Knowledge injection frameworks are software tools or systems that help add external information or structured knowledge into artificial intelligence models or applications. This process improves the model’s understanding and decision-making by providing data it might not learn from its training alone. These frameworks manage how, when, and what information is inserted, ensuring consistency and relevance.
Category: Data Science
Temporal Graph Prediction
Temporal graph prediction is a technique used to forecast future changes in networks where both the structure and connections change over time. Unlike static graphs, temporal graphs capture how relationships between items or people evolve, allowing predictions about future links or behaviours. This helps in understanding and anticipating patterns in dynamic systems such as social…
Knowledge Graph Completion
Knowledge graph completion is the process of filling in missing information or relationships in a knowledge graph, which is a type of database that organises facts as connected entities. It uses techniques from machine learning and data analysis to predict and add new links or facts that were not explicitly recorded. This helps make the…
Bayesian Optimization Strategies
Bayesian optimisation strategies are methods used to efficiently find the best solution to a problem when evaluating each option is expensive or time-consuming. They work by building a model that predicts how good different options might be, then using that model to decide which option to try next. This approach helps to make the most…
Dynamic Feature Selection
Dynamic feature selection is a process in machine learning where the set of features used for making predictions can change based on the data or the situation. Unlike static feature selection, which picks a fixed set of features before training, dynamic feature selection can adapt in real time or for each prediction. This approach helps…
Knowledge Transfer Protocols
Knowledge Transfer Protocols are structured methods or systems used to pass information, skills, or procedures from one person, group, or system to another. They help make sure that important knowledge does not get lost when people change roles, teams collaborate, or technology is updated. These protocols can be written guides, training sessions, digital tools, or…
Domain Generalization Techniques
Domain generalisation techniques are methods used in machine learning to help models perform well on new, unseen data from different environments or sources. These techniques aim to make sure a model can handle differences between the data it was trained on and the data it will see in real use. This helps reduce the need…
Knowledge-Augmented Inference
Knowledge-augmented inference is a method where artificial intelligence systems use extra information from external sources to improve their understanding and decision-making. Instead of relying only on what is directly given, the system looks up facts, rules, or context from databases, documents, or knowledge graphs. This approach helps the AI make more accurate and informed conclusions,…
Uncertainty-Aware Models
Uncertainty-aware models are computer models designed to estimate not only their predictions but also how confident they are in those predictions. This means the model can communicate when it is unsure about its results. Such models are useful in situations where making a wrong decision could be costly or risky, as they help users understand…
Temporal Knowledge Graphs
Temporal Knowledge Graphs are data structures that store information about entities, their relationships, and how these relationships change over time. Unlike standard knowledge graphs, which show static connections, temporal knowledge graphs add a time element to each relationship, helping track when things happen or change. This allows for more accurate analysis of events, trends, and…