Graph predictive analytics is a method that uses the relationships and connections between items, often represented as a network or graph, to make predictions about future events or behaviours. Instead of looking at individual data points on their own, this approach considers how they are linked together, such as people in a social network or…

Graph knowledge modelling is a way of organising information using nodes and connections, much like a map of relationships. Each node represents an entity, such as a person, place, or concept, and the lines between them show how they are related. This approach helps to visualise and analyse complex sets of information by making relationships…

Graph knowledge extraction is the process of identifying and organising relationships between different pieces of information, usually by representing them as nodes and connections in a graph structure. This method helps to visualise and analyse how various elements, such as people, places, or concepts, are linked together. It is often used to turn unstructured text…

Graph-based analytics is a way of analysing data by representing it as a network of points and connections. Each point, called a node, represents an object such as a person, place, or device, and the connections, called edges, show relationships or interactions between them. This approach helps uncover patterns, relationships, and trends that might not…

Graph predictive systems are computer models that use graphs to represent relationships between different items and then predict future events, trends, or behaviours based on those relationships. In these systems, data is organised as nodes (representing entities) and edges (showing how those entities are connected). By analysing the connections and patterns in the graph, the…

Graph knowledge analysis is the process of examining and understanding data that is organised as networks or graphs, where items are represented as nodes and their relationships as edges. This approach helps identify patterns, connections and insights that might not be obvious from traditional data tables. It is commonly used to study complex systems, such…

Graph-based extraction is a method for finding and organising information by representing data as a network of interconnected points, or nodes, and links between them. This approach helps to identify relationships and patterns that might not be obvious in plain text or tables. It is commonly used in areas like text analysis and knowledge management…