0.9” set for the poster, and the score may be corrected. Good.
If the weight determination means 424 correct | amends a score based on reliability, it will memorize | store the corrected score in evaluation information DB51.
　スコア集計手段４２５は、重み付け決定手段４２４が全ての評価内容に対してスコアを算出した後に動作し、評価情報ＤＢ５１に記憶されたスコアを評価対象者及び評価分野毎に集計して当該分野における専門家のランキング情報を生成する。 The score totaling unit 425 operates after the weighting determination unit 424 calculates scores for all evaluation contents, and totals the scores stored in the evaluation information DB 51 for each evaluation target person and each evaluation field, and specializes in that field. Generate house ranking information.
　図８に生成したランキング情報に基づいて上位一位の専門家（医師）が専門分野毎に示されている画面を示す。ランキング情報の表示領域Ｖ３には、夫々の専門分野の一位の専門家についての要約情報が表示される。 FIG. 8 shows a screen in which the top first expert (doctor) is shown for each specialized field based on the ranking information generated. In the ranking information display area V3, summary information about the first expert in each specialized field is displayed.
　次に、図１０及び図１１を参照して、以上説明した図３の機能的構成の専門家評価装置１が実行する専門家評価処理について説明する。 Next, the expert evaluation process executed by the expert evaluation apparatus 1 having the functional configuration shown in FIG. 3 described above will be described with reference to FIGS.
　初めに、評価情報受付手段４１は、投稿者端末２から評価の投稿を受け付ける（ステップＳ１）。例えば、評価情報受付手段４１は、評価対象者の医師ＩＤ、投稿者の医師ＩＤ、評価分野、及び評価内容を示すテキスト情報を少なくとも含む情報を受け付ける。続いて、評価情報受付手段４１は、ステップＳ１で受け付けた情報を評価情報ＤＢ５１に記憶する（ステップＳ２）。 First, the evaluation information receiving means 41 receives an evaluation post from the poster terminal 2 (step S1). For example, the evaluation information accepting unit 41 accepts information including at least text information indicating a doctor ID of a person to be evaluated, a doctor ID of a poster, an evaluation field, and evaluation contents. Subsequently, the evaluation information receiving means 41 stores the information received in step S1 in the evaluation information DB 51 (step S2).
　その後、ランキング情報生成手段４２は、所定の期間が経過したかを判定する。所定の期間は、評価情報の蓄積度合いや予め設定された時間等のように適宜設定することができる。所定の期間が経過した場合には、ステップＳ４に進み、所定の期間が経過していない場合には、ステップＳ１に戻り、評価の投稿を受け付ける。ステップＳ４では、ランキング情報生成手段４２は、後述するランキング情報生成処理を実行し、専門家評価処理を終了する。 Thereafter, the ranking information generating means 42 determines whether a predetermined period has elapsed. The predetermined period can be set as appropriate, such as the degree of accumulation of evaluation information or a preset time. If the predetermined period has elapsed, the process proceeds to step S4. If the predetermined period has not elapsed, the process returns to step S1 to accept an evaluation posting. In step S4, the ranking information generation unit 42 executes ranking information generation processing described later, and ends the expert evaluation processing.
　次に、図１１を参照して、ランキング情報生成処理について具体的に説明する。
　初めに、評価情報抽出手段４２１は、評価情報ＤＢ５１から評価情報を抽出する（ステップＳ４１）。続いて、相対評価抽出手段４２２は、抽出した評価情報の評価内容を解析し、相対評価用ワードを抽出する。また、相対評価抽出手段４２２は、抽出した相対評価ワードに対応する相対評価情報を抽出する（ステップＳ４２）。 Next, the ranking information generation process will be specifically described with reference to FIG.
First, the evaluation information extraction unit 421 extracts evaluation information from the evaluation information DB 51 (step S41). Subsequently, the relative evaluation extraction unit 422 analyzes the evaluation contents of the extracted evaluation information and extracts a relative evaluation word. Further, the relative evaluation extraction unit 422 extracts relative evaluation information corresponding to the extracted relative evaluation word (step S42).
　続いて、スコア算出手段４２３は、抽出した相対評価情報に基づいて評価情報のスコアを算出する（ステップＳ４３）。即ち、スコア算出手段４２３は、ワード種別「基準点ワード」の相対評価ワードに基づいて基準スコアを取得し、当該基準スコアに対してワード種別「評価ワード」の相対評価ワードに設定されたスコアを加算する。 Subsequently, the score calculation unit 423 calculates the score of the evaluation information based on the extracted relative evaluation information (step S43). That is, the score calculation unit 423 acquires a reference score based on the relative evaluation word of the word type “reference point word”, and uses the score set in the relative evaluation word of the word type “evaluation word” with respect to the reference score. to add.
　次に、重み付け決定手段４２４は、投稿者の信頼度を医師情報ＤＢ５２から抽出して、当該信用度に基づいてステップ４３で算出したスコアを補正する（ステップＳ４４）。その後、重み付け決定手段４２４は、補正したスコアを評価情報ＤＢ５１に記憶し（ステップＳ４５）、全ての評価内容に対するスコア付けが終了したか否かを判定する（ステップＳ４６）。このとき、スコア付けが終了している場合には、ステップＳ４７に進み、未だ終了していない場合には、ステップＳ４１に戻り、終了していない評価内容に対するスコア付けを実行する。 Next, the weight determination means 424 extracts the poster's reliability from the doctor information DB 52 and corrects the score calculated in step 43 based on the reliability (step S44). Thereafter, the weighting determination unit 424 stores the corrected score in the evaluation information DB 51 (step S45), and determines whether scoring for all evaluation contents has been completed (step S46). At this time, if scoring has been completed, the process proceeds to step S47, and if not yet completed, the process returns to step S41, and scoring is performed on evaluation contents that have not been completed.
　ステップＳ４７では、スコア集計手段４２５は、評価情報ＤＢ５１に記憶されたスコアを評価対象者及び評価分野毎に集計し、集計したスコアに基づいて当該分野における専門家のランキング情報を生成する。これにより、ランキング情報生成処理は終了する。 In step S47, the score totaling unit 425 totals the scores stored in the evaluation information DB 51 for each evaluation target person and each evaluation field, and generates ranking information of experts in the field based on the total score. Thereby, the ranking information generation process ends.
　以上のように、本実施形態では、投稿者が投稿した評価から専門家のランキング情報を生成する。このとき、専門家評価装置１は、評価辞書ＤＢ５３に記憶された相対評価ワードに基づいて相対的に専門家のスコア付けを行う。即ち、投稿者が投稿した評価の内容に応じて基準点を異ならせてスコア付けを行う。その結果、スコア付けに適したワード種別「評価ワード」が同一であったとしても、基準点が異なる場合には異なるスコアが算出されることになる。例えば、専門家Ａが行った「私より優れている」「この分野において優れている」「Ｂ先生より優れている」といった評価では、評価ワードは全て「優れている」で同じであるが、基準点が夫々異なるため、異なるスコアを算出することができる。
　これにより、投稿者が行った自由な投稿に対して適切なスコア付けを行うことができ、信憑性の高い専門家のランキング情報を生成することができる。 As described above, in this embodiment, expert ranking information is generated from the evaluation posted by the poster. At this time, the expert evaluation apparatus 1 scores experts relatively based on the relative evaluation words stored in the evaluation dictionary DB 53. That is, scoring is performed with different reference points according to the content of the evaluation posted by the poster. As a result, even if the word types “evaluation words” suitable for scoring are the same, different scores are calculated when the reference points are different. For example, in the evaluations performed by expert A such as “Better than me”, “Excellent in this field”, “Better than teacher B”, the evaluation words are all “Excellent” and the same, Since the reference points are different, different scores can be calculated.
Accordingly, it is possible to appropriately score a free post made by the poster, and it is possible to generate ranking information of experts with high credibility.
　なお、本発明は、上述の実施形態に限定されるものではなく、本発明の目的を達成できる範囲での変形、改良等は本発明に含まれるものである。 It should be noted that the present invention is not limited to the above-described embodiment, and modifications, improvements, and the like within the scope that can achieve the object of the present invention are included in the present invention.
　上述の実施形態では、本発明が適用される専門家評価装置１は、対象となる専門家を医師として説明したが、特にこれに限られず、専門家を対象とした評価情報を管理するものであれば足り、専門家は、例えば、医師のほかにも、薬剤師、看護師、弁護士、弁理士、司法書士、不動産鑑定士、公認会計士、税理士等の国家資格を有する専門職の他、コンサルタント、システムエンジニア等のように資格を必要としない専門職等各種各様のものを採用することができる。 In the above-described embodiment, the expert evaluation apparatus 1 to which the present invention is applied has been described with the subject expert as a doctor. If necessary, specialists, for example, in addition to doctors, pharmacists, nurses, lawyers, patent attorneys, judicial scriveners, real estate appraisers, certified public accountants, tax accountants, professionals with national qualifications, consultants, Various types of professionals that do not require qualifications such as system engineers can be employed.
　上述した一連の処理は、ハードウェアにより実行させることもできるし、ソフトウェアにより実行させることもできる。
　換言すると、図３の機能的構成は例示に過ぎず、特に限定されない。即ち、上述した一連の処理を全体として実行できる機能が専門家評価装置１に備えられていれば足り、この機能を実現するためにどのような機能ブロックを用いるのかは特に図３の例に限定されない。
　また、１つの機能ブロックは、ハードウェア単体で構成してもよいし、ソフトウェア単体で構成してもよいし、それらの組み合わせで構成してもよい。 The series of processes described above can be executed by hardware or can be executed by software.
In other words, the functional configuration of FIG. 3 is merely an example, and is not particularly limited. That is, it is sufficient that the expert evaluation apparatus 1 has a function capable of executing the above-described series of processes as a whole, and what functional blocks are used to realize this function is limited to the example of FIG. Not.
In addition, one functional block may be configured by hardware alone, software alone, or a combination thereof.
　一連の処理をソフトウェアにより実行させる場合には、そのソフトウェアを構成するプログラムが、コンピュータ等にネットワークや記録媒体からインストールされる。
　コンピュータは、専用のハードウェアに組み込まれているコンピュータであってもよい。また、コンピュータは、各種のプログラムをインストールすることで、各種の機能を実行することが可能なコンピュータ、例えば汎用のパーソナルコンピュータであってもよい。 When a series of processing is executed by software, a program constituting the software is installed on a computer or the like from a network or a recording medium.
The computer may be a computer incorporated in dedicated hardware. Further, the computer may be a computer capable of executing various functions by installing various programs, for example, a general-purpose personal computer.
　このようなプログラムを含む記録媒体は、管理者にプログラムを提供するために装置本体とは別に配布される図２のメディア入出力部１７により構成されるだけでなく、装置本体に予め組み込まれた状態でユーザに提供される記録媒体等で構成される。メディア入出力部１７に提供されるメディアは、例えば、磁気ディスク（フロッピディスクを含む）、光ディスク、又は光磁気ディスク等により構成される。光ディスクは、例えば、ＣＤ－ＲＯＭ（Ｃｏｍｐａｃｔ　Ｄｉｓｋ－Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ），ＤＶＤ（Ｄｉｇｉｔａｌ　Ｖｅｒｓａｔｉｌｅ　Ｄｉｓｋ）等により構成される。光磁気ディスクは、ＭＤ（Ｍｉｎｉ－Ｄｉｓｋ）等により構成される。また、装置本体に予め組み込まれた状態でユーザに提供される記録媒体は、例えば、図２の記憶部１２に含まれるハードディスク等で構成される。 The recording medium including such a program is not only configured by the media input / output unit 17 of FIG. 2 distributed separately from the apparatus main body in order to provide the program to the administrator, but is also preinstalled in the apparatus main body. It is composed of a recording medium provided to the user in a state. The media provided to the media input / output unit 17 is composed of, for example, a magnetic disk (including a floppy disk), an optical disk, a magneto-optical disk, or the like. The optical disk is composed of, for example, a CD-ROM (Compact Disk-Read Only Memory), a DVD (Digital Versatile Disk), or the like. The magneto-optical disk is constituted by an MD (Mini-Disk) or the like. In addition, the recording medium provided to the user in a state of being preliminarily incorporated in the apparatus main body includes, for example, a hard disk included in the storage unit 12 in FIG.
　なお、本明細書において、記録媒体に記録されるプログラムを記述するステップは、その順序に沿って時系列的に行われる処理はもちろん、必ずしも時系列的に処理されなくとも、並列的或いは個別に実行される処理をも含むものである。
　また、本明細書において、システムの用語は、複数の装置や複数の手段等より構成される全体的な装置を意味するものとする。 In the present specification, the step of describing the program recorded on the recording medium is not limited to the processing performed in time series along the order, but is not necessarily performed in time series, either in parallel or individually. The process to be executed is also included.
Further, in the present specification, the term “system” means an overall apparatus configured by a plurality of devices, a plurality of means, and the like.
　以上、本発明のいくつかの実施形態について説明したが、これらの実施形態は、例示に過ぎず、本発明の技術的範囲を限定するものではない。本発明はその他のさまざまな実施形態を取ることが可能であり、さらに、本発明の要旨を逸脱しない範囲で、省略や置換等種々の変更を行うことができる。これら実施形態やその変形は、本明細書等に記載された発明の範囲や要旨に含まれると共に、特許請求の範囲に記載された発明とその均等の範囲に含まれる。 As mentioned above, although several embodiment of this invention was described, these embodiment is only an illustration and does not limit the technical scope of this invention. The present invention can take various other embodiments, and various modifications such as omission and replacement can be made without departing from the gist of the present invention. These embodiments and modifications thereof are included in the scope and gist of the invention described in this specification and the like, and are included in the invention described in the claims and the equivalents thereof.
　１・・・専門家評価装置，２・・・投稿者端末，３・・・閲覧者端末，４・・・管理者端末，５・・・通信ネットワーク，６・・・専門家評価システム，１１・・・制御部，１２・・・記憶部，１３・・・入力部，１４・・・出力部，１５・・・通信部，１６・・・バス，４１・・・評価情報受付手段，４２・・・ランキング情報生成手段，５１・・・評価情報ＤＢ，５２・・・医師情報ＤＢ，５３・・・評価辞書ＤＢ，４２１・・・評価情報抽出手段，４２２・・・相対評価抽出手段，４２３・・・スコア算出手段，４２４・・・重み付け決定手段，４２５・・・スコア集計手段 DESCRIPTION OF SYMBOLS 1 ... Expert evaluation apparatus, 2 ... Contributor terminal, 3 ... Reader terminal, 4 ... Manager terminal, 5 ... Communication network, 6 ... Expert evaluation system, 11 ... Control part, 12 ... Storage part, 13 ... Input part, 14 ... Output part, 15 ... Communication part, 16 ... Bus, 41 ... Evaluation information receiving means, 42 ... Ranking information generation means, 51 ... Evaluation information DB, 52 ... Doctor information DB, 53 ... Evaluation dictionary DB, 421 ... Evaluation information extraction means, 422 ... Relative evaluation extraction means, 423... Score calculating means, 424... Weighting determining means, 425.

Claims (4)

  1. 　所定の専門分野における専門家のコミュニティにおいて専門家相互の評価情報の投稿を受け付けて管理する専門家評価装置であって、
    　投稿者の端末から、専門家について、評価の対象となる専門分野を示す評価分野情報と、当該専門家以外の他の専門家との相対評価情報と、を含む評価情報の投稿を受け付けたことに応じて、当該評価情報を記憶する評価情報記憶手段と、
    　前記記憶した評価情報の投稿に含まれる評価分野情報と相対評価情報とを読み出して、前記評価分野情報が示す専門分野毎に、前記相対評価情報に基づいて専門家のランキング情報を生成するランキング情報生成手段と、
    　を備える専門家評価装置。     An expert evaluation device that accepts and manages posting of evaluation information between experts in a community of experts in a predetermined specialized field,
    Acceptance of submission of evaluation information, including evaluation field information indicating the field of expertise to be evaluated, and relative evaluation information with other experts other than the expert, from the terminal of the poster According to the evaluation information storage means for storing the evaluation information,
    Ranking information that reads the evaluation field information and relative evaluation information included in the posted evaluation information and generates expert ranking information based on the relative evaluation information for each specialized field indicated by the evaluation field information Generating means;
    An expert evaluation device comprising:
  2. 　前記ランキング情報生成手段は、前記評価情報が含む評価分野情報における投稿者のランキングが高いほど、前記相対評価情報により大きな重みを付けて前記ランキング情報を生成する、
    　請求項１に記載の専門家評価装置。     The ranking information generation means generates the ranking information with higher weight on the relative evaluation information as the ranking of the poster in the evaluation field information included in the evaluation information is higher.
    The expert evaluation apparatus according to claim 1.
  3. 　前記評価情報記憶手段は、前記相対評価情報をテキストとして受け付けて記憶し、
    　前記ランキング情報生成手段は、前記相対評価情報のテキストを読み出して、文字列解析によりランキングの対象となる複数の専門家と当該複数の専門家の相対的な評価とを判定して前記ランキング情報を生成する、
    　請求項１又は２に記載の専門家評価装置。     The evaluation information storage means receives and stores the relative evaluation information as text;
    The ranking information generation means reads the text of the relative evaluation information, determines a plurality of experts to be ranked by character string analysis and a relative evaluation of the plurality of experts, and determines the ranking information. Generate,
    The expert evaluation apparatus according to claim 1 or 2.
  4. 　前記ランキング情報生成手段は、相対評価情報が、当該相対評価情報を含む評価情報を投稿した投稿者を、評価の対象とする専門家の一として含む場合に、当該相対評価情報により大きな重みを付けて前記ランキング情報を生成する、請求項１乃至３のいずれかに記載の専門家評価装置。 The ranking information generating means assigns a greater weight to the relative evaluation information when the relative evaluation information includes a poster who has posted the evaluation information including the relative evaluation information as one of the experts to be evaluated. The expert evaluation device according to claim 1, wherein the ranking information is generated.
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