Subjects: Library Science,Information Science >> Information Science submitted time 2017-10-11 Cooperative journals: 《数据分析与知识发现》
Abstract: [Objective] To extract talents’ knowledge structure automatically. [Methods] We built an online knowledge structure extraction system based on Web information retrieval, webpage analysis, word segmentation and semantic Web technologies. [Results] We examined the usability of the new system. For course recognition, the overall precision rate was more than 95%. For semi-structured files, the recall rate was above 95%. For some non-structured files, the reacall rate was below 90%. [Limitations] The recall rate of course recognition was restricted by the content of the dictionary. [Conclusions] The proposed method meets the requirements of constructing talents’ knowledge structure and is a useful tool for related research.
Subjects: Library Science,Information Science >> Information Science submitted time 2017-10-11 Cooperative journals: 《数据分析与知识发现》
Abstract: [Objective] This study aims to build a knowledge requirement model for online outsourcing tasks. [Context]The proposed model could help us find proper personnel for each task. [Methods] We first designed an expert system framework and built a descriptive model for each task. And then, we analyzed the tasks based on inference rules and text analysis technology, with the purpose of quantifying the knowledge requirement for each task. [Results] The proposed framework successfully established the knowledge requirement model. [Conclusions] The new model laid foundation for the task-talent matching system of online outsourcing services.