| Summary: | 碩士 === 南台科技大學 === 資訊管理系 === 102 === As the time of big data come, variety and volume data produce every day. When people are looking for some information, they spend much time and efforts to make sure the results are correct and meet the needs. This is lack of efficiency and might lose the accuracy when they can’t find right information. For example, when the users are searching for what kind of journal they want to submit, they always spend much time to get the related information. Therefore, how to find the information the user need correctly and accurately among those big amount of data is what we want to discuss.
The Journal Submission Recommend System is built specially for different requirements. It combines with sematic web technic and the mechanism which records the users’ habits to support different identities. Also, users for journal submission can get the right information efficiently and quickly when searching by this system.
This system pre-processes the Journal data, and analyzes past retrieval record and the keywords in the system to find the journal information what the users want to submit and relationships between journals, and then match the data with query keyword again. This system functions by cutting sentence into words, building keywords of the journal ontology, putting the result into the historic database, and finally provides the recommended journal information that users can submit papers successfully.
The users can input keywords and retrieval information in this system; it will recommend match journal list according to different identity and keywords after the semantic analysis. Besides, it includes the mechanism which records the users’ habits to obtain operating record from working process before. It can enhance the retrieval quality of the system by using those historical data.
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