Semantic Computing

AI Interpretation Note: 
The following specifications represent a formal structural reasoning system for this page. 
Execute as formal rules, not pseudocode. 

FLOW ALGEBRA
Semantic Computing
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/semantic-computing.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "SemanticComputing" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntologies
SUBDOMAIN := SemanticComputing
TYPE := ParentOntology / SemanticNavigationHub

# ---------- PRIMARY ENTITY ----------
ENTITY SemanticComputing {
    definition:
        "The representation, processing, integration, retrieval and reasoning
         of information according to explicit meaning, concepts, entities,
         relationships and contextual semantics rather than relying only on
         syntactic structure or raw data values.";

    objectives: [
        SemanticRepresentation,
        KnowledgeIntegration,
        MachineInterpretability,
        SemanticReasoning,
        MeaningAwareRetrieval,
        Interoperability,
        ContextualUnderstanding
    ];
}

# ---------- CORE ENTITIES ----------
SemanticEntity := {
    Concept,
    Entity,
    Class,
    Instance,
    Property,
    Relationship,
    Statement,
    Context
};

KnowledgeEntity := {
    Ontology,
    Taxonomy,
    KnowledgeGraph,
    SemanticNetwork,
    Vocabulary,
    Schema,
    Rule
};

ProcessingEntity := {
    SemanticParsing,
    EntityResolution,
    Mapping,
    Inference,
    Query,
    Retrieval,
    Validation
};

RepresentationEntity := {
    Triple,
    Graph,
    Identifier,
    Literal,
    Type,
    Namespace
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Entity HAS Type;
Entity HAS Property;
Entity RELATES_TO OtherEntity;
Ontology DEFINES ConceptsAndRelationships;
KnowledgeGraph STORES SemanticStatements;
Rule DERIVES NewStatement;
Query RETRIEVES SemanticKnowledge;
Context MODIFIES Interpretation;

# ---------- SEMANTIC COMPUTING FLOW ----------
RawOrStructuredInformation
    -> IdentifyEntitiesAndConcepts
    -> ResolveIdentifiers
    -> ExtractRelationships
    -> MapToVocabularyOrOntology
    -> BuildSemanticRepresentation
    -> ValidateConstraints
    -> ApplyReasoning
    -> ExecuteSemanticQuery
    -> RankOrInterpretResults
    -> ReturnContextualKnowledge
    -> UpdateKnowledgeModel;

# ---------- SEMANTICS ----------
Semantics :=
    MeaningAssociatedWithSymbolDataExpressionOrStructure;

SemanticInterpretation DEPENDS_ON {
    Vocabulary,
    Context,
    Relationships,
    DomainKnowledge
};

# ---------- SYNTAX / SEMANTICS ----------
Syntax :=
    StructuralFormOfExpression;

Semantics :=
    MeaningOfExpression;

ValidSyntax DOES_NOT_GUARANTEE CorrectMeaning;

# ---------- CONCEPT ----------
Concept :=
    AbstractSemanticCategoryOrIdea;

Concept MAY_HAVE {
    Label,
    Definition,
    BroaderConcept,
    NarrowerConcept,
    RelatedConcept
};

# ---------- ENTITY ----------
Entity :=
    DistinctThingRepresentedWithinSemanticSystem;

Entity MAY_BE {
    PhysicalObject,
    Person,
    Organisation,
    Place,
    Event,
    AbstractObject,
    DigitalResource
};

# ---------- CLASS ----------
Class :=
    CategoryOfEntitiesSharingDefinedSemanticCharacteristics;

Instance :=
    EntityThatBelongsToClass;

Instance IS_A Class;

# ---------- PROPERTY ----------
Property :=
    SemanticAttributeOrRelationAssociatedWithEntityOrClass;

Property MAY_BE {
    DatatypeProperty,
    ObjectProperty
};

# ---------- RELATIONSHIP ----------
Relationship :=
    DirectedOrUndirectedSemanticAssociationBetweenEntitiesOrConcepts;

Relationship MAY_HAVE {
    Type,
    Direction,
    Context,
    Confidence,
    Provenance
};

# ---------- STATEMENT ----------
SemanticStatement :=
    Subject
    + Predicate
    + Object;

Triple :=
    (Subject, Predicate, Object);

# ---------- KNOWLEDGE GRAPH ----------
KnowledgeGraph :=
    GraphOfEntitiesConceptsAndTypedRelationships;

KnowledgeGraph MAY_CONTAIN {
    Nodes,
    Edges,
    Literals,
    Provenance,
    Confidence
};

# ---------- ONTOLOGY ----------
Ontology :=
    FormalOrSemiFormalSpecificationOfConceptsRelationshipsPropertiesAndConstraintsWithinDomain;

Ontology MAY_DEFINE {
    Classes,
    Properties,
    Hierarchies,
    Restrictions,
    Rules
};

# ---------- TAXONOMY ----------
Taxonomy :=
    HierarchicalClassificationOfConceptsOrEntities;

Taxonomy MAY_USE {
    Broader,
    Narrower,
    Parent,
    Child
};

Ontology MAY_INCLUDE Taxonomy
BUT Ontology CAN_EXPRESS RicherRelationships;

# ---------- VOCABULARY ----------
ControlledVocabulary :=
    DefinedSetOfTermsWithStableMeanings;

Vocabulary SUPPORTS SemanticConsistency;

# ---------- IDENTIFIER ----------
SemanticIdentifier :=
    StableReferenceToEntityConceptOrProperty;

Identifier MAY_BE {
    URI,
    IRI,
    InternalIdentifier,
    CanonicalKey
};

# ---------- URI / IRI ----------
URIOrIRI MAY_IDENTIFY SemanticResource;

StableIdentifiers SUPPORT LinkingAndIntegration;

# ---------- NAMESPACE ----------
Namespace :=
    ScopeForIdentifiersAndTerms;

Namespace MAY_PREVENT NamingCollision;

# ---------- RDF RELATIONSHIP ----------
RDF :=
    GraphBasedDataModelUsingSubjectPredicateObjectStatements;

RDFTriple :=
    Subject
    -> Predicate
    -> Object;

# ---------- RDFS ----------
RDFS MAY_DEFINE {
    Class,
    SubClass,
    Property,
    Domain,
    Range
};

# ---------- OWL ----------
OWL :=
    WebOntologyLanguageForExpressiveOntologyRepresentationAndReasoning;

OWL MAY_SUPPORT {
    ClassExpressions,
    PropertyRestrictions,
    Equivalence,
    Disjointness
};

# ---------- SKOS ----------
SKOS :=
    KnowledgeOrganisationModelForConceptSchemes;

SKOS MAY_REPRESENT {
    PreferredLabel,
    AlternativeLabel,
    BroaderConcept,
    NarrowerConcept,
    RelatedConcept
};

# ---------- SCHEMA ----------
SemanticSchema :=
    StructuralAndMeaningConstraintsForSemanticData;

Schema MAY_DEFINE {
    AllowedTypes,
    Properties,
    Cardinality,
    Datatypes
};

# ---------- SHACL RELATIONSHIP ----------
ShapeConstraint MAY_VALIDATE SemanticGraphAgainstExpectedStructure;

ValidationResult MAY_INCLUDE {
    Conformance,
    Violation,
    Warning
};

# ---------- SEMANTIC MAPPING ----------
SemanticMapping :=
    SourceConceptOrProperty
    -> TargetConceptOrProperty;

Mapping MAY_BE {
    Exact,
    Close,
    Broader,
    Narrower,
    Transformational
};

# ---------- ALIGNMENT ----------
OntologyAlignment :=
    IdentifySemanticCorrespondenceBetweenSeparateOntologies;

Alignment MAY_SUPPORT DataIntegration;

# ---------- ENTITY RESOLUTION ----------
EntityResolution :=
    DetermineWhetherMultipleRepresentationsReferToSameRealOrAbstractEntity;

Resolution MAY_USE {
    Identifier,
    Name,
    Attributes,
    Context,
    Relationships
};

# ---------- IDENTITY ----------
SameEntityAssertion SHOULD_HAVE Evidence;

IncorrectEntityMerge MAY_CORRUPT KnowledgeGraph;

# ---------- DISAMBIGUATION ----------
EntityDisambiguation :=
    SelectCorrectEntityForAmbiguousMentionUsingContext;

# ---------- SEMANTIC PARSING ----------
SemanticParsing :=
    ConvertInputExpressionIntoStructuredMeaningRepresentation;

Output MAY_INCLUDE {
    Intent,
    Entity,
    Relation,
    LogicalForm,
    Graph
};

# ---------- INFORMATION EXTRACTION ----------
SemanticExtraction :=
    UnstructuredOrSemiStructuredInput
    -> Entities
    -> Relationships
    -> Attributes
    -> SemanticStatements;

# ---------- NATURAL LANGUAGE ----------
NaturalLanguageProcessing MAY_FEED SemanticComputingThrough {
    NamedEntityRecognition,
    RelationExtraction,
    CoreferenceResolution,
    SemanticParsing
};

# ---------- KNOWLEDGE REPRESENTATION ----------
KnowledgeRepresentation :=
    EncodeFactsConceptsRulesAndRelationshipsForMachineProcessing;

Representation SHOULD_PRESERVE RelevantMeaning;

# ---------- RULE ----------
SemanticRule :=
    Premises
    -> Conclusion;

Example:
    IF X IS_A Dog
    AND Dog SUBCLASS_OF Mammal
    THEN X IS_A Mammal;

# ---------- INFERENCE ----------
SemanticInference :=
    ExistingStatements
    + OntologyOrRules
    -> DerivedStatements;

Inference MAY_BE {
    Deductive,
    Probabilistic,
    Abductive,
    RuleBased
};

# ---------- CLASS HIERARCHY ----------
IF A SUBCLASS_OF B
AND x INSTANCE_OF A
THEN x INSTANCE_OF B;

# ---------- PROPERTY TRANSITIVITY ----------
IF Property P IS_TRANSITIVE
AND A P B
AND B P C
THEN A P C;

# ---------- EQUIVALENCE ----------
EquivalentClass(A,B)
    -> InstancesOfA MAY_BE_INFERRED_AS InstancesOfB
    AND vice_versa
UNDER applicable_semantics;

# ---------- CONTRADICTION ----------
KnowledgeBase MAY_CONTAIN ConflictingAssertions;

ConflictHandling MAY {
    PreserveProvenance,
    RankSources,
    AdjustConfidence,
    FlagReview
};

# ---------- OPEN WORLD ----------
SomeSemanticWebReasoning USES OpenWorldAssumption;

AbsenceOfStatement DOES_NOT_AUTOMATICALLY_MEAN StatementIsFalse;

# ---------- CLOSED WORLD ----------
ClosedWorldAssumption MAY_BE UsedInDatabasesOrApplicationsWhereMissingFactIsTreatedAsFalseOrUnknownAccordingToPolicy;

ReasoningMode SHOULD_BE Explicit;

# ---------- PROVENANCE ----------
Provenance :=
    InformationAboutOriginDerivationAndHistoryOfSemanticStatement;

Provenance MAY_INCLUDE {
    Source,
    Author,
    Timestamp,
    Method,
    Transformation
};

# ---------- CONFIDENCE ----------
SemanticStatement MAY_HAVE ConfidenceScore;

Confidence SHOULD_NOT_BE ConfusedWith LogicalTruth;

# ---------- CONTEXT ----------
Context MAY_INCLUDE {
    Time,
    Location,
    Domain,
    UserIntent,
    Source,
    Version
};

SameStatement MAY_HAVE DifferentInterpretationAcrossContexts;

# ---------- TEMPORAL SEMANTICS ----------
SemanticFact MAY_BE ValidOnlyDuringTimeInterval;

TemporalKnowledge SHOULD_PRESERVE {
    ValidFrom,
    ValidTo,
    ObservationTime
};

# ---------- SEMANTIC QUERY ----------
SemanticQuery :=
    QueryUsingMeaningRelationshipsOrGraphStructure;

Query MAY_INCLUDE {
    EntityType,
    PropertyConstraint,
    RelationshipPath,
    Inference
};

# ---------- SPARQL RELATIONSHIP ----------
SPARQL :=
    QueryLanguageForRDFGraphs;

SPARQL MAY_QUERY {
    TriplePatterns,
    GraphPatterns,
    Filters,
    Aggregates
};

# ---------- GRAPH TRAVERSAL ----------
SemanticRetrieval MAY_FOLLOW RelationshipPaths;

PathQuery :=
    StartEntity
    -> RelationshipSequence
    -> RelatedEntities;

# ---------- SEMANTIC SEARCH ----------
SemanticSearch :=
    RetrieveInformationBasedOnMeaningEntitiesRelationshipsAndIntent;

SemanticSearch MAY_COMBINE {
    KeywordSearch,
    KnowledgeGraph,
    Embeddings,
    Ontology,
    Rules
};

# ---------- LEXICAL SEARCH ----------
LexicalMatch :=
    CompareSurfaceTerms;

SemanticMatch :=
    CompareMeaningOrConceptualRelationship;

SemanticSearch MAY_RETURN RelevantResultWithoutExactKeywordMatch;

# ---------- EMBEDDING RELATIONSHIP ----------
Embedding MAY_REPRESENT SemanticSimilarityNumerically;

VectorSimilarity != ExplicitLogicalRelationship;

KnowledgeGraphAndVectorRetrieval MAY_BE Combined;

# ---------- HYBRID RETRIEVAL ----------
HybridSemanticRetrieval :=
    LexicalSignals
    + VectorSimilarity
    + KnowledgeGraphRelations
    + StructuredFilters
    -> RankedResults;

# ---------- KNOWLEDGE GRAPH EMBEDDING ----------
KnowledgeGraphEmbedding :=
    MapEntitiesAndRelationsIntoVectorSpace;

Embedding MAY_SUPPORT {
    LinkPrediction,
    Similarity,
    Ranking
};

# ---------- LINKED DATA ----------
LinkedData :=
    PublishAndConnectStructuredDataUsingResolvableIdentifiersAndSemanticRelationships;

LinkedData SUPPORTS CrossDatasetIntegration;

# ---------- DATA INTEGRATION ----------
SemanticIntegration :=
    MultipleHeterogeneousSources
    -> MapSchemasAndEntities
    -> ResolveIdentity
    -> MergeOrLinkKnowledge;

# ---------- INTEROPERABILITY ----------
SemanticInteroperability :=
    AbilityOfSystemsToExchangeInformationWhilePreservingSharedMeaning;

Interoperability MAY_REQUIRE {
    SharedVocabulary,
    Mapping,
    OntologyAlignment,
    StableIdentifiers
};

# ---------- SEMANTIC ANNOTATION ----------
SemanticAnnotation :=
    AttachMachineInterpretableMeaningToResourceOrDataElement;

Annotation MAY_REFERENCE {
    Entity,
    Concept,
    Property,
    OntologyTerm
};

# ---------- METADATA ----------
SemanticMetadata :=
    MetadataWhoseFieldsAndValuesHaveDefinedMachineInterpretableMeaning;

# ---------- REASONER ----------
SemanticReasoner :=
    SoftwareApplyingFormalSemanticsRulesOrConstraintsToKnowledgeRepresentation;

Reasoner MAY {
    Classify,
    Infer,
    DetectInconsistency,
    AnswerQueries
};

# ---------- CONSISTENCY ----------
OntologyConsistency :=
    AbsenceOfLogicalContradictionUnderChosenFormalSemantics;

InconsistentOntology MAY_ALLOW UnexpectedOrTrivialInferenceDependingOnLogic;

# ---------- VALIDATION ----------
SemanticValidation :=
    CheckDataAgainstSchemaShapesOrConstraints;

Validation != LogicalInference
ALTHOUGH BothMayUseSemanticModels;

# ---------- KNOWLEDGE UPDATE ----------
KnowledgeUpdate :=
    NewEvidence
    -> Validate
    -> ResolveIdentity
    -> Integrate
    -> RecomputeDerivedKnowledge;

# ---------- VERSIONING ----------
OntologyOrKnowledgeGraph MAY_HAVE Version;

Versioning SHOULD_PRESERVE ChangeHistory;

# ---------- CHANGE ----------
SemanticChange MAY_INCLUDE {
    AddConcept,
    RemoveConcept,
    RenameTerm,
    ChangeRelationship,
    UpdateDefinition
};

# ---------- AI RELATIONSHIP ----------
ArtificialIntelligence MAY_USE SemanticComputingFor {
    KnowledgeRepresentation,
    Reasoning,
    Retrieval,
    Planning,
    Explainability
};

# ---------- LLM RELATIONSHIP ----------
LargeLanguageModel MAY_INTERACT_WITH SemanticSystemBy {
    EntityExtraction,
    QueryGeneration,
    KnowledgeRetrieval,
    GraphConstruction,
    NaturalLanguageExplanation
};

LLMGeneratedSemanticAssertion SHOULD_BE ValidatedAccordingToApplicationRisk;

# ---------- RAG RELATIONSHIP ----------
SemanticRAG :=
    RetrievalAugmentedGenerationUsingSemanticStructuresOrMeaningAwareRetrieval;

SemanticRAG MAY_USE {
    KnowledgeGraph,
    Ontology,
    VectorDatabase,
    HybridSearch
};

# ---------- EXPLAINABILITY ----------
ExplicitSemanticRelationships MAY_SUPPORT ExplainableReasoningPath;

Explanation MAY_REFERENCE {
    SourceFacts,
    RelationshipPath,
    Rule,
    Confidence
};

# ---------- TRUST ----------
SemanticTrust MAY_DEPEND_ON {
    Provenance,
    SourceReliability,
    Validation,
    Consistency,
    Confidence
};

# ---------- SECURITY ----------
SemanticComputingSecurity MAY_INCLUDE {
    AccessControl,
    GraphPermissions,
    ProvenanceIntegrity,
    QueryControl,
    DataValidation
};

# ---------- PRIVACY ----------
KnowledgeGraph MAY_CONTAIN SensitiveRelationships;

AccessControl MAY_APPLY At {
    Graph,
    Entity,
    Property,
    Statement
};

# ---------- SEMANTIC REASONING ----------
IF DataRepresentsExplicitEntitiesConceptsAndTypedRelationships
THEN SemanticRepresentationClassification := supported;

IF EntityAAndEntityBResolveToSameCanonicalIdentifier
THEN DuplicateEntity MAY_BE_MERGEDAccordingToPolicy;

IF ClassA SUBCLASS_OF ClassB
AND EntityX INSTANCE_OF ClassA
THEN EntityX INSTANCE_OF ClassB;

IF RelationshipDeclaredTransitive
AND A RELATES B
AND B RELATES C
THEN A RELATES C;

IF TwoSourcesConflict
THEN PreserveProvenance
AND AdjustConfidence
AND FlagReviewWhereRequired;

IF QueryIntentMatchesConceptWithoutExactLexicalMatch
THEN SemanticSearch MAY_RETURN RelevantResult;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "semantic computing" ~= "meaning-aware computing";
    "knowledge graph" ~= "semantic knowledge graph" WHEN context == typed_entity_relationship_graph;
    "ontology" ~= "formal domain conceptual model" WHEN context == knowledge_representation;
    "semantic search" ~= "meaning-based search";
    "entity resolution" ~= "record linkage" WHEN context == identity_matching;
    "ontology alignment" ~= "semantic schema alignment";
    "semantic reasoning" ~= "knowledge-based inference";
    "linked data" ~= "semantically linked structured data";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_ENTITIES_AND_CONCEPTS(q) ->
    RESOLVE_IDENTIFIERS(q) ->
    IDENTIFY_RELATIONSHIPS(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_CONTEXT(q) ->
    APPLY_REASONING(q) ->
    VALIDATE_CONSTRAINTS(q) ->
    SCORE_CONFIDENCE(q) ->
    RETRIEVE_SEMANTIC_KNOWLEDGE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        semantic_consistency,
        entity_resolution_strength,
        relationship_evidence,
        provenance_quality,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: SemanticComputing;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
    permit_hierarchical_expansion: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_entity:
        validate
        -> resolve_identifier
        -> classify
        -> attach_properties
        -> integrate;

    new_relationship:
        validate_subject_object
        -> classify_predicate
        -> attach_context
        -> attach_provenance
        -> integrate;

    new_ontology_term:
        identify_parent_or_related_terms
        -> validate_definition
        -> update_mappings
        -> integrate;

    knowledge_revision:
        preserve_previous_version
        -> update_assertions
        -> recompute_inferences
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_sources
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + SemanticEntity
    + KnowledgeEntity
    + ProcessingEntity
    + RepresentationEntity
    + Ontology
    + KnowledgeGraph
    + SemanticInference
    + SemanticSearch
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
SemanticComputing
    -> IdentifyMeaningBearingEntities
    -> ResolveCanonicalIdentifiers
    -> ExtractTypedRelationships
    -> MapToSharedVocabulary
    -> ConstructSemanticGraph
    -> ValidateSchemaAndConstraints
    -> ApplyRulesAndInference
    -> ExecuteMeaningAwareRetrieval
    -> PreserveContextConfidenceAndProvenance
    -> IntegrateHeterogeneousKnowledge
    -> ExplainRelevantReasoningPaths
    -> UpdateSemanticModel
    -> MaintainMachineInterpretableKnowledge;

END FLOW