Papers accepted at SKG4J, a workshop associated with CIKM 2020

By | 2020-09-24

We have three papers accepted at 1st Workshop Semantic and Knowledge Graph Advances for Journalism, a workshop associated with CIKM 2020.

Challenges and Opportunities for Journalistic Knowledge Platforms, by Marc and Andreas: “Journalism is under pressure from loss of advertisement and revenues, while experiencing an increase in digital consumptionand user demands for quality journalism and trusted sources. Journalistic Knowledge Platforms (JKPs) are an emerginggeneration of platforms which combine state-of-the-art artificial intelligence (AI) techniques such as knowledge graphs,linked open data (LOD), and natural-language processing (NLP) for transforming newsrooms and leveraging informationtechnologies to increase the quality and lower the cost of news production. In order to drive research and design better JKPsthat allow journalists to get most benefits out of them, we need to understand what challenges and opportunities JKPs arefacing. This paper presents an overview of the main challenges and opportunities involved in JKPs which have been manuallyextracted from literature with the support of natural language processing and understanding techniques. These challengesand opportunities are organised in: stakeholders, information, functionalities, components, techniques and other aspects.”

Lifting News into a Journalistic Knowledge Platform, by Tareq and Marc: “A massive amount of news is being shared online by individuals and news agencies, making it difficult to take advantage ofthese news and analyse them in traditional ways. In view of this, there is an urgent need to use recent technologies to analyseall news relevant information that is being shared in natural language and convert it into forms that can be more easily andprecisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural LanguageProcessing (NLP) offers the possibility for mining and lifting natural language texts to knowledge graphs allowing to exploitits semantic capabilities, facilitating new possibilities for news analysis and understanding.  However, the current availabletechniques are still away from perfect. Many approaches and frameworks have been proposed to track and analyse news inthe last few years. The shortcomings of those systems are that they are static and not updateable, are not designed for large-scale data volumes, did not support real-time processing, dealt with limited data resources, used traditional lifting pipelinesand supported limited tasks, or have neglected the use of knowledge graphs to represent news into a computer-processableform.  Therefore, there is a need to better support lifting natural language into a KG. With the continuous development ofNLP techniques, the design of new dynamic NLP lifters that can cope with all the previous shortcomings is required.  Thispaper introduces a general NLP lifting architecture for automatically lifting and processing news reports in real-time basedon the recent development of the NLP methods.”

Data Privacy in Journalistic Knowledge Platforms, by Marc, Tareq and Andreas: “Journalistic knowledge platforms (JKPs) leverage data from the news, social media and other sources.  They collect largeamounts of data and attempt to extract potentially news-relevant information for news production.  At the same time, byharvesting and recombining big data, they can challenge data privacy ethically and legally.  Knowledge graphs offer newpossibilities for representing information in JKPs, but their power also amplifies long-standing privacy concerns. This paperstudies the implications of data privacy policies for JKPs. To do so, we have reviewed the GDPR and identified different areaswhere it potentially conflicts with JKPs.”

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