Making Sense of News, the Knowledge Graph Way

Making Sense of News, the Knowledge Graph Way` is a blog post that explains how to create a news monitoring data pipeline that combines natural language processing (NLP) and knowledge graph technologies using Neo4j. The data pipeline consists of three parts: scraping articles from an internet provider of news, running the articles through an NLP pipeline and storing results in the form of a knowledge graph, and enriching the knowledge graph with information from the WikiData API. The authors demonstrate how to perform simple network analysis and find insights using the knowledge graph. They also explore graph data science techniques such as co-occurrence networks, community detection, and centrality. The post provides a comprehensive overview of the benefits of using a knowledge graph to store news data and offers practical advice for implementing a similar data pipeline.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.