Data Privacy in Journalistic Knowledge Platforms

Journalistic knowledge platforms (JKPs) leverage data from the news, social media and other sources. They collect large amounts of data and attempt to extract potentially news-relevant information for news production. At the same time, by harvesting and recombining big data, they can challenge data privacy ethically and legally. Knowledge graphs offer new possibilities for representing information in JKPs, but their power also amplifies long-standing privacy concerns. This paper studies the implications of data privacy policies for JKPs. To do so, we have reviewed the GDPR and identified different areas where it potentially conflicts with JKPs.publishedVersio

Named Entity Extraction for Knowledge Graphs: A Literature Overview

An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.publishedVersio

Semantic Knowledge Graphs for the News: A Review

ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio

SKG4J 2020: 1st International Workshop on Semantic and Knowledge Graph Advances for Journalism

SKG4J targeted contributions at the interface between Artificial Intelligence, Data Management and its implications for journalistic practice. The first version of the workshop accepted three submissions with topics emphasising the complementary requirements for delivering realistic journalistic knowledge extraction/management platforms

Lifting news into a journalistic knowledge platform

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

Data Privacy in Journalistic Knowledge Platforms

Journalistic knowledge platforms (JKPs) leverage data from the news, social media and other sources. They collect large amounts of data and attempt to extract potentially news-relevant information for news production. At the same time, by harvesting and recombining big data, they can challenge data privacy ethically and legally. Knowledge graphs offer new possibilities for representing information in JKPs, but their power also amplifies long-standing privacy concerns. This paper studies the implications of data privacy policies for JKPs. To do so, we have reviewed the GDPR and identified different areas where it potentially conflicts with JKPs