论文标题
用于捕获和分析知识管理系统的非结构化和半结构数据的框架
A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System
论文作者
论文摘要
主流知识管理研究人员通常同意，从非结构化数据中提取的知识和半结构化数据已成为组织战略决策的重要性。在这项研究中，我们开发了一个框架，该框架使用机器学习技术捕获和分析非结构化数据，并将从数据获得的知识和洞察力整合到传统知识管理系统中。与文献中发表的大多数框架不同，该框架着重于特定类型的非结构化数据，我们的框架涉及各种非结构化数据，从社交网站，在线论坛，讨论板，评论，评论到音频数据，图像数据和视频数据等非结构数据。我们重点介绍了这些数据的一些预处理和处理技术，还突出了一些标准输出。我们通过使用Python和Beautiful Soup开发文本数据应用程序编程界面（API）来评估框架，并对学生审查通过API收集的数据进行情感分析。
Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge management systems. Unlike most frameworks published in the literature that focuses on a specific type of unstructured data, our frameworks cut across the varieties of unstructured data ranging from textual data from social network sites, online forums, discussion boards, reviews to audio data, image data and video data. We highlight some pre-processing and processing techniques for these data and also highlight some standard output. We evaluate the framework by developing a textual data application programming interface (API) using python and beautiful soup and we perform sentiment analysis on the students review data collected through the API.
