This Application claims priority of Taiwan Patent Application No. 099102048 filed on Jan. 26, 2010, the entirety of which is incorporated by reference herein.
1. Technical Field
The application relates to analysis and identification methods and systems, and more particularly, to methods and systems for recommending an expert list by collaborative intelligence.
2. Related Art
Recently, most methods for recommending an expert list are based on the number of times the writing content of an expert appears in an inquired proposal. However, normally, the expert list contains experts in only one specific domain. Accordingly, most research reports and documents of different domains are not identified and included in the analysis.
Current expert list methods suffer from the following problems. First, for multiple domains, the number of times the writing content of an expert appears in an inquired proposal is normally larger than that of a specific domain; thus, keywords calculated by current expert list methods do not meet requirements for multiple domain application. Second, because advanced classification of expert publications are not performed by current expert list methods, much time is required when reviewing and comparing information which may not be relevant. Therefore, efficiency for system performance is poor.
Furthermore, different domains use different technical expressions, such as the word “calculating machine” and the word “computer”; which are actually equivalent meanings. Therefore, it is very difficult to manually or automatically construct and maintain a semantic network between different domains.
Therefore, in order to solve the above-mentioned problem, the invention provides expert list recommendation methods and systems which may cover different domains, have high comparing efficiency, and effectively maintain a semantic network.
One aspect of the invention is to provide an expert list recommendation system, comprising: a domain modeler for establishing an expert knowledge database according to a plurality of expert publications in different domains, receiving an inquired proposal, determining the academic field of the inquired proposal according to keywords of the inquired proposal and keyword sets of the expert publications in different domains stored in the expert knowledge database, and outputting a first domain expert list corresponding to the inquired proposal, wherein the first domain expert list comprises a first group of expert publications and a first group of expert names; and an expertise matcher for receiving the first domain expert list, comparing semantic relatedness between keywords of the inquired proposal and keywords of the first group of the expert publications corresponding to the first domain expert list to output a first expert list to a display device.
Another aspect of the invention is to provide an expert list recommendation method, comprising: providing a plurality of expert publications in a different domains; establishing an expert knowledge database according to keywords of the expert publications in different domains by a domain modeler, receiving an inquired proposal; determining the academic field of the inquired proposal according to keywords of the inquired proposal and keyword sets of the keywords of the expert publications in different domains stored in the expert knowledge database; outputting a first domain expert list corresponding to the inquired proposal, wherein the first domain expert list comprises a first group of expert publications and a first group of expert names; receiving the first domain expert list; comparing semantic relatedness between keywords of the inquired proposal and keywords of the first group of the expert publications corresponding to the first domain expert list to generate a first expert list; and outputting the first expert list to a display device.
Another aspect of the invention is to provide an expert list recommendation method, comprising: providing a plurality of online communities, wherein subject matters of the online communities are related to different domains; establishing a semantic network according to phraseology keywords and technical expressions used and communicated by social network users of online communities; storing the semantic network in an expert knowledge database; receiving an inquired proposal; outputting an expert name list and expert publications of the academic field related to the inquired proposal according to keywords of the proposed title of the inquired proposal by checking the expert knowledge database; comparing semantic relatedness between the inquired proposal and the expert name list and the expert publications of the academic field related to the inquired proposal to generate an expert list; and displaying the expert list in a display device.
The advantage and spirit of the invention may be better understood by the following recitations and the appended drawings.
The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein:
The following description may be a contemplated mode of carrying out the invention. This description is made for the purpose of illustrating the general principles of the invention and should not be taken in a limiting sense. The scope of the invention is best determined by reference to the appended claims.
The domain modeler 101 receives a plurality of expert publications in different domains, and establishes the expert knowledge database 102 according to keywords of the tiles in the expert publications in different domains by collecting wikipedia page titles (WPTs) corresponding keywords of the tiles in the expert publications in different domains by a wikipedia website 3. Therefore, the expert knowledge database 102 stores the keyword sets of the expert publications in a different domain and wikipedia page titles corresponding to the keyword sets of the expert publications in different domains.
The domain modeler 101 receives an inquired proposal SP, when the domain modeler 101 has already estimated the expert knowledge database 102. The domain modeler 101 then collects wikipedia page titles corresponding to the keywords of the titles of the inquired proposal SP according to the keywords of the titles of the inquired proposal SP by checking the wikipedia website 3.
The domain modeler 101 determines whether the inquired proposal belongs to an academic field according to wikipedia page titles corresponding to the inquired proposal SP and wikipedia page titles corresponding to the keyword sets of the expert publications in different domains stored in the expert knowledge database. A first domain expert list DPL corresponding to the determined academic field which the inquired proposal SP belongs to is output to the expertise matcher 103 by the domain modeler 101, wherein the first domain expert list DPL comprises a first group of expert publications and a first group of expert names.
The expertise matcher 103 in the expert list recommendation system 10 receives the first domain expert list DPL. The semantic relatedness between wikipedia page titles of the inquired proposal SP and wikipedia page titles of keywords of the first group of the expert publications corresponding to the first domain expert list DPL is compared to the wikipedia website 3 to seek relatedness and depth between the inquired proposal SP and the first domain expert list DPL.
The expertise matcher 103 generates a semantic net distance table SND according to the relatedness and the depth thereof. The depth represents classification category levels from a starting catalog of a wikipedia page (root directory) and the relatedness represents is a distance between the wikipedia page titles corresponding to the inquired proposal SP and the wikipedia page titles corresponding to first domain expert list DPL under the catalog structure of the wikipedia page in the wikipedia website 3, and also represents the quantified distribution between the wikipedia page titles corresponding to the inquired proposal SP and the wikipedia page titles corresponding to first domain expert list DPL under the catalog structure of the wikipedia page.
For example, the depth (levels) of Cathay General hospital is seven under the catalog structure of the wikipedia page (root directory, nature, life, health, hospital, Taiwan hospital, Cathay General hospital, the total amount of levels is seven). For another example, as shown in the
The expertise matcher 103 generates a first expert list FPL according to the semantic net distance table SND and outputs the first expert list FPL to the ranking device 104 and displays the first expert list FPL in the display device 105.
The ranking device 104 in the expert list recommendation system 10 estimates a ranking score table AS corresponding to the first expert list FPL according to the first expert list FPL and an academic authority score table PSS and outputs a second expert list SPL to the display device 105 according to the ranking score table AS.
Therefore, the expert list related to and closed to the inquired proposal SP is obtained by the expert list recommendation system 10. The problems associated with multi-word meanings or multi-word uses may be avoided by comparing wikipedia page titles of keywords in the exemplary embodiment of the invention. For example, the expert list related to and closed to the proposal is obtained by the expert list recommendation system 10 to seek oral examination members according to the expert list and more easily seek highly related experts in the same domain to be oral examination members.
The correlated relatedness calculator 1032 quantifies the semantic net distance table SND, and outputs the related scores between the inquired proposal SP and the first domain expert list DPL (which are more similar, when the related scores are larger), wherein the related scores are defined as the first group expertise relatedness score table. The correlated relatedness calculator 1032 then generates the first expert list FPL according to the related scores such that the first expert list FPL comprises the first group expertise relatedness score table.
The score calculator 1042 weights the first group expertise relatedness score table of the first expert list FPL and the academic scores of the experts related to the inquired proposal to calculate the ranking score table AS corresponding to the first expert list FPL, and arranges expert names in order according to scores of the ranking score table AS. Several expert names and corresponding expert publications with higher scores on the ranking score table AS are chosen to generate the second expert list SPL, and the second expert list SPL is displayed in the display device 105.
The expert list recommendation method comprises: outputting a first domain expert list DPL corresponding to the academic field which the inquired proposal SP belongs to (step S44), wherein the first domain expert list comprises the first group of expert publications and the first group of expert names: in step S45, receiving the first domain expert list DPL by the expertise matcher 103: in step S46, comparing semantic relatedness between the wikipedia page titles corresponding to keywords of the inquired proposal and the wikipedia page titles corresponding to keywords of the first group of the expert publications in the first domain expert list DPL to generate a first expert list FPL and display the first expert list FPL in display device 105; in step S47, estimating a ranking score table corresponding to the first expert list FPL according to the first expert list FPL and an academic authority score table by the ranking device 104; in step S48, generating a second expert list SPL according to the ranking score table and outputting the second expert list SPL to the display device 105, to complete the process.
The domain construction and expertise comparison using the wikipedia website is not limiting, and can be replaced by a semantic network according to phraseology keywords and technical expressions used and communicated by social network users of online communities.
After processing, the comparison range may be reduced to avoid unnecessary keyword comparisons with unrelated publications. Therefore, the invention can increase efficiency.
With the example and explanations above, the features and spirit of the invention are hopefully well described. Those skilled in the art will readily observe that numerous modifications and alterations of the embodiments may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.
Number | Date | Country | Kind |
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099102048 | Jan 2010 | TW | national |