A knowledge graph is a way of organising information so that different pieces of data are connected to each other, much like a web. It stores facts about people, places, things, and how they are related, allowing computers to understand and use this information more effectively. Knowledge graphs help systems answer questions, find patterns, and…
A learning graph is a visual or data-based representation showing how different pieces of knowledge or skills are connected and build on each other. It maps out the steps or concepts that need to be learned in a particular order for better understanding. Learning graphs help organise information so learners can see what they already…
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 signal extraction is the process of identifying and isolating meaningful patterns or information from data that is organised on a network or graph. In such data, each node in the graph has a value, and these values can represent anything from sensor readings to social media activity. The aim is to filter out noise…
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 feature modelling is the process of identifying and using important characteristics or patterns from data that are represented as graphs. In graphs, data points are shown as nodes, and the connections between them are called edges. By extracting features from these nodes and edges, such as how many connections a node has or how…
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…