Inductive inference

As explained in Knowledge Graphs and LLMs in Action

Inductive reasoning is the derivation of general patterns, conclusions, or predictions from observed cases, including cases with incomplete information.

Inductive inference derives broader patterns, conclusions, or predictions from observed cases. Unlike memorization, it generalizes to unseen cases; unlike deduction, its conclusions can remain uncertain or false even when observations are correct. section 2.4 section 2.5.2

An inductive reasoning pipeline typically proceeds as follows:

• Transform raw observations into structured data, often using machine learning or natural language processing. section 2.5.2
• Store the resulting concepts, relationships, and observations in a knowledge base or knowledge graph. section 2.5.2 section 2.1
• Abstract patterns from available data to predict outcomes or generate actions, even when information is incomplete. section 2.5.2

Spam filtering

A filter that stores only previously labeled spam messages and matches new messages performs retrieval, not proper learning. An inductive filter generalizes from labeled examples to identify unseen spam messages. section 2.4

Inductive learning is especially useful when new data arrives. In graph learning, parametric mappings use node features to generate representations for unseen nodes, whereas shallow lookup-table embeddings remain tied to nodes observed during training. section 11.1.1 section 11.3.2

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.

Use the inductive-reasoner diagram to trace structured-data construction, knowledge-base expansion, uncertain abstraction, and prediction or action. section 2.5.2

How Knowledge Graphs and LLMs in Action covers it

How deep it goes

Explains generalization, uncertainty, and a structured-data pipeline, but stops short of detailed learning algorithms or mathematical derivations.

Examples

Uses spam classification, coin and grandfather generalizations, and graph embeddings for social, biological, and medical networks.

What it assumes

Assumes familiarity with data and basic reasoning, while introducing the distinction between deduction, memorization, and inductive generalization.

What you can do after

Evaluate/understand how models generalize from observations, structure raw data, and make predictions despite incomplete information.

How it connects

Connects machine learning, knowledge representation, knowledge graphs, natural language processing, deductive reasoning, and graph representation learning.

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Also explained in

• 

  liveBookmanning.com
  Graph Neural Networks in ActionInductive learning learns a generalizable function or representation from observed data for application to unseen data.
• 

  liveBookmanning.com
  Sutskever's ListInductive inference is the process of inferring the process or hypothesis that could have generated observed data.

Compare how 3 books explain inductive inference →

Related concepts

• transductive learningcontrasts with · Transductive learning infers representations or predictions for specific entities and structures observed during training rather than learning rules that generalize to unseen entities.
• generalizationproduces · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
• multi-step reasoningkind of · Multi-step reasoning derives a conclusion, solution, or decision through multiple connected intermediate inferential or problem-solving steps.
• deductive reasoningcontrasts with · Deductive reasoning is the process of deriving conclusions that necessarily follow from stated general premises, rules, or hypotheses.
• machine learninguses · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.

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.