论文标题

Kosmos:面向知识图的社交媒体和主流媒体概述系统

KOSMOS: Knowledge-graph Oriented Social media and Mainstream media Overview System

论文作者

Yang, Chua Hao, Jie, Yong Shan, Chin, Boon Kok, Chin, Lander, Ng, Lynnette Hui Xian

论文摘要

我们介绍了Kosmos,这是一种知识检索系统,基于社交媒体和主流媒体文档的构建知识图。系统首先通过聚类在每个时间范围内从文档中标识关键事件,提取文档以表示每个群集,然后用5W1H(谁,什么,何时,何时,何时,何时,为什么,为什么,如何,如何)描述文档。与代表文档的关系三胞胎和实体歧义歧义,以事件为中心的知识图可以增强。此知识检索由Web接口支持,该Web界面呈现了基于用户查询的相关节点和相关文章的图形可视化。该界面通过Kosmos信息提取管道有助于理解主流和社交媒体新闻报道的事件之间的关系,这对于了解媒体倾向和公众舆论很有价值。最后,我们探讨了从文件中提取事件和关系的用例,以了解媒体和社区对2020年Covid19大流行的看法。

We introduce KOSMOS, a knowledge retrieval system based on the constructed knowledge graph of social media and mainstream media documents. The system first identifies key events from the documents at each time frame through clustering, extracting a document to represent each cluster, then describing the document in terms of 5W1H (Who, What, When, Where, Why, How). The event centric knowledge graph is enhanced by relation triplets and entity disambiguation from the representative document. This knowledge retrieval is supported by a web interface that presents a graph visualisation of related nodes and relevant articles based on a user query. The interface facilitates understanding relationships between events reported in mainstream and social media journalism through the KOSMOS information extraction pipeline, which is valuable to understand media slant and public opinions. Finally, we explore a use case in extracting events and relations from documents to understand the media and community's view to the 2020 COVID19 pandemic.

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