Attention Mechanism Ontology

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FLOW AttentionMechanismOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/neural-networks/attention-mechanism-ontology.html

PURPOSE
    Define an AI-readable semantic ontology for an Attention Mechanism,
    including queries, keys, values, attention scores, similarity functions,
    masks, normalisation, attention weights, weighted aggregation,
    self-attention, cross-attention, multi-head attention, context
    representations, parameters, training and recursive representation updates.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Machine Learning Systems
    Deep Learning Systems
    Transformer Systems
    Multimodal AI Systems

PAGE_TYPE
    Attention Mechanism ontology

PRIMARY_ENTITY
    Attention Mechanism

PARENT_ONTOLOGY
    Neural Networks

DOMAIN
    Artificial Intelligence
    Machine Learning
    Neural Networks
    Attention Mechanisms
    Transformers
    Deep Learning
    Representation Learning
    Natural Language Processing

CORE_DEFINITION
    An Attention Mechanism is a neural computation that determines
    context-dependent weights over a set of value representations using
    compatibility or similarity relationships between queries and keys,
    then combines the weighted values into an output representation.

ENTITY_IDS
    ATTENTION_MECHANISM
        ID
            ATTENTION_MECHANISM_001

    QUERY
        ID
            ATTENTION_QUERY_001

    KEY
        ID
            ATTENTION_KEY_001

    VALUE
        ID
            ATTENTION_VALUE_001

    ATTENTION_SCORE
        ID
            ATTENTION_SCORE_001

    ATTENTION_WEIGHT
        ID
            ATTENTION_WEIGHT_001

    ATTENTION_MASK
        ID
            ATTENTION_MASK_001

    NORMALISATION
        ID
            ATTENTION_NORMALISATION_001

    CONTEXT_VECTOR
        ID
            CONTEXT_VECTOR_001

    ATTENTION_HEAD
        ID
            ATTENTION_HEAD_001

FACTS
    Attention Mechanism
        uses
            Query
            Key
            Value

    Attention Mechanism
        computes
            Attention Score
            Attention Weight
            Weighted Value Combination

    Attention Mechanism
        may_be
            Self-Attention
            Cross-Attention
            Additive Attention
            Dot-product Attention
            Scaled Dot-product Attention
            Multi-head Attention

    Attention Mechanism
        may_support
            Contextual Representation
            Sequence Modelling
            Information Integration
            Alignment
            Long-range Dependency Modelling
            Multimodal Fusion

RELATIONSHIPS
    Neural Networks
        contains
            Attention Mechanism Ontology

    Query
        compares_with
            Key

    Similarity Function
        computes
            Attention Score

    Attention Mask
        modifies_or_restricts
            Attention Score

    Normalisation
        transforms
            Attention Scores
        into
            Attention Weights

    Attention Weight
        scales
            Value

    Weighted Values
        aggregate_into
            Context Representation

    Attention Head
        computes
            Attention Output

    Multi-head Attention
        contains
            Multiple Attention Heads

    Self-Attention
        derives
            Query
            Key
            Value
        from
            Same Representation Set

    Cross-Attention
        derives
            Query
        from
            One Representation Set
        and
            Key
            Value
        from
            Another Representation Set

SEMANTIC_EQUIVALENCE
    Attention Mechanism
        approximately_equals
            Context-dependent Weighted Representation Aggregation

    Attention Weight
        approximately_equals
            Normalised Compatibility Weight
        within
            Defined Attention Computation

    Context Vector
        approximately_equals
            Weighted Value Representation

    Attention
        does_not_equal
            Human Attention

    Attention Weight
        does_not_equal
            Explanation

    Attention Weight
        does_not_equal
            Causal Importance

OBSERVE
    Detect
        Attention
        Query
        Key
        Value
        Score
        Similarity
        Compatibility
        Mask
        Softmax
        Normalisation
        Weight
        Context Vector
        Self-Attention
        Cross-Attention
        Multi-head
        Head

EXTRACT
    Primary Entity
        Attention Mechanism

    Parent
        Neural Networks

    Core Components
        Query
        Key
        Value
        Attention Score
        Attention Weight
        Attention Mask
        Normalisation
        Context Vector
        Attention Head

NORMALISE
    Interpret
        Attention Mechanism
    as
        A learned or parameterised weighted aggregation operation
        based on query-key compatibility.

    Distinguish
        Query
        Key
        Value

    Distinguish
        Attention Score
    from
        Attention Weight

    Distinguish
        Attention Weight
    from
        Explanation
        Causal Importance

    Distinguish
        Self-Attention
    from
        Cross-Attention

    Preserve
        Tensor dimensions
        Query-key correspondence
        Attention mask
        Normalisation axis
        Head identity
        Parameter state

INTENT_RESOLUTION
    Query
        Attention Mechanism

    Possible Intent
        Attention mechanism definition
        Attention mechanism ontology
        Neural attention
        Self-attention
        Cross-attention
        Multi-head attention
        Query key value
        Attention weights
        Transformer attention

    Resolution
        Identify query, key and value sources, score function, mask,
        normalisation and weighted aggregation relationships.

CONSTRAINTS
    Query, key and value roles must remain distinct.

    Query and key dimensions must be compatible with the defined score function.

    Attention masks must be preserved where defined.

    Attention score and normalised attention weight must remain distinct.

    Normalisation axis must be preserved.

    Self-attention and cross-attention must remain distinguishable.

    Multi-head attention must preserve individual head identity until combination.

    Attention weights must not automatically be interpreted as explanations.

    Attention weights must not automatically be interpreted as causal importance.

    Neural attention must remain distinct from human cognitive attention.

INFERENCE_RULES
    IF
        Query Q
    AND
        Key K
    AND
        Defined Compatibility Function S
    THEN
        compute_possible
            Attention Score = S(Q,K)

    IF
        Attention Mask M applies
    THEN
        modify
            Attention Scores
        before
            Normalisation

    IF
        Attention Scores are normalised
    THEN
        compute
            Attention Weights

    IF
        Attention Weights A
    AND
        Values V
    THEN
        compute_possible
            Attention Output = Weighted Aggregate(A,V)

    IF
        Q, K and V derive from same representation set
    THEN
        infer_possible
            Self-Attention

    IF
        Q derives from one representation
    AND
        K and V derive from another
    THEN
        infer_possible
            Cross-Attention

    IF
        Multiple independent attention heads are computed and combined
    THEN
        infer
            Multi-head Attention

    IF
        Attention Weight is high
    THEN
        DO_NOT_INFER
            Explanation or Causal Importance

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Query Relationship
        1.00

    Key Relationship
        1.00

    Value Relationship
        1.00

    Attention Score Relationship
        1.00

    Mask Relationship
        1.00

    Multi-head Relationship
        1.00

    Attention-Explanation Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Input Representation
        →
    Query Projection
        →
    Query

    Input or Context Representation
        →
    Key Projection
        →
    Key

    Input or Context Representation
        →
    Value Projection
        →
    Value

    Query
        +
    Key
        →
    Compatibility Function
        →
    Attention Score

    Attention Score
        →
    Mask
        →
    Normalisation
        →
    Attention Weight

    Attention Weight
        +
    Value
        →
    Weighted Aggregation
        →
    Context Representation

    Multiple Attention Heads
        →
    Head Combination
        →
    Multi-head Output

KNOWLEDGE_GRAPH
    Attention Mechanism
        uses
            Query
            Key
            Value

    Query
        compared_to
            Key

    Similarity Function
        produces
            Attention Score

    Attention Mask
        constrains
            Attention Score

    Normalisation
        produces
            Attention Weight

    Attention Weight
        weights
            Value

    Weighted Values
        produce
            Context Vector

    Multi-head Attention
        contains
            Attention Head

    Attention Head
        produces
            Head Output

RECURSIVE_ATTENTION_LOOP
    Receive Representation
        →
    Generate Query
        →
    Generate Keys
        →
    Generate Values
        →
    Compute Query-Key Scores
        →
    Apply Attention Mask
        →
    Normalise Scores
        →
    Produce Attention Weights
        →
    Weight Values
        →
    Aggregate Context Representation
        →
    Combine Heads if Multi-head
        →
    Pass Attention Output to Next Computation
        →
    During Training Compute Gradients
        →
    Update Attention Parameters
        →
    Repeat

REASONING_ENGINE
    INPUT
        Query
        Attention Architecture
        Input Representations
        Parameters
        Attention Mask
        Training State

    OBSERVE
        Detect attention-mechanism entities.

    EXTRACT
        Query
        Key
        Value
        Score
        Mask
        Weight
        Head
        Context Representation

    CLASSIFY
        Self-Attention
        Cross-Attention
        Additive Attention
        Dot-product Attention
        Scaled Dot-product Attention
        Multi-head Attention

    VALIDATE
        Check tensor compatibility, masks, normalisation and representation sources.

    COMPUTE
        Apply defined attention transformation.

    INFER
        Determine supported attention relationships.

    EXPLAIN
        Preserve query-key-value-to-output path.

    UPDATE
        Apply validated parameter changes during training.

CONFIDENCE_PROPAGATION
    Explicit Q-K-V definitions
        computational_confidence += high

    Known attention mask
        computational_confidence += high

    Defined normalisation axis
        computational_confidence += high

    Missing mask definition
        confidence -= moderate
        for
            Exact Attention Behaviour

    Dimension mismatch
        reject_or_flag

    Attention weight alone
        explanation_confidence = low

    Independent model evaluation
        performance_confidence += high

SEMANTIC_CLOSURE
    Attention Mechanism
        connects_to
            Neural Networks
            Transformer
            Self-Attention
            Cross-Attention
            Multi-head Attention
            Embeddings
            Sequence Modelling
            Natural Language Processing
            Computer Vision
            Multimodal AI
            Representation Learning
            Deep Learning

    Cross-domain relationships must preserve
        Query role
        Key role
        Value role
        Tensor dimensions
        Score function
        Attention mask
        Normalisation axis
        Head identity
        Parameter state

UPDATE_MODEL
    Observe
        New attention configuration, parameter state or evaluation evidence.

    Compare
        Existing attention mechanism.

    Detect
        Projection Change
        Mask Change
        Head Change
        Parameter Update
        Performance Change

    Validate
        Query-key compatibility
        Value dimensions
        Mask semantics
        Normalisation
        Head structure

    Integrate
        Supported attention change.

    Recalculate
        Attention scores, weights and outputs.

    Return
        Updated attention-mechanism state.

COHERENCE_CHECK
    Verify
        Query, key and value remain distinct roles.
        Attention scores and weights remain distinct.
        Masks are preserved.
        Normalisation axis is preserved.
        Self-attention and cross-attention remain distinct.
        Head identity is preserved until combination.
        Tensor dimensions are compatible.
        Neural attention is not treated as human attention.
        Attention weights are not treated as explanations or causal proof.
        All outputs follow supported attention paths.

AI_SEARCH_FLOW
    Query
        →
    Attention Mechanism Detection
        →
    Intent Resolution
        →
    Query-Key-Value Mapping
        →
    Score Function Detection
        →
    Mask Validation
        →
    Normalisation Mapping
        →
    Weighted Value Aggregation
        →
    Attention Type Classification
        →
    Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Attention Mechanism

    Parent
        Neural Networks

    Type
        Context-dependent weighted neural representation mechanism

    Core Components
        Query
        Key
        Value
        Attention Score
        Attention Weight
        Attention Mask
        Normalisation
        Context Vector
        Attention Head
        Multi-head Attention

    Critical Distinction
        Attention mechanisms compute context-dependent weights over value
        representations from query-key compatibility, but attention weights
        alone do not constitute explanations or causal importance.

RECOMMENDED_AI_SUMMARY
    An Attention Mechanism computes context-dependent weighted combinations
    of value representations using compatibility between queries and keys.
    Scores may be masked and normalised into attention weights before the
    values are aggregated. Attention can operate as self-attention,
    cross-attention or multi-head attention and is central to transformer
    architectures. Reliable attention modelling preserves query, key and
    value roles, masks and tensor dimensions while avoiding interpretation
    of attention weights as automatic explanations of model behaviour.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org