ProtoScript’s seamless integration with C# is a powerful feature that allows developers to combine the dynamic, graph-based capabilities of ProtoScript with the robust, statically-typed ecosystem of C#. By leveraging native types, runtime calls, and automatic type conversions, ProtoScript enables developers to use existing C# libraries, tools, and frameworks within its ontology framework, creating hybrid applications that blend symbolic reasoning with traditional programming. This section explores how ProtoScript integrates with C#, detailing the mechanisms of type mapping, runtime interoperability, and practical use cases. With a focus on clarity for C# developers, we’ll use analogies, verified examples, and step-by-step explanations to illustrate how this integration enhances ProtoScript’s flexibility and power, setting it apart from traditional ontology systems.
Integration with C# is critical for:
System.String, System.Collections) to enhance ProtoScript’s functionality without reinventing the wheel.Significance in ProtoScript’s Ontology:
Traditional ontologies (e.g., OWL, RDF) are isolated systems, requiring external tools or APIs for integration with programming languages, often involving complex mappings. ProtoScript’s C# integration offers:
For C# developers:
For JavaScript developers:
For database developers:
ProtoScript integrates with C# through three primary mechanisms:
string, int) and .NET types (e.g., System.String, System.Collections.Generic.List<T>) as NativeValuePrototypes or Prototypes, ensuring compatibility.String.Format), with the runtime handling parameter passing and return values.ProtoScript supports C# types in two forms:
string, bool, int, double) represent raw values, translated to NativeValuePrototypes by the runtime.System.String, System.Boolean, System.Int32) represent structured nodes with metadata, used for explicit Prototype integration.Collection map to System.Collections.Generic.List<T>, enabling C# collection operations.C# Analogy: Like using string for simple values and String for method access in C#, but both are graph nodes in ProtoScript.
ProtoScript functions can call C# methods directly, with the runtime managing:
string["hello"]) to C# types (e.g., string).System.String methods, System.Math) without explicit imports.C# Analogy: Like calling a static method (String.Format) from a C# class, but integrated into ProtoScript’s graph runtime.
The runtime handles bidirectional conversions:
string["hello"]) become C# values (e.g., string).List<string>) map to ProtoScript Collections or Prototypes.C# Analogy: Like JSON serialization/deserialization in C#, but for graph nodes with automatic type mapping.
Scenario: Format a City Prototype’s name using String.Format.
Prototype Definition:
prototype City {
string Name = "";
function FormatName() : string {
return String.Format("City: {0}", Name);
}
}
prototype Buffalo_City : City {
Name = "Buffalo";
}
Application
string result = Buffalo_City.FormatName();
What’s Happening?
FormatName calls C#’s String.Format, passing the Name property ("Buffalo").string["Buffalo"] to a C# string, processes the call, and returns string["City: Buffalo"].Buffalo_City links to a string["Buffalo"] node, with the function creating a new string node for the result.Beyond Ontologies: Unlike OWL’s isolation, ProtoScript directly uses C# libraries, simplifying string processing within the ontology.
Scenario: Count cities in a State using C#’s List<T> methods.
Prototype Definition:
prototype State {
string Name = "";
Collection Cities = new Collection();
function GetCityCount() : int {
return Cities.Count; // Maps to List<T>.Count
}
}
prototype City {
string Name = "";
}
prototype NewYork_State : State {
Name = "New York";
}
prototype NewYork_City : City {
Name = "New York City";
}
NewYork_State.Cities.Add(NewYork_City);
Application:
int count = NewYork_State.GetCityCount();
What’s Happening?
GetCityCount accesses the Count property of Cities, a Collection mapped to System.Collections.Generic.List<T>.1 as an int NativeValuePrototype.NewYork_State links to a Collection node with an edge to NewYork_City, with Count traversing this edge.Beyond Ontologies: ProtoScript’s native access to C# collections avoids the need for custom ontology query languages like SPARQL.
Scenario: Transform a C# List<string> to a ProtoScript Collection for ontology processing.
C# Code (assumed external):
List<string> csharpCities = new List<string> { "Buffalo", "Albany" };
ProtoScript Transformation:
prototype CityList {
Collection CityNames = new Collection();
function AddCitiesFromCSharp(List<string> csharpList) : CityList {
foreach (string city in csharpList) {
CityNames.Add(city);
}
return this;
}
}
prototype CityList_Example : CityList {
CityNames = new Collection();
}
CityList_Example.AddCitiesFromCSharp(csharpCities);
What’s Happening?
AddCitiesFromCSharp takes a C# List<string>, iterates using ProtoScript’s foreach, and adds each string to CityNames.string values to string NativeValuePrototypes (e.g., string["Buffalo"]). CityList_Example links to a Collection with edges to string["Buffalo"] and string["Albany"].Beyond Ontologies: ProtoScript’s type conversions enable direct data import, unlike OWL’s need for external ETL processes.
Scenario: Transform an NL query to a C# method call using a Transformation Function.
Input Prototype:
prototype Query {
string Question = "";
}
prototype Springfield_Query : Query {
Question = "Who lives in Springfield?";
}
Transformation Function:
prototype Person {
string Name = "";
Location Location = new Location();
}
prototype Location {
string Name = "";
}
[TransferFunction(NL)]
function Query_To_CSharpMethod(Query query) : string {
if (query.Question == "Who lives in Springfield?") {
Collection names = new Collection();
foreach (Person p in AllPersons) {
if (p.Location.Name == "Simpsons House") {
names.Add(p.Name);
}
}
return String.Join(", ", names.ToArray());
}
return "";
}
Application:
string result = UnderstandUtil.TransferToSememesWithDimension(Springfield_Query, NL, _interpreter);
What’s Happening?
Springfield_Query to a C# string, using String.Join to format a list of names.Collection to a C# array for String.Join, returning a string NativeValuePrototype (e.g., "Homer Simpson, Marge Simpson").Springfield_Query links to a string node with the result, derived from Person nodes.Beyond Ontologies: Combines NL processing with C# method calls, unifying domains without external tools.
Internal Mechanics
The ProtoScript runtime manages C# integration:
string to string, Collection to List<T>).C# integration:
Beyond Gradient Descent: Integration provides a practical bridge to traditional programming, complementing ProtoScript’s unsupervised learning with C#’s deterministic capabilities.
C# integration empowers ProtoScript to blend graph-based ontology reasoning with the robust capabilities of C#, creating a powerful platform for hybrid applications. In the next section, we’ll explore Hidden Context Prototypes (HCPs) (Advanced Topic), which abstract Prototype Paths into standalone entities, streamlining categorization and transformation. You’re now ready to integrate ProtoScript with C# for dynamic, cross-domain solutions!
Hidden Context Prototypes (HCPs) are a sophisticated feature in ProtoScript, representing standalone Prototypes that abstract the divergent subgraphs identified by Prototype Paths into a compact, reusable form. By encapsulating these differences, HCPs streamline categorization, transformation, and reasoning within ProtoScript’s graph-based ontology, enhancing the unsupervised learning framework established by Shadows, Paths, Subtypes, and Transformation Functions. HCPs “hide” the full graph context of a Prototype, focusing only on its unique characteristics relative to a Shadow, making them a powerful tool for efficient data representation and cross-domain applications. This section explains HCPs, their syntax, mechanics, and significance, using clear analogies, detailed examples, and practical use cases to ensure developers familiar with C# or JavaScript can grasp their role in ProtoScript’s advanced ontology capabilities.
HCPs are essential for:
Significance in ProtoScript’s Ontology:
Traditional ontologies (e.g., OWL, RDF) rely on static class instances and property assertions, with no mechanism to abstract instance-specific differences dynamically. HCPs offer:
For C# developers:
For JavaScript developers:
For database developers:
A Hidden Context Prototype (HCP) is a standalone Prototype that encapsulates the divergent subgraphs (Paths) identified when parameterizing a Prototype against a Shadow. HCPs “hide” the full graph context of the original Prototype, focusing only on the properties or substructures that differ from the Shadow’s generalized form. They are:
0, .1).typeof relationships.HCPs are generated by the ProtoScript runtime after Parameterization:
0, .1) in a new Prototype with a unique identifier.typeof relationship between the original Prototype and the HCP, indicating full representation.Syntax (Conceptual, generated by runtime):
prototype UniqueIdentifier {
Type0 .0 = Value0;
Type1 .1 = Value1;
// Additional properties as needed
}
C# Analogy: Like creating a new class with only the properties that differ from a base class, but dynamically generated and stored as a graph node.
Scenario: Create an HCP for int i = 0 using its Shadow and Paths.
Shadow (from int i = 0 and int j = -1):
prototype InitializedIntVariable : CSharp_VariableDeclaration {
Type.TypeName = "int";
Type.IsNullable = false;
VariableName = "";
Initializer = new CSharp_Expression();
}
Paths (from Parameterization):
prototype CSharp_VariableDeclaration {
CSharp_Type Type = new CSharp_Type();
string VariableName = "";
CSharp_Expression Initializer = new CSharp_Expression();
}
prototype CSharp_Type {
string TypeName = "";
bool IsNullable = false;
}
prototype CSharp_Expression {
string Value = "";
}
prototype Int_Declaration_I : CSharp_VariableDeclaration {
Type.TypeName = "int";
VariableName = "i";
Initializer = IntegerLiteral_0;
}
prototype IntegerLiteral_0 : CSharp_Expression {
Value = "0";
}
HCP Creation:
prototype TestNamespace_5E0C7570A1C2276F8E9ECEE135323F87 {
string .0 = "i";
CSharp_Expression .1 = IntegerLiteral_0;
}
What’s Happening?
.0 (VariableName) and .1 (Initializer).TestNamespace_5E0C7570A1C2276F8E9ECEE135323F87 is a unique identifier generated by the runtime.Int_Declaration_I typeof TestNamespace_5E0C7570A1C2276F8E9ECEE135323F87 indicates the HCP fully represents Int_Declaration_I. Int_Declaration_I via typeof.Beyond Ontologies: HCPs provide a compact, dynamic representation of instance differences, unlike OWL’s verbose instance data.
Scenario: Create an HCP for Homer using the SimpsonsHouseParent Shadow.
Shadow:
prototype SimpsonsHouseParent : Person {
Name = "";
Gender = "";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
Spouse = new Person();
Age = 0;
}
Paths (from Parameterization):
Path 1: Homer.Name = Compare.Entity
// Result: string["Homer Simpson"]
Path 2: Homer.Gender = Compare.Entity
// Result: string["Male"]
Path 3: Homer.Spouse = Compare.Entity
// Result: Marge
Path 4: Homer.Age = Compare.Entity
// Result: int[39]
Input Prototype:
prototype Person {
string Name = "";
string Gender = "";
Location Location = new Location();
Collection ParentOf = new Collection();
Person Spouse = new Person();
int Age = 0;
}
prototype Location {
string Name = "";
}
prototype SimpsonsHouse : Location {
Name = "Simpsons House";
}
prototype Homer : Person {
Name = "Homer Simpson";
Gender = "Male";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
Spouse = Marge;
Age = 39;
}
prototype Marge : Person {
Name = "Marge Simpson";
}
HCP Creation:
prototype TestNamespace_8A3B2C1D4E5F6G7H8I9J0K1L2 {
string .0 = "Homer Simpson";
string .1 = "Male";
Person .2 = Marge;
int .3 = 39;
}
What’s Happening?
.0 (Name), .1 (Gender), .2 (Spouse), and .3 (Age).Homer typeof TestNamespace_8A3B2C1D4E5F6G7H8I9J0K1L2 links the HCP to Homer."Homer Simpson", "Male", Marge, and 39, with a typeof edge to Homer.Age).Beyond Ontologies: HCPs compactly represent instance-specific data, streamlining reasoning compared to OWL’s complex instance graphs.
Scenario: Use an HCP to transform a C# variable to a database column.
Shadow (IntDataElement):
prototype IntDataElement {
string Name = "";
string Type = "int";
}
Paths (for Int_Declaration_I):
Path 1: Int_Declaration_I.VariableName = Compare.Entity
// Result: string["i"]
Path 2: Int_Declaration_I.Initializer = Compare.Entity
// Result: IntegerLiteral_0 { Value = "0" }
HCP Creation:
prototype TestNamespace_9B4C7D2E8F1G3H5I6J8K0L {
string .0 = "i";
CSharp_Expression .1 = IntegerLiteral_0;
}
Transformation:
prototype Database_Column {
string ColumnName = "";
string DataType = "";
}
function Variable_To_Column(TestNamespace_9B4C7D2E8F1G3H5I6J8K0L hcp) : Database_Column {
Database_Column col = new Database_Column();
col.ColumnName = hcp..0; // Access .0 for VariableName
col.DataType = "int"; // From Shadow
return col;
}
What’s Happening?
VariableName and Initializer for Int_Declaration_I.ColumnName, producing Database_Column { ColumnName = "i", DataType = "int" }."i" and IntegerLiteral_0, enabling the transformation to a new Database_Column node.Beyond Ontologies: HCPs enable efficient transformations, unlike OWL’s need for verbose SPARQL queries or external tools.
HCPs build on prior learning mechanisms:
InitializedIntVariable Shadow and Paths to streamline database transformation.The ProtoScript runtime manages HCPs:
typeof relationships between the HCP and original Prototype.HCPs:
Beyond Gradient Descent: HCPs offer a deterministic, scalable alternative, leveraging graph structures for efficiency and clarity.
HCPs enhance ProtoScript’s ontology by abstracting instance-specific data into compact, reusable Prototypes, streamlining its unsupervised learning framework. In the next section, we’ll explore Constrained and Underconstrained Relationships (Advanced Topic), examining how Transformation Functions handle one-to-one and one-to-many mappings. You’re now ready to use HCPs for efficient, dynamic reasoning in ProtoScript!
The hierarchical tree structure in ProtoScript is a fundamental organizational framework that arranges Prototypes into a rooted tree, resembling classification trees or taxonomies in knowledge representation. This structure emerges dynamically from ProtoScript’s graph-based ontology, leveraging Shadows, Prototype Paths, Subtypes, and Transformation Functions to categorize Prototypes hierarchically based on their properties and relationships. Unlike traditional ontologies, which rely on static, predefined hierarchies, ProtoScript’s tree is adaptive, evolving through unsupervised learning to reflect the data’s inherent structure. This section explores the hierarchical tree structure, its mechanics, and its significance, using clear analogies, detailed examples, and practical use cases to ensure developers familiar with C# or JavaScript can understand its role in ProtoScript’s advanced reasoning capabilities.
The hierarchical tree structure is critical for:
Significance in ProtoScript’s Ontology:
Traditional ontologies (e.g., OWL, RDF) use fixed class hierarchies and axioms, requiring manual design and external inference engines for reasoning. ProtoScript’s hierarchical tree structure offers:
For C# developers:
GroupBy operation that organizes objects into a nested structure based on shared properties.For JavaScript developers:
For database developers:
The hierarchical tree structure in ProtoScript is a rooted tree where:
isa links, connecting more specific nodes to their generalizations.Entity, Variable), with branches leading to increasingly specific subtypes (e.g., Person, InitializedIntVariable).Homer, int i = 0).This tree emerges from ProtoScript’s unsupervised learning:
InitializedIntVariable) as internal nodes.The tree is constructed dynamically by the runtime:
SimpsonsHouseParent).InitializedIntVariable_SubType).typeof.isa edges.Syntax (Conceptual, managed by runtime):
prototype declarations.isa relationships (via inheritance or UnderstandUtil.SubType) form edges.typeof and -> operators query the tree.C# Analogy: Like dynamically building a class hierarchy by comparing objects and grouping them by shared properties, but stored as a graph tree.
Scenario: Build a tree for C# variable declarations, focusing on the “initialized variable” branch.
Tree Structure:
a_variable
├── an_initialized_variable
│ ├── an_initialized_string_variable
│ │ ├── string s1 = ""
│ │ ├── string s2 = "hello world"
│ │ ├── string strData = string.Empty
│ │ ├── string message = "Greetings"
│ │ └── string name = "Alice"
│ └── an_initialized_integer_variable
│ ├── int i = 0
│ ├── int j = -1
│ ├── int counter = 42
│ ├── int x = 100
│ └── int y = -10
└── an_uninitialized_variable
Prototypes:
prototype Variable {
string Name = "";
string Type = "";
}
prototype Initialized_Variable : Variable {
CSharp_Expression Initializer = new CSharp_Expression();
}
prototype CSharp_Expression {
string Value = "";
}
prototype Initialized_Int_Variable : Initialized_Variable {
Type = "int";
}
prototype Int_Declaration_I : Initialized_Int_Variable {
Name = "i";
Initializer = IntegerLiteral_0;
}
prototype IntegerLiteral_0 : CSharp_Expression {
Value = "0";
}
prototype Int_Declaration_J : Initialized_Int_Variable {
Name = "j";
Initializer = UnaryExpression_Minus1;
}
prototype UnaryExpression_Minus1 : CSharp_Expression {
string Operator = "-";
string Value = "1";
}
Shadow and Subtype:
Initialized_Int_Variable (from LGG of Int_Declaration_I and Int_Declaration_J).[SubType]
prototype Initialized_Int_Variable_SubType : Initialized_Variable {
function IsCategorized(Initialized_Variable var) : bool {
return var -> Initialized_Variable {
this.Type == "int" &&
this.Initializer != new CSharp_Expression()
};
}
}
HCP (for Int_Declaration_I):
prototype TestNamespace_5E0C7570A1C2276F8E9ECEE135323F87 {
string .0 = "i";
CSharp_Expression .1 = IntegerLiteral_0;
}
What’s Happening?
a_variable, branching to an_initialized_variable (Shadow).an_initialized_integer_variable (Shadow/Subtype) branches further, with leaves Int_Declaration_I and Int_Declaration_J (instances/HCPs).Int_Declaration_I isa Initialized_Int_Variable), with HCPs as leaf abstractions.Beyond Ontologies: Unlike OWL’s static taxonomies, ProtoScript’s tree evolves dynamically, reflecting data patterns without manual design.
Scenario: Build a tree for Simpsons characters, focusing on “parents in the Simpsons’ house.”
Tree Structure:
entity
├── person
│ ├── simpsons_house_resident
│ │ ├── simpsons_house_parent
│ │ │ ├── Homer
│ │ │ └── Marge
│ │ ├── Bart
│ │ ├── Lisa
│ │ └── Maggie
│ └── non_resident
└── location
├── SimpsonsHouse
└── FlandersHouse
Prototypes:
prototype Entity {
string Name = "";
}
prototype Person : Entity {
string Gender = "";
Location Location = new Location();
Collection ParentOf = new Collection();
}
prototype Location : Entity {
string Name = "";
}
prototype SimpsonsHouse : Location {
Name = "Simpsons House";
}
prototype SimpsonsHouse_Resident : Person {
Location = SimpsonsHouse;
}
prototype SimpsonsHouse_Parent : SimpsonsHouse_Resident {
ParentOf = [Bart, Lisa, Maggie];
}
prototype Homer : SimpsonsHouse_Parent {
Name = "Homer Simpson";
Gender = "Male";
}
prototype Marge : SimpsonsHouse_Parent {
Name = "Marge Simpson";
Gender = "Female";
}
Subtype:
[SubType]
prototype SimpsonsHouse_Parent_SubType : Person {
function IsCategorized(Person person) : bool {
return person -> Person {
this.Location.Name == "Simpsons House" &&
this.ParentOf.Count > 0
};
}
}
HCP (for Homer):
prototype TestNamespace_8A3B2C1D4E5F6G7H8I9J0K1L2 {
string .0 = "Homer Simpson";
string .1 = "Male";
}
What’s Happening?
entity, branching to person and simpsons_house_resident (Shadows).simpsons_house_parent (Shadow/Subtype) branches to Homer and Marge (instances/HCPs).Homer isa SimpsonsHouse_Parent isa SimpsonsHouse_Resident, with the HCP linked via typeof.Beyond Ontologies: The tree’s dynamic construction adapts to new characters, unlike OWL’s fixed class structures.
Scenario: Build a tree unifying C# variables and database columns.
Tree Structure:
data_element
├── int_data_element
│ ├── initialized_int_variable
│ │ ├── int i = 0
│ │ └── int j = -1
│ └── int_column
│ ├── ID
│ └── Counter
Prototypes:
prototype DataElement {
string Name = "";
string Type = "";
}
prototype Int_DataElement : DataElement {
Type = "int";
}
prototype Initialized_Int_Variable : Int_DataElement {
CSharp_Expression Initializer = new CSharp_Expression();
}
prototype Int_Column : Int_DataElement {
}
prototype Int_Declaration_I : Initialized_Int_Variable {
Name = "i";
Initializer = IntegerLiteral_0;
}
prototype ID_Column : Int_Column {
Name = "ID";
}
Shadow and Subtype:
[SubType]
prototype Int_DataElement_SubType : DataElement {
function IsCategorized(DataElement elem) : bool {
return elem -> DataElement {
this.Type == "int"
};
}
}
HCP (for Int_Declaration_I):
prototype TestNamespace_9B4C7D2E8F1G3H5I6J8K0L {
string .0 = "i";
CSharp_Expression .1 = IntegerLiteral_0;
}
What’s Happening?
data_element into int_data_element (Shadow), branching to initialized_int_variable and int_column (Subtypes).Int_Declaration_I and ID_Column are leaves (instances/HCPs).Beyond Ontologies: The tree dynamically unifies code and database domains, unlike OWL’s separate taxonomies.
The ProtoScript runtime manages the tree:
isa edges.typeof and -> to query the tree, navigating from root to leaves.Scalability: Pruning trivial branches and clustering similar Shadows ensure efficiency, supporting large ontologies.
The tree:
Beyond Gradient Descent: The tree’s dynamic construction offers a deterministic, scalable alternative, leveraging graph structures for clarity.
The hierarchical tree structure empowers ProtoScript’s ontology with a dynamic, scalable taxonomy, enhancing its unsupervised learning and reasoning capabilities. In the next section, we’ll explore Constrained and Underconstrained Relationships (Advanced Topic), examining how Transformation Functions handle one-to-one and one-to-many mappings. You’re now ready to navigate and query ProtoScript’s hierarchical ontology!
The Dual-Channel Clustering Hypothesis proposes a groundbreaking enhancement to ProtoScript’s unsupervised learning framework, introducing a secondary channel—such as narrative context—to complement the primary channel of structural properties. By incorporating feedback between these channels, ProtoScript aims to mimic human-like learning efficiency, reducing the need for exhaustive data comparisons and enabling more natural, intuitive categorization. This advanced topic builds on ProtoScript’s core mechanisms (Shadows, Paths, Subtypes, Transformation Functions, HCPs, and the hierarchical tree) to address the limitations of single-channel clustering, offering a potential leap in ontology-based reasoning compared to traditional systems and neural network approaches like gradient descent. This section explores the hypothesis, its mechanics, and its implications, using clear analogies, illustrative examples, and practical insights to ensure developers familiar with C# or JavaScript can grasp its transformative potential.
The Dual-Channel Clustering Hypothesis is critical for:
Significance in ProtoScript’s Ontology:
Traditional ontologies (e.g., OWL, RDF) rely on static hierarchies and axioms, with no mechanism for multi-channel learning. Neural networks, using gradient descent, depend on large datasets and lack interpretability. The Dual-Channel Clustering Hypothesis offers:
For C# developers:
For JavaScript developers:
For database developers:
The Dual-Channel Clustering Hypothesis posits that ProtoScript’s unsupervised learning, currently based on a single channel (structural properties via Shadows and Paths), can be significantly enhanced by adding a secondary channel (e.g., narrative or semantic context). Feedback between these channels guides clustering, prioritizing meaningful categories and reducing the combinatorial burden of pairwise comparisons. The hypothesis aims to:
ProtoScript’s current clustering:
Gender, Location, ParentOf in the Simpsons ontology).SimpsonsHouseParent) and Paths to refine categorization.Gender alone), leading to combinatorial growth (e.g., (\binom{n}{2}) comparisons for (n) Prototypes).The hypothesis introduces:
The proposed dual-channel clustering process:
Location = SimpsonsHouse).Role = "FamilyMember"). FamilyMember clusters).Syntax (Conceptual, runtime-driven):
Narrative.Role).C# Analogy: Like a machine learning model that combines feature data (structural) with user feedback (contextual) to refine clusters, but using graph-based reasoning instead of statistical methods.
Scenario: Enhance clustering of Simpsons characters using a narrative channel.
Single-Channel Clustering (Current):
prototype Person {
string Name = "";
string Gender = "";
Location Location = new Location();
Collection ParentOf = new Collection();
}
prototype Location {
string Name = "";
}
prototype SimpsonsHouse : Location {
Name = "Simpsons House";
}
prototype Homer : Person {
Name = "Homer Simpson";
Gender = "Male";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
}
prototype Marge : Person {
Name = "Marge Simpson";
Gender = "Female";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
}
prototype Ned : Person {
Name = "Ned Flanders";
Gender = "Male";
Location = FlandersHouse;
ParentOf = [Rod, Todd];
}
Shadows (Structural):
SimpsonsHouseParent: Location = SimpsonsHouse, ParentOf = [Bart, Lisa, Maggie] (Homer, Marge).SimpsonsMale: Gender = "Male" (Homer, Ned).Limitation: Generates trivial Shadows (e.g., SimpsonsMale), requiring many comparisons ((\binom{n}{2}) for (n) Prototypes).
Dual-Channel Clustering (Proposed):
prototype Person {
string Name = "";
string Gender = "";
Location Location = new Location();
Collection ParentOf = new Collection();
string Narrative_Role = "";
}
prototype Homer : Person {
Name = "Homer Simpson";
Gender = "Male";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
Narrative_Role = "FamilyMember";
}
prototype Marge : Person {
Name = "Marge Simpson";
Gender = "Female";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
Narrative_Role = "FamilyMember";
}
prototype Ned : Person {
Name = "Ned Flanders";
Gender = "Male";
Location = FlandersHouse;
ParentOf = [Rod, Todd];
Narrative_Role = "Neighbor";
}
Clustering with Feedback:
impsonsHouseParent, SimpsonsMale). Narrative_Role: SimpsonsHouseParent: High score (both Homer and Marge are "FamilyMember").SimpsonsMale: Low score (mixed roles: Homer = "FamilyMember", Ned = "Neighbor").SimpsonsHouseParent, demote SimpsonsMale.entity
├── person
│ ├── simpsons_house_resident
│ │ ├── simpsons_house_parent
│ │ │ ├── Homer
│ │ │ └── Marge
│ └── neighbor
│ └── Ned
What’s Happening?
SimpsonsHouseParent due to shared "FamilyMember" roles, reducing trivial Shadows like SimpsonsMale.Homer and Marge link to SimpsonsHouseParent via isa, with Ned under a separate Neighbor branch.Beyond Ontologies and Neural Networks: Dual-channel clustering mimics human learning, outperforming OWL’s static hierarchies and neural networks’ data-heavy approaches.
Scenario: Cluster C# variables and database columns using a semantic channel.
Single-Channel Clustering:
prototype DataElement {
string Name = "";
string Type = "";
}
prototype Initialized_Int_Variable : DataElement {
CSharp_Expression Initializer = new CSharp_Expression();
}
prototype Int_Column : DataElement {
}
prototype Int_Declaration_I : Initialized_Int_Variable {
Name = "i";
Type = "int";
Initializer = IntegerLiteral_0;
}
prototype ID_Column : Int_Column {
Name = "ID";
Type = "int";
}
Shadows:
Int_DataElement: Type = "int" (both).Initialized_Data: Has Initializer (only Int_Declaration_I).Dual-Channel Clustering:
prototype DataElement {
string Name = "";
string Type = "";
string Semantic_Role = "";
}
prototype Int_Declaration_I : DataElement {
Name = "i";
Type = "int";
Semantic_Role = "Variable";
}
prototype ID_Column : DataElement {
Name = "ID";
Type = "int";
Semantic_Role = "PrimaryKey";
}
Clustering with Feedback:
Int_DataElement Shadow.Semantic_Role: "Variable", "PrimaryKey").Variable_Data, PrimaryKey_Data): High scores for role-specific clusters.Int_DataElement.data_element
├── variable_data
│ └── int i = 0
├── primary_key_data
│ └── ID
What’s Happening?
Int_Declaration_I links to Variable_Data, ID_Column to PrimaryKey_Data.Beyond Ontologies and Neural Networks: Dual-channel clustering ensures relevant categories, unlike OWL’s static taxonomies or neural networks’ data-intensive training.
The ProtoScript runtime would manage dual-channel clustering:
Narrative_Role), scoring Shadows based on alignment.The hypothesis:
Beyond Gradient Descent: Offers a symbolic, feedback-driven alternative, leveraging graph structures for interpretability and efficiency.
The Dual-Channel Clustering Hypothesis positions ProtoScript as a pioneer in symbolic AI, enhancing its unsupervised learning with human-like efficiency. In the next section, we’ll explore Constrained and Underconstrained Relationships (Advanced Topic), examining how Transformation Functions handle one-to-one and one-to-many mappings. You’re now ready to envision ProtoScript’s future with dual-channel clustering!
Constrained and underconstrained relationships in ProtoScript define how Transformation Functions map input Prototypes to outputs, addressing the precision and ambiguity inherent in cross-domain transformations. These relationships are critical for ensuring accurate mappings in ProtoScript’s graph-based ontology, particularly when transforming data across domains like natural language (NL), C# code, or database schemas. Unlike traditional ontologies, which rely on rigid mappings, ProtoScript’s approach allows dynamic resolution of ambiguities, enhancing its unsupervised learning framework. This section explores constrained (one-to-one) and underconstrained (one-to-many) relationships, their mechanics, and strategies for handling ambiguity, using clear analogies and examples to guide developers familiar with C# or JavaScript.
These relationships are essential for:
true), while underconstrained relationships handle ambiguity (e.g., NL “integer” to int, long).Significance in ProtoScript’s Ontology:
Traditional ontologies (e.g., OWL, RDF) use fixed property mappings, requiring manual alignment for cross-domain tasks. ProtoScript’s approach offers:
For C# developers:
For JavaScript developers:
For database developers:
bool true).int, long, short, uint).These relationships are defined within Transformation Functions, which use graph traversals to resolve mappings.
bool Flag = true in NL maps to bool Flag = true in C#.int for “integer”). int, long, etc., resolved by context.Syntax (within Transformation Functions):
[TransferFunction(Dimension)]
function FunctionName(InputType input) : OutputType {
// Map properties, handling constrained/underconstrained cases
}
C# Analogy: Like a switch statement for constrained mappings and a dictionary lookup with fallback logic for underconstrained mappings.
Scenario: Transform NL “declare a boolean variable b1 with a value of true” to C# bool b1 = true.
Transformation Function:
prototype NL_DeclareVariable {
string Type = "";
string Name = "";
string Value = "";
}
prototype CSharp_VariableDeclaration {
string Type = "";
string VariableName = "";
string Initializer = "";
}
[TransferFunction(NL)]
function NL_To_CSharp(NL_DeclareVariable nl) : CSharp_VariableDeclaration {
CSharp_VariableDeclaration cs = new CSharp_VariableDeclaration();
cs.VariableName = nl.Name;
if (nl.Type == "boolean") {
cs.Type = "bool";
cs.Initializer = nl.Value; // Constrained: "true" maps to "true"
}
return cs;
}
Application:
prototype Bool_Declaration : NL_DeclareVariable {
Type = "boolean";
Name = "b1";
Value = "true";
}
Collection result = UnderstandUtil.TransferToSememesWithDimension(Bool_Declaration, NL, _interpreter);
What’s Happening?
Type = "boolean" to "bool" and Value = "true" to "true", a constrained one-to-one relationship.Bool_Declaration links to a CSharp_VariableDeclaration node with "bool", "b1", and "true".Beyond Ontologies: Unlike OWL’s static mappings, ProtoScript’s constrained relationships are runtime-driven, ensuring accuracy.
Scenario: Transform NL “declare an integer variable x1 with a value of 0” to C# (ambiguous: int, long, etc.).
Transformation Function:
[TransferFunction(NL)]
function NL_To_CSharp(NL_DeclareVariable nl) : CSharp_VariableDeclaration {
CSharp_VariableDeclaration cs = new CSharp_VariableDeclaration();
cs.VariableName = nl.Name;
cs.Initializer = nl.Value;
if (nl.Type == "integer") {
cs.Type = "int"; // Default choice for underconstrained mapping
}
return cs;
}
Application:
Type = "integer";
Name = "x1";
Value = "0";
}
Collection result = UnderstandUtil.TransferToSememesWithDimension(Int_Declaration, NL, _interpreter);
What’s Happening?
Type = "integer" is underconstrained, potentially mapping to int, long, short, or uint."int", resolving ambiguity. Int_Declaration links to a CSharp_VariableDeclaration node with "int", "x1", and "0".Beyond Ontologies: ProtoScript dynamically resolves ambiguities, unlike OWL’s need for explicit mappings.
The runtime manages these relationships:
They:
Beyond Gradient Descent: These relationships offer a deterministic, graph-based alternative, ensuring clarity and scalability.
ProtoScript’s graph-based ontology is deeply rooted in graph theory and computer science, connecting its operations (e.g., Shadows, Paths, Subtypes, Transformation Functions, HCPs, hierarchical trees) to established concepts like subgraph isomorphism, graph differencing, and hierarchical clustering. These connections provide a theoretical foundation for ProtoScript’s unsupervised learning and reasoning, distinguishing it from traditional ontologies and neural network approaches. This section explores how ProtoScript aligns with graph theory and computer science, detailing key connections, their implications, and unique aspects of ProtoScript’s approach, using analogies and examples to guide developers familiar with C# or JavaScript.
These connections are critical for:
Significance in ProtoScript’s Ontology:
Traditional ontologies lack dynamic graph operations, relying on static axioms. Neural networks use statistical methods, not graph-based reasoning. ProtoScript offers:
For C# developers:
For JavaScript developers:
For database developers:
int i = 0 and int j = -1 create int _ = _, like intersecting their property graphs. int i = 0 extract VariableName = "i", like diffing graphs.SimpsonsHouseParent_SubType checks if Homer’s graph matches the subtype’s pattern.Homer projects Name, Gender into a compact node, like extracting key features.entity → person → simpsons_house_parent) mirrors a decision tree.Scenario: Map Simpsons clustering to graph theory concepts.
Prototypes:
prototype Person {
string Name = "";
string Gender = "";
Location Location = new Location();
Collection ParentOf = new Collection();
}
prototype Location {
string Name = "";
}
prototype SimpsonsHouse : Location {
Name = "Simpsons House";
}
prototype Homer : Person {
Name = "Homer Simpson";
Gender = "Male";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
}
prototype Marge : Person {
Name = "Marge Simpson";
Gender = "Female";
Location = SimpsonsHouse;
ParentOf = [Bart, Lisa, Maggie];
}
Connections:
Homer and Marge creates SimpsonsHouseParent (subtree intersection).Name = "Homer Simpson" (graph differencing).SimpsonsHouseParent_SubType tests graph structure (subgraph isomorphism).Homer’s unique properties (graph projection).person → simpsons_house_parent → Homer (hierarchical clustering).What’s Happening?
Homer and Marge link to SimpsonsHouseParent via isa, with Paths as divergent subgraphs.Beyond Ontologies and Neural Networks: ProtoScript’s graph-based operations are dynamic and interpretable, unlike OWL’s static axioms or neural networks’ statistical models.
They:
Beyond Gradient Descent: Graph-based operations offer a deterministic, scalable alternative, ensuring clarity.
The Addition Problem in ProtoScript illustrates how the system handles complex, iterative tasks like binary addition (e.g., 11 + 10 = 101) using graph-based operations, extending its ontology beyond simple categorization to computational tasks. Unlike traditional ontologies, which focus on static relationships, ProtoScript’s ability to model binary operations (functions with two inputs) showcases its potential as a universal computational framework. This section explores the Addition Problem, its graph-based solution, and the challenges of binary operations, using analogies and examples to guide developers familiar with C# or JavaScript.
The Addition Problem is critical for:
Significance in ProtoScript’s Ontology:
Traditional ontologies lack computational capabilities, focusing on static data. Neural networks handle computations via gradient descent but lack interpretability. ProtoScript offers:
For C# developers:
For JavaScript developers:
For database developers:
Problem: Compute binary addition (e.g., 11 + 10 = 101) in ProtoScript, requiring:
11, 10).Solution: Use temporary subgraphs and computed properties:
Bit0, Bit1) and carry.(Bit1 + Bit2 + Carry) % 2) and carry ((Bit1 + Bit2 + Carry) / 2).101).Prototypes:
prototype BinaryNumber {
Collection Bits = new Collection();
}
prototype Bit {
int Value = 0;
}
prototype AdditionState {
Collection SumBits = new Collection();
int Carry = 0;
function ComputeBit(Bit bit1, Bit bit2) : Bit {
int sum = bit1.Value + bit2.Value + Carry;
SumBits.Add(new Bit { Value = sum % 2 });
Carry = sum / 2;
return SumBits[SumBits.Count - 1];
}
}
prototype Number_11 : BinaryNumber {
Bits = [Bit_1, Bit_1];
}
prototype Number_10 : BinaryNumber {
Bits = [Bit_1, Bit_0];
}
prototype Bit_1 : Bit {
Value = 1;
}
prototype Bit_0 : Bit {
Value = 0;
}
Addition Function:
function AddBinary(BinaryNumber num1, BinaryNumber num2) : BinaryNumber {
AdditionState state = new AdditionState();
int maxLength = Math.Max(num1.Bits.Count, num2.Bits.Count);
for (int i = 0; i < maxLength; i++) {
Bit bit1 = i < num1.Bits.Count ? num1.Bits[i] : Bit_0;
Bit bit2 = i < num2.Bits.Count ? num2.Bits[i] : Bit_0;
state.ComputeBit(bit1, bit2);
}
if (state.Carry > 0) {
state.SumBits.Add(new Bit { Value = state.Carry });
}
return new BinaryNumber { Bits = state.SumBits };
}
Application:
BinaryNumber result = AddBinary(Number_11, Number_10);
What’s Happening?
11 ([1, 1]) and 10 ([1, 0]): 0: 1 + 0 + 0 = 1, carry = 0.1: 1 + 1 + 0 = 2, sum = 0, carry = 1.1 as the final bit.101 ([1, 0, 1]).AdditionState nodes track bits and carry, linked to the output BinaryNumber.Beyond Ontologies and Neural Networks: ProtoScript’s graph-based computation is dynamic and interpretable, unlike OWL’s static data or neural networks’ statistical approximations.
It:
Beyond Gradient Descent: Offers a symbolic, graph-based alternative, avoiding iterative optimization.
Learning Transformation Functions in ProtoScript involves discovering graph-based operations to map inputs to outputs, a process akin to symbolic regression but applied to structured data. This task faces combinatorial challenges due to the vast search space of possible graph configurations, particularly for complex functions like binary addition. Unlike traditional ontologies or neural networks, ProtoScript’s approach leverages its graph-based ontology to learn functions unsupervised, offering a scalable, interpretable alternative. This section explores the mechanics of learning functions, the combinatorial challenges, and potential solutions, using analogies and examples to guide developers.
Learning functions is critical for:
Significance in ProtoScript’s Ontology:
Traditional ontologies lack function learning, relying on static mappings. Neural networks learn functions via gradient descent but require large datasets. ProtoScript offers:
For C# developers:
For JavaScript developers:
For database developers:
Process: Learn a Transformation Function by:
true). [TransferFunction] with the inferred operations.Combinatorial Challenges:
Input-Output Pair:
int[1]int[-1]Learned Function:
prototype Integer {
int Value = 0;
function Negation() : Integer {
return new Integer { Value = -this.Value };
}
}
What’s Happening?
Value by observing the pair.Integer[1] links to Integer[-1] via a Negation edge.Solution Strategies:
Beyond Ontologies and Neural Networks: ProtoScript’s symbolic regression on graphs is dynamic and interpretable, unlike OWL’s static rules or neural networks’ data-heavy training.
It:
Beyond Gradient Descent: Offers a symbolic, graph-based alternative, ensuring efficiency and clarity.
ProtoScript’s graph-based ontology, with its unsupervised learning mechanisms (Shadows, Paths, Subtypes, Transformation Functions, HCPs, hierarchical trees, dual-channel clustering), represents a significant advancement in symbolic AI. Its implications span theoretical innovation, practical applications, and future development opportunities, positioning ProtoScript as a versatile platform for knowledge representation and reasoning. This section explores the implications of ProtoScript’s approach and outlines future directions, using analogies and examples to guide developers familiar with C# or JavaScript toward its potential.
int or SQL INTEGER in one ontology.ProtoScript offers:
For C# developers:
For JavaScript developers:
For database developers:
Narrative_Role feedback.Narrative_Role scoring in dual-channel clustering.Scenario: Transform NL medical queries to SQL for patient records.
Prototypes:
prototype Medical_Query {
string QueryText = "";
string Context = "";
}
prototype SQL_Query {
string TableName = "";
string Conditions = "";
}
[TransferFunction(NL)]
function MedicalQuery_To_SQL(Medical_Query query) : SQL_Query {
SQL_Query sql = new SQL_Query();
if (query.QueryText == "Find patients with diabetes" && query.Context == "Hospital") {
sql.TableName = "Patients";
sql.Conditions = "Condition = 'Diabetes'";
}
return sql;
}
What’s Happening?
Context for disambiguation.Medical_Query links to a SQL_Query node with "Patients" and "Condition = 'Diabetes'".Beyond Ontologies and Neural Networks: ProtoScript’s dynamic, interpretable mappings enable real-world applications, unlike OWL’s static rules or neural networks’ opacity.
They:
Beyond Gradient Descent: ProtoScript’s symbolic, graph-based framework offers a scalable, interpretable alternative, poised for future growth.
ProtoScript’s remaining advanced topics—Constrained and Underconstrained Relationships, Graph Theory and Computer Science Connections, the Addition Problem and Binary Operations, Learning Functions and Combinatorial Challenges, and Implications and Future Directions—showcase its transformative potential as a dynamic, unsupervised learning framework. By integrating graph-based reasoning with computational expressiveness, ProtoScript transcends traditional ontologies and neural networks, offering a scalable, interpretable platform for knowledge representation and reasoning. Developers can now leverage ProtoScript to build innovative, cross-domain applications, from code generation to AI-driven querying, with a clear path for future advancements. Congratulations on mastering ProtoScript’s advanced capabilities—you’re ready to shape the future of symbolic AI!