Reasoning analyzes information, applies rules, and draws conclusions from evidence or premises. In an intelligent system, a reasoning engine uses one or more knowledge bases to produce insights and suggestions for user goals. section 2.1 section 2.4
Distinguish reasoning modes by how they move from available knowledge to conclusions:
- Deductive reasoning starts with general statements or hypotheses and derives a specific logical conclusion, assuming the premises are true. section 2.4
- Inductive reasoning generalizes from individual examples, such as labeled spam messages, to classify previously unseen cases. section 2.4
- Practical reasoning must account for uncertain, inaccurate, or ambiguous information and determine how missing knowledge can be inferred. section 2.4
Knowledge representation constrains reasoning: richer representations can be harder to process efficiently, while domain-appropriate knowledge graphs provide structured entities, relationships, and properties for inference. Multiple knowledge bases and reasoning engines may operate sequentially or in parallel, passing outputs between processes. section 2.1 section 1.1
For natural-language questions over a knowledge graph, reasoning becomes an implementation pipeline: detect intent, extract and annotate the schema, identify relevant relationships, generate a query, execute it, handle errors, visualize results, and summarize important patterns. Structured intermediate outputs—potential relationships, a reasoning plan, then the Cypher query—reduce hallucinated relationships and make generation more reliable. section 14.4 section 14.7.3
Testing multistep reasoning
A farmer must transport himself and a sheep across a river using a boat that holds one person and one animal. Solving the smallest-trip puzzle tests whether a model plans intermediate steps instead of jumping directly to an answer. Chain-of-thought and scratchpad prompting similarly provide intermediate workings before the final response. section 14.7
