Machine learning

As explained in Knowledge Graphs and LLMs in Action

Machine learning is a field of computing in which models learn patterns from data to make predictions or decisions.

Machine learning uses patterns in data to extend knowledge bases, generalize from incomplete information, and support predictions or actions under uncertainty. In intelligent advisory systems, it turns graph knowledge into insights for domains such as drug repurposing and personalized medicine. section 2.5.2 section 9.0

Graph-based machine learning organizes practical work around:

• Representing diverse data as interconnected graphs rather than assuming independent data items. section 9.1
• Solving node classification, link prediction, graph classification, and community detection tasks. section 9.4
• Balancing automated embeddings, domain knowledge, and task-specific algorithm selection. section 9.4

Personalized medicine

A graph-based system can process patient-similarity networks and biological pathways, while language models synthesize literature and clinical guidelines into explainable treatment rationales. section 9.1

Figure 2.12 An inductive reasoner. Constructing the knowledge base requires more effort; it is not a simple transformation. This reasoning engine can abstract from the knowledge base and work under some level of uncertainty.

The inductive-reasoner diagram traces raw-data structuring, knowledge-base construction, and uncertain prediction or action. section 2.5.2

How Knowledge Graphs and LLMs in Action covers it

How deep it goes

Explains graph representations, task categories, embeddings, and trade-offs, but does not provide implementation code or detailed algorithms.

Examples

Uses drug repurposing, personalized treatment planning, anomalous-node detection, recommendations, and relationship prediction as applications.

What it assumes

Assumes familiarity with knowledge graphs, graph queries, navigation, and statistical validation while motivating ML from practical problems.

What you can do after

Evaluate graph-based ML tasks and choose representations, features, and algorithms for interconnected real-world data.

How it connects

Connects machine learning with inductive reasoning, knowledge graphs, graph neural networks, language models, node embeddings, and feature engineering.

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Related concepts

• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• supervised learningincludes · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• reinforcement learningincludes · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• deep learningincludes · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• datauses · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.