论文标题
社交网络提取无监督
Social Network Extraction Unsupervised
论文作者
论文摘要
在信息技术时代，两个发展中的方面是数据科学和人工智能。就科学数据而言，任务之一是从具有大数据本质的信息来源中提取社交网络。同时，就人工智能而言，矛盾方法的存在对知识有影响。本文将无监督的方法描述为从信息来源提取社交网络的方法流。作为开始概念，有多种可能的方法和策略。每种方法都有其优势，但通常，它有助于彼此的整合，即简化，丰富和强调结果。
In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.
