Lifting news into a journalistic knowledge platform

Abstract

A massive amount of news is being shared online by individuals and news agencies, making it difficult to take advantage of these news and analyse them in traditional ways. In view of this, there is an urgent need to use recent technologies to analyse all news relevant information that is being shared in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language Processing (NLP) offers the possibility for mining and lifting natural language texts to knowledge graphs allowing to exploit its semantic capabilities, facilitating new possibilities for news analysis and understanding. However, the current available techniques are still away from perfect. Many approaches and frameworks have been proposed to track and analyse news in the last few years. The shortcomings of those systems are that they are static and not updateable, are not designed for largescale data volumes, did not support real-time processing, dealt with limited data resources, used traditional lifting pipelines and supported limited tasks, or have neglected the use of knowledge graphs to represent news into a computer-processable form. Therefore, there is a need to better support lifting natural language into a KG. With the continuous development of NLP techniques, the design of new dynamic NLP lifters that can cope with all the previous shortcomings is required. This paper introduces a general NLP lifting architecture for automatically lifting and processing news reports in real-time based on the recent development of the NLP methods.