Building Knowledge Graphs with Python

Abstract

Data comes in a variety of forms. Data is useful if we are able to extract information from it and it becomes difficult when we are working on a large scale. Here, comes the knowledge graphs. The demands of emergency management are well-suited to the rich, adaptable, and uniform ways that knowledge graphs portray data. They build upon the standards, resources, methods, and methods for semantic data and computation which are already in place. Natural-language texts are a form of data source that would provide unique analytic issues, particularly those gathered via social media like Twitter. Knowledge graphs have been created using Python with tools like Neo4j that employ the Cypher query language for databases. A knowledge graphs is a well labelled graph where the labels have a well-defined meaning. It has many components such as nodes, edges and labels. A common example is the voice assistant and the search engines.