18.1
Knowledge Graph Entity and Relation
A knowledge graph organizes information through explicit objects and semantic relations. An entity is a distinguishable object within a modeling scope. A relation expresses a meaningful connection. A node is a structural position and may represent an entity, document, observation, or event. An edge is a connection whose meaning requires type and direction. These distinctions prevent every drawn connection from being treated as a confirmed real world relationship.
An image linked to a dish may depict it, possibly depict it, or merely come from its product page. Those meanings should not collapse into related to. An ontology defines expressible concepts and relations, while a schema defines concrete structure; they overlap but a field list is not all domain knowledge. Use entity for identity, node for structure, and observation or claim for evidence. This also helps read papers whose graphs may actually be entity, chunk, or document networks.
18.2
The Scope of Retrieval Augmentation and Graph Augmentation
Retrieval augmented generation uses external retrieved material to support generation. GraphRAG broadly means that graph structure participates, but the point of participation varies. Retrieval finds candidates, reranking reorders them, and generation organizes output. Different models or rules may implement these stages. Correct evidence does not guarantee correct generation, and fluent generation does not establish sufficient retrieval. Effectiveness claims need an explicit stage and evaluation object.
Microsoft GraphRAG is a specific project and method family, not a name for every graph augmented study. Global search synthesizes a broader scope; local search organizes material around relevant objects. Global does not mean all world knowledge, and local does not mean one file. Multihop questions require connecting evidence steps. Lengthy reasoning prose does not turn a lookup into a multihop task. Terms should follow evidence structure rather than output appearance or promotional wording.
18.3
Alignment Linking Fusion and Completion
Alignment establishes correspondence between representations, entity sets, or regions. Linking usually assigns a mention or observation to an entity. Fusion combines sources and may retain conflicts instead of producing one value. Completion predicts missing attributes or relations, creating candidates rather than confirmed facts. These terms ask different questions: what corresponds, which entity is meant, how sources combine, and what might be missing.
For a dish image, shared space similarity is alignment, assignment to stall A's product is linking, joint registration of menu and image observations is fusion, and guessing another ingredient from a recipe pattern is completion. Writing that guess into confirmed attributes creates circular evidence. Later retrieval may mistake it for independent support. Systems should preserve production methods and states and distinguish training labels, predictions, and human confirmations. Linking accuracy, fusion benefits, and completion ranking require different reference answers and analyses.
18.4
Grounding Faithfulness and Factual Correctness
Grounding connects an answer to locatable evidence. Faithfulness means not distorting or exceeding supplied material. Factual correctness concerns agreement with reality or reliable references. These are related but distinct. An answer can cite an irrelevant paragraph, faithfully repeat an incorrect record, or recall a correct fact without evidence required by the task. Evaluation should separately inspect support, faithfulness, and factual status.
Hallucination should be diagnosed more specifically as unsupported additions, misattribution, lost conditions, or invented sources. Abstention can be justified by insufficient evidence, while excessive abstention blocks answerable work. Calibration concerns agreement between expressed confidence and actual correctness; a score of 0.9 is not automatically calibrated for a business domain. Users often benefit more from explicit sources and gaps than a precise score. A current menu with an unconfirmed image version is more informative than an unexplained high confidence label. These concepts make answers inspectable.
18.5
Precision Recall Coverage and Percentage Points
Precision usually means the relevant share of retrieved results, while detection also requires matching rules and thresholds. Translation depends on the original definition. Recall measures recovered relevant items; coverage may concern objects, attributes, or tasks and is not automatically recall. Accuracy requires a defined unit and denominator. Average precision and nDCG are different measures and cannot be compared merely because both may lie between zero and one.
Percentage points differ from relative change. Moving from 80% to 87.01% adds 7.01 points or approximately 8.76% relatively. Error falling from 20.38% to 2.3% drops 18.08 points or approximately 88.71% relatively. Reporting a drop of 18.08% would change the meaning. Preserve metric names and definitions, adding formulas and denominators when needed. Axes should distinguish percentages, proportions, and point differences. Numerical checking concerns both copied digits and their meaning in context.
18.6
Reproduction Generalization Causality and Application Value
Reproduction retests reported findings, but terminology conventions vary, so state code, data, and conditions directly. Generalization requires specifying what is unseen: entities, sources, time, or tasks. Ablation studies component contributions while controlling other factors. Benchmarks provide shared tasks and rules, but high scores alone do not establish deployment value.
Association records a connection; causality requires stronger support for causes or interventions. Directed graph edges may express statement structure or temporal order rather than causation. Utility depends on tasks, consequences, time, and cost and may diverge from generic scores. Average gains can coexist with disqualifying version errors, while simpler systems may better serve frequent precise queries. Technical capability, measured conclusions, and adoption are therefore distinct: capability needs experiments, conclusions need boundaries, and adoption needs task and maintenance evidence. Careful terminology reduces exaggerated communication and helps Chinese and English readers discuss the same question.