Semantic Computing Ontology

FLOW ALGEBRA
Semantic Computing Ontology
Kireth Semantic Research Archive

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

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "SemanticComputingOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := SemanticComputing
ONTOLOGY := SemanticComputingOntology

# ---------- PRIMARY ENTITY ----------
ENTITY SemanticComputing {
    definition:
        "A computational approach in which information is represented and
         processed according to explicit meaning, concepts, entities,
         relationships, context and knowledge structures so machines can
         integrate, retrieve and reason over information semantically.";

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

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

KnowledgeStructure := {
    Ontology,
    KnowledgeGraph,
    Taxonomy,
    Vocabulary,
    SemanticNetwork,
    Schema,
    RuleBase
};

SemanticOperation := {
    Annotation,
    EntityResolution,
    Mapping,
    Alignment,
    Inference,
    Validation,
    Query,
    Retrieval
};

EvidenceEntity := {
    Source,
    Provenance,
    Confidence,
    Timestamp,
    Version
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Entity INSTANCE_OF Class;
Class MAY_SUBCLASS_OF OtherClass;
Entity HAS Property;
Entity RELATES_TO OtherEntity;
Ontology DEFINES SemanticTerms;
KnowledgeGraph STORES SemanticStatements;
Rule DERIVES Statement;
Context QUALIFIES Interpretation;
Provenance SUPPORTS Statement;

# ---------- SEMANTIC COMPUTATION FLOW ----------
InputInformation
    -> ParseStructure
    -> IdentifyEntities
    -> IdentifyConcepts
    -> ResolveIdentity
    -> ExtractRelationships
    -> MapToOntology
    -> BuildSemanticRepresentation
    -> ValidateKnowledge
    -> ApplyInference
    -> ExecuteSemanticQuery
    -> ReturnContextualResult
    -> UpdateKnowledge;

# ---------- SEMANTICS ----------
Semantics :=
    MeaningAssignedToDataSymbolExpressionOrStructure;

SemanticMeaning MAY_DEPEND_ON {
    Domain,
    Vocabulary,
    Context,
    Relationships,
    Usage
};

# ---------- SYNTAX ----------
Syntax :=
    FormalOrStructuralArrangementOfSymbols;

Syntax != Semantics;

SyntacticallyValidData MAY_BE SemanticallyIncorrect;

# ---------- CONCEPT ----------
Concept :=
    AbstractMeaningCategoryRepresentedWithinKnowledgeSystem;

Concept MAY_HAVE {
    PreferredLabel,
    AlternativeLabel,
    Definition,
    BroaderConcept,
    NarrowerConcept,
    RelatedConcept
};

# ---------- ENTITY ----------
Entity :=
    IdentifiableThingRepresentedWithinSemanticModel;

Entity MAY_BE {
    Physical,
    Digital,
    Abstract,
    Event,
    Agent,
    Location
};

# ---------- CLASS ----------
Class :=
    SemanticCategoryOfEntities;

Instance :=
    ParticularEntityBelongingToClass;

# ---------- SUBCLASS ----------
IF ClassA SUBCLASS_OF ClassB
THEN EveryValidInstanceOfClassA IS_ALSO InstanceOfClassB
UNDER standard_subclass_semantics;

# ---------- PROPERTY ----------
Property :=
    TypedSemanticCharacteristicOrRelationship;

Property MAY_BE {
    DatatypeProperty,
    ObjectProperty,
    AnnotationProperty
};

# ---------- RELATIONSHIP ----------
Relationship :=
    SemanticAssociationBetweenEntitiesOrConcepts;

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

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

Triple :=
    (Subject, Predicate, Object);

# ---------- GRAPH ----------
SemanticGraph :=
    Nodes
    + TypedEdges;

Node MAY_REPRESENT EntityOrConcept;
Edge MAY_REPRESENT Relationship;

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

KnowledgeGraph MAY_SUPPORT {
    Query,
    Traversal,
    Inference,
    Integration
};

# ---------- ONTOLOGY ----------
Ontology :=
    ExplicitSpecificationOfDomainConceptsRelationshipsPropertiesAndConstraints;

Ontology MAY_DEFINE {
    Classes,
    Hierarchy,
    Properties,
    Restrictions,
    Equivalence,
    Disjointness,
    Rules
};

# ---------- TAXONOMY ----------
Taxonomy :=
    HierarchicalClassificationStructure;

Taxonomy IS_OFTEN LessExpressiveThanOntology;

# ---------- CONTROLLED VOCABULARY ----------
ControlledVocabulary :=
    ManagedSetOfTermsWithDefinedMeanings;

Vocabulary REDUCES TerminologicalAmbiguity;

# ---------- IDENTIFIER ----------
SemanticIdentifier :=
    StableReferenceToSemanticResource;

Identifier MAY_BE {
    URI,
    IRI,
    CanonicalIdentifier,
    InternalKey
};

StableIdentifier SUPPORTS LinkingAndEntityResolution;

# ---------- NAMESPACE ----------
Namespace :=
    IdentifierScopePreventingNamingCollisionAndProvidingTermContext;

# ---------- RDF ----------
RDF :=
    GraphDataModelRepresentingStatementsAsTriples;

RDFStatement :=
    Subject
    -> Predicate
    -> Object;

# ---------- RDFS ----------
RDFS MAY_EXPRESS {
    Class,
    SubClassOf,
    Property,
    Domain,
    Range
};

# ---------- OWL ----------
OWL :=
    OntologyLanguageSupportingFormalSemanticDescriptionsAndReasoning;

OWL MAY_EXPRESS {
    ClassEquivalence,
    Disjointness,
    PropertyCharacteristics,
    Restrictions
};

# ---------- SKOS ----------
SKOS :=
    ModelForKnowledgeOrganisationSystems;

SKOS MAY_REPRESENT {
    Concept,
    PreferredLabel,
    AlternativeLabel,
    Broader,
    Narrower,
    Related
};

# ---------- SCHEMA ----------
SemanticSchema :=
    SpecificationOfExpectedSemanticStructure;

Schema MAY_DEFINE {
    EntityTypes,
    Properties,
    Datatypes,
    Cardinality,
    Constraints
};

# ---------- VALIDATION ----------
SemanticValidation :=
    CompareKnowledgeRepresentationAgainstDefinedConstraints;

ValidationResult MAY_BE {
    Conformant,
    Warning,
    Violation
};

Validation != Inference;

# ---------- ANNOTATION ----------
SemanticAnnotation :=
    AttachExplicitSemanticMeaningToDataOrResource;

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

# ---------- ENTITY EXTRACTION ----------
EntityExtraction :=
    InputContent
    -> DetectEntityMentions
    -> AssignCandidateTypes;

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

ResolutionEvidence MAY_INCLUDE {
    Identifier,
    Name,
    Attribute,
    Relationship,
    Context
};

# ---------- DISAMBIGUATION ----------
EntityDisambiguation :=
    AmbiguousMention
    + Context
    -> SelectedCanonicalEntity;

# ---------- COREFERENCE ----------
CoreferenceResolution :=
    DetermineWhichExpressionsReferToSameEntityWithinOrAcrossContent;

# ---------- RELATION EXTRACTION ----------
RelationExtraction :=
    InputContent
    -> IdentifyEntityPairsOrConcepts
    -> IdentifySemanticRelationship;

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

Representation MAY_BE {
    LogicalForm,
    Graph,
    IntentEntityStructure,
    Frame
};

# ---------- ONTOLOGY MAPPING ----------
OntologyMapping :=
    SourceTermOrConcept
    -> TargetOntologyTerm;

Mapping MAY_HAVE Strength {
    Exact,
    Close,
    Broad,
    Narrow,
    Related
};

# ---------- ONTOLOGY ALIGNMENT ----------
OntologyAlignment :=
    DiscoverAndRepresentCorrespondencesBetweenOntologies;

Alignment MAY_SUPPORT InteroperabilityAndIntegration;

# ---------- SCHEMA MATCHING ----------
SchemaMatching :=
    IdentifyCorrespondenceBetweenFieldsPropertiesOrStructuresAcrossDataModels;

SemanticEvidence MAY_IMPROVE MatchingBeyondNameSimilarity;

# ---------- KNOWLEDGE INTEGRATION ----------
SemanticIntegration :=
    MultipleSources
    -> Normalise
    -> ResolveEntities
    -> MapSchemas
    -> PreserveProvenance
    -> MergeOrLinkStatements;

# ---------- IDENTITY CONFLICT ----------
IF TwoRecordsShareSimilarNames
BUT EvidenceInsufficientForIdentity
THEN DoNotForceMerge;

FalseMerge MAY_CREATE WidespreadSemanticError;

# ---------- INFERENCE ----------
SemanticInference :=
    ExistingKnowledge
    + FormalSemanticsOrRules
    -> DerivedKnowledge;

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

# ---------- RULE ----------
Rule :=
    PremiseSet
    -> Conclusion;

Example:
    IF x INSTANCE_OF Dog
    AND Dog SUBCLASS_OF Mammal
    THEN x INSTANCE_OF Mammal;

# ---------- TRANSITIVE PROPERTY ----------
IF PropertyP IS_TRANSITIVE
AND A P B
AND B P C
THEN A P C;

# ---------- SYMMETRIC PROPERTY ----------
IF PropertyP IS_SYMMETRIC
AND A P B
THEN B P A;

# ---------- INVERSE PROPERTY ----------
IF PropertyP INVERSE_OF PropertyQ
AND A P B
THEN B Q A;

# ---------- EQUIVALENCE ----------
EquivalentClass(A,B)
    -> Membership MAY_BE InferredAcrossAAndB;

EquivalentProperty(P,Q)
    -> Assertions MAY_BE InterpretedAcrossPAndQ;

# ---------- DISJOINTNESS ----------
IF ClassA DISJOINT_WITH ClassB
AND EntityX INSTANCE_OF Both
THEN KnowledgeBase MAY_BE Inconsistent;

# ---------- CONSISTENCY ----------
SemanticConsistency :=
    AbsenceOfContradictionUnderSelectedFormalSemantics;

Reasoner MAY_DETECT Inconsistency;

# ---------- OPEN WORLD ----------
OpenWorldAssumption :=
    MissingStatement DOES_NOT ImplyFalseStatement;

SemanticWebReasoning OFTEN_USES OpenWorldAssumption;

# ---------- CLOSED WORLD ----------
ClosedWorldAssumption :=
    ApplicationMayTreatMissingInformationAsFalseOrUnavailableAccordingToDefinedPolicy;

ReasoningAssumption SHOULD_BE Explicit;

# ---------- PROVENANCE ----------
Provenance :=
    OriginAndDerivationInformationForKnowledge;

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

# ---------- CONFIDENCE ----------
StatementConfidence :=
    DegreeOfSupportForAssertion;

Confidence MAY_BE DerivedFrom {
    SourceReliability,
    ExtractionQuality,
    Agreement,
    Context
};

# ---------- CONTEXT ----------
Context :=
    InformationRequiredToInterpretSemanticStatementCorrectly;

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

# ---------- TEMPORAL KNOWLEDGE ----------
TemporalStatement MAY_HAVE {
    ValidFrom,
    ValidTo,
    ObservationTime
};

KnowledgeAtTime(t) MAY_DIFFER_FROM CurrentKnowledge;

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

Query MAY_USE {
    TriplePattern,
    GraphPattern,
    RelationshipPath,
    Inference,
    Filter
};

# ---------- SPARQL ----------
SPARQL :=
    QueryLanguageForRDFData;

SPARQL MAY_SUPPORT {
    SELECT,
    ASK,
    CONSTRUCT,
    DESCRIBE
};

# ---------- GRAPH PATH ----------
RelationshipPath :=
    EntityA
    -> PredicateSequence
    -> EntityB;

GraphTraversal MAY_DISCOVER IndirectSemanticConnections;

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

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

# ---------- SEMANTIC SIMILARITY ----------
SemanticSimilarity :=
    DegreeOfMeaningRelatednessBetweenConceptsEntitiesOrContent;

Similarity MAY_BE DerivedFrom {
    OntologyDistance,
    GraphStructure,
    EmbeddingDistance,
    SharedProperties
};

# ---------- VECTOR RELATIONSHIP ----------
Embedding :=
    NumericVectorRepresentationOfContentOrEntity;

EmbeddingSimilarity MAY_CAPTURE StatisticalSemanticRelatedness;

VectorSimilarity != ExplicitOntologicalRelation;

# ---------- HYBRID SEARCH ----------
HybridSemanticSearch :=
    LexicalScore
    + VectorScore
    + GraphEvidence
    + StructuredConstraints
    -> RankedResult;

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

Embedding MAY_SUPPORT {
    LinkPrediction,
    Similarity,
    Ranking
};

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

LinkedData MAY_CONNECT DistributedDatasets;

# ---------- SEMANTIC INTEROPERABILITY ----------
SemanticInteroperability :=
    SystemsExchangeInformationWhilePreservingSharedMeaning;

Interoperability MAY_REQUIRE {
    SharedOntology,
    VocabularyMapping,
    IdentifierAlignment,
    ContextTranslation
};

# ---------- REASONER ----------
SemanticReasoner :=
    SystemApplyingLogicalOrRuleBasedInference;

Reasoner MAY {
    ClassifyOntology,
    InferTypes,
    InferRelationships,
    DetectInconsistency
};

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

# ---------- VERSIONING ----------
OntologyVersion :=
    IdentifiedStateOfOntologyAtPointInTime;

KnowledgeGraphVersion MAY_PRESERVE ChangeHistory;

# ---------- CONFLICT ----------
ConflictingAssertions MAY_EXISTAcrossSources;

ConflictResolution MAY {
    PreserveBothWithProvenance,
    PreferHigherQualitySource,
    AdjustConfidence,
    FlagHumanReview
};

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

# ---------- LARGE LANGUAGE MODEL ----------
LargeLanguageModel MAY_SUPPORT {
    EntityExtraction,
    RelationExtraction,
    OntologyMapping,
    NaturalLanguageQuery,
    Explanation
};

LLMGeneratedSemanticKnowledge SHOULD_BE ValidatedAccordingToRiskAndUse;

# ---------- RAG ----------
SemanticRetrievalAugmentedGeneration :=
    Query
    -> SemanticRetrieval
    -> RetrieveKnowledge
    -> ConstructContext
    -> GenerateResponse;

SemanticRAG MAY_USE KnowledgeGraphAndVectorDatabaseTogether;

# ---------- EXPLAINABILITY ----------
SemanticReasoningPath :=
    SourceStatements
    -> AppliedRulesOrRelationships
    -> DerivedConclusion;

ReasoningPath MAY_SUPPORT ExplainableAI;

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

# ---------- SECURITY ----------
SemanticComputingSecurity MAY_INCLUDE {
    Authentication,
    Authorisation,
    GraphAccessControl,
    QueryControl,
    ProvenanceIntegrity
};

# ---------- PRIVACY ----------
SemanticGraph MAY_REVEAL SensitiveRelationshipsEvenWhenIndividualFactsAppearBenign;

PrivacyPolicy MAY_APPLY_TO {
    Entity,
    Property,
    Statement,
    Graph
};

# ---------- SEMANTIC REASONING ----------
IF InformationHasExplicitEntitiesConceptsAndTypedRelationships
THEN SemanticComputingClassification := supported;

IF EntityAAndEntityBHaveSameVerifiedCanonicalIdentity
THEN They MAY_BE UnifiedAccordingToIntegrationPolicy;

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

IF PropertyP IS_TRANSITIVE
AND A P B
AND B P C
THEN A P C;

IF TwoAssertionsConflict
THEN PreserveProvenance
AND ReevaluateConfidence;

IF QueryConceptMatchesRelevantEntityWithoutExactKeyword
THEN SemanticRetrieval MAY_RETURN Entity;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "semantic computing" ~= "meaning-aware computing";
    "semantic processing" ~= "meaning-aware information processing";
    "knowledge graph" ~= "semantic knowledge graph" WHEN typed_relations_present;
    "ontology mapping" ~= "semantic mapping" WHEN mapping_ontology_terms;
    "entity resolution" ~= "identity resolution";
    "semantic inference" ~= "semantic reasoning";
    "semantic search" ~= "meaning-aware search";
    "semantic interoperability" ~= "meaning-preserving interoperability";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_ENTITIES(q) ->
    IDENTIFY_CONCEPTS(q) ->
    RESOLVE_IDENTITIES(q) ->
    IDENTIFY_RELATIONSHIPS(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_CONTEXT(q) ->
    VALIDATE_SEMANTICS(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETRIEVE_RELEVANT_KNOWLEDGE(q) ->
    RETURN_RESULT;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        semantic_consistency,
        identity_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;
    ontology: SemanticComputingOntology;
    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;
}

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

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

    new_ontology_term:
        validate_definition
        -> identify_hierarchy
        -> identify_mappings
        -> integrate;

    semantic_revision:
        preserve_previous_version
        -> update_statements
        -> recompute_inferences
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + SemanticEntity
    + KnowledgeStructure
    + SemanticOperation
    + EvidenceEntity
    + KnowledgeGraph
    + Ontology
    + SemanticInference
    + SemanticSearch
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
SemanticComputingOntology
    -> IdentifySemanticEntities
    -> ResolveCanonicalIdentity
    -> ClassifyConceptsAndInstances
    -> ExtractTypedRelationships
    -> MapToOntologyAndVocabulary
    -> ConstructKnowledgeGraph
    -> ValidateSemanticConstraints
    -> ApplyRulesAndInference
    -> ExecuteMeaningAwareQueries
    -> PreserveContextProvenanceAndConfidence
    -> IntegrateHeterogeneousKnowledge
    -> ExplainDerivedRelationships
    -> MaintainMachineInterpretableSemanticKnowledge;

END FLOW