Deploy JSON-LD v3 pipeline from jjohare/logseq @ 4ae936046553e31781ed7c573c41fafd5da4dd39 jjohare/logseq@4ae9360

Author: jjohare

api/markdown/3D Gaussian Splatting.md

@@ -179,7 +179,7 @@ alias:: TELE-051-3d-gaussian-splatting
         "label": "Virtual Reality Telepresence"
       },
       {
-        "@id": "urn:ngm:class:scene-reconstruction",
+        "@id": "urn:ngm:class:3-d-scene-reconstruction",
         "label": "Scene Reconstruction"
       }
     ]

api/markdown/3D Rendering.md

@@ -70,7 +70,7 @@ public:: true
         "label": "Texture Mapping"
       },
       {
-        "@id": "urn:ngm:class:shader-programming",
+        "@id": "urn:ngm:class:shader",
         "label": "Shader Programming"
       },
       {

api/markdown/3d-modelling.md

@@ -0,0 +1,49 @@
+- ### Definition
+- 3D modelling is the creation of a mathematical representation of a three-dimensional surface or volume using [[Design Software]].
+- Models are commonly built from a [[Polygon Mesh]], parametric surfaces or volumetric data.
+- The resulting geometry is later textured, rigged, animated and rendered.
+- It is the foundational discipline of [[Computer Graphics]] for games, film, visualisation and spatial computing.
+- ### Overview
+- 3D modelling encompasses several paradigms. Polygonal modelling builds surfaces from vertices, edges and faces and dominates real-time and entertainment work. NURBS and parametric surfaces produce mathematically smooth geometry favoured in industrial design.
+- Digital sculpting treats the model like virtual clay, enabling high-detail organic forms that are later retopologised into efficient meshes.
+- Procedural and generative approaches construct geometry from rules and parameters, scaling complex environments and assets.
+- The discipline is distinct from but adjacent to computer-aided design, which prioritises engineering precision, manufacturability and parametric constraints over artistic surface modelling.
+- ### Key aspects
+- Mesh construction from vertices, edges and faces.
+- Clean topology supporting deformation and subdivision.
+- UV layout to prepare surfaces for texturing.
+- Level-of-detail management for performance.
+- Interchange through formats such as FBX and glTF.
+- ### Mechanisms
+- Box and polygon modelling for hard-surface and stylised assets.
+- Digital sculpting and retopology for organic detail.
+- Boolean and subdivision-surface operations.
+- Procedural generation driven by parameters and graphs.
+- ### Applications
+- Game assets and characters for real-time engines.
+- Film and visual effects environments and creatures.
+- Product visualisation and industrial design.
+- Digital humans and props for augmented and virtual reality.
+- ### Relationships
+- hasPart:: [[Polygon Mesh]]
+- hasPart:: [[Geometry Processing]]
+- hasPart:: [[Topology]]
+- requires:: [[Design Software]]
+- enables:: [[Rendering]]
+- enables:: [[Character Model]]
+- enables:: [[Texture Mapping]]
+- uses:: [[UV Mapping]]
+- uses:: [[Computer-Aided Design]]
+- supports:: [[Visual Effects]]
+- supports:: [[Game Engine]]
+- supports:: [[Virtual Production]]
+- contrastsWith:: [[Computer-Aided Design]]
+- relatedTo:: [[FBX]]
+- relatedTo:: [[Character Rigging]]
+- relatedTo:: [[Skinning]]
+- bridgesTo:: [[Augmented Reality]]
+- sameAs:: [[3D Modeling]]
+- ### Provenance
+- updated:: 2026-06-15
+- attributedTo:: did:nostr:ontology-mesh
+- inferenceRule:: GapMaterialisation

api/markdown/A Star Algorithm.md

@@ -0,0 +1,160 @@
+public:: true
+
+# A Star Algorithm
+
+```json-ld
+{
+  "@context": "https://narrativegoldmine.com/ns/v1",
+  "@id": "urn:visionflow:page:a-star-algorithm",
+  "@type": "Page",
+  "title": "A Star Algorithm",
+  "vc:slug": "a-star-algorithm",
+  "vc:public": true,
+  "vc:schemaVersion": 2,
+  "vc:outboundWikilinks": []
+}
+```
+
+```json-ld
+{
+  "@context": "https://narrativegoldmine.com/ns/v2.jsonld",
+  "@id": "urn:ngm:class:a-star-algorithm",
+  "@type": "Class",
+  "label": "A Star Algorithm",
+  "definition": "The A Star algorithm is an informed graph-search method that finds a least-cost path between nodes by combining the accumulated cost from the start with a heuristic estimate of the remaining cost to the goal. By expanding the node with the lowest combined estimate, it guarantees an optimal path when the heuristic is admissible. A Star generalises Dijkstra's algorithm by adding goal-directed guidance.",
+  "domain": "artificial-intelligence",
+  "maturity": "established",
+  "subClassOf": [
+    {
+      "@id": "urn:ngm:class:search-algorithm",
+      "label": "Search Algorithm"
+    }
+  ],
+  "relations": {
+    "implements": [
+      {
+        "@id": "urn:ngm:class:informed-search",
+        "label": "Informed Search"
+      },
+      {
+        "@id": "urn:ngm:class:graph-search",
+        "label": "Graph Search"
+      }
+    ],
+    "hasPart": [
+      {
+        "@id": "urn:ngm:class:cost-function",
+        "label": "Cost Function"
+      }
+    ],
+    "uses": [
+      {
+        "@id": "urn:ngm:class:cost-function",
+        "label": "Cost Function"
+      },
+      {
+        "@id": "urn:ngm:class:graph-search",
+        "label": "Graph Search"
+      }
+    ],
+    "partOf": [
+      {
+        "@id": "urn:ngm:class:search-algorithm",
+        "label": "Search Algorithm"
+      }
+    ],
+    "contrastsWith": [
+      {
+        "@id": "urn:ngm:class:dijkstra-algorithm",
+        "label": "Dijkstra Algorithm"
+      }
+    ],
+    "supports": [
+      {
+        "@id": "urn:ngm:class:pathfinding",
+        "label": "Pathfinding"
+      },
+      {
+        "@id": "urn:ngm:class:autonomous-navigation",
+        "label": "Autonomous Navigation"
+      }
+    ],
+    "enables": [
+      {
+        "@id": "urn:ngm:class:pathfinding",
+        "label": "Pathfinding"
+      }
+    ],
+    "requires": [
+      {
+        "@id": "urn:ngm:class:graph-search",
+        "label": "Graph Search"
+      }
+    ],
+    "relatedTo": [
+      {
+        "@id": "urn:ngm:class:optimisation",
+        "label": "Optimisation"
+      },
+      {
+        "@id": "urn:ngm:class:robotics",
+        "label": "Robotics"
+      },
+      {
+        "@id": "urn:ngm:class:game-ai",
+        "label": "Game AI"
+      },
+      {
+        "@id": "urn:ngm:class:dijkstra-algorithm",
+        "label": "Dijkstra Algorithm"
+      }
+    ]
+  },
+  "sameAs": [],
+  "quality": 0.62,
+  "provenance": {
+    "attributedTo": "did:nostr:ontology-mesh",
+    "generatedAt": "2026-06-15T00:00:00Z",
+    "inferenceRule": "GapMaterialisation"
+  }
+}
+```
+
+- ### Definition
+  - The A Star algorithm is an informed graph-search method that finds a least-cost path between nodes by combining the accumulated cost from the start with a heuristic estimate of the remaining cost to the goal. By expanding the node with the lowest combined estimate, it guarantees an optimal path when the heuristic is admissible. A Star generalises Dijkstra's algorithm by adding goal-directed guidance.
+  - Related: [[Search Algorithm]] [[Pathfinding]] [[Dijkstra Algorithm]] [[Cost Function]]
+- ### Overview
+  - A Star maintains a priority queue ordered by the evaluation function f(n) = g(n) + h(n), where g is the cost so far and h is the heuristic estimate to the goal. Admissibility of the heuristic ensures optimality, while consistency additionally guarantees that nodes are never re-expanded.
+- ### Mechanisms
+  - Evaluation function combining path cost and heuristic estimate
+  - Admissible heuristics guaranteeing optimal solutions
+  - Priority queue expansion of the most promising node
+  - Reduction to Dijkstra's algorithm when the heuristic is zero
+  - Trade-off between heuristic accuracy and nodes expanded
+- ### Applications
+  - Pathfinding in robotics and autonomous navigation
+  - Route planning in maps and logistics
+  - Movement and navigation in game artificial intelligence
+  - General least-cost search over weighted graphs
+- ### Relationships
+  - subClassOf:: [[Search Algorithm]]
+  - implements:: [[Informed Search]]
+  - implements:: [[Graph Search]]
+  - hasPart:: [[Cost Function]]
+  - uses:: [[Cost Function]]
+  - uses:: [[Graph Search]]
+  - partOf:: [[Search Algorithm]]
+  - contrastsWith:: [[Dijkstra Algorithm]]
+  - supports:: [[Pathfinding]]
+  - supports:: [[Autonomous Navigation]]
+  - enables:: [[Pathfinding]]
+  - requires:: [[Graph Search]]
+  - relatedTo:: [[Optimisation]]
+  - relatedTo:: [[Robotics]]
+  - relatedTo:: [[Game AI]]
+  - relatedTo:: [[Dijkstra Algorithm]]
+- ### Provenance
+  - attributedTo:: did:nostr:ontology-mesh
+  - inferenceRule:: GapMaterialisation
+  - quality:: 0.62
+  - updated:: 2026-06-15

api/markdown/AI Energy Optimisation.md

@@ -139,7 +139,7 @@ public:: true
     ],
     "bridgesTo": [
       {
-        "@id": "urn:ngm:class:data-centre-infrastructure",
+        "@id": "urn:ngm:class:data-centre",
         "label": "Data Centre Infrastructure"
       },
       {

api/markdown/AI Impact Assessment.md

@@ -238,7 +238,7 @@ public:: true
         "label": "GDPR"
       },
       {
-        "@id": "urn:ngm:class:environmental-impact-assessment",
+        "@id": "urn:ngm:class:environmental-assessment",
         "label": "Environmental Impact Assessment"
       }
     ],

api/markdown/AI Risk.md

@@ -230,7 +230,7 @@ public:: true
     ],
     "relatedTo": [
       {
-        "@id": "urn:ngm:class:model-alignment",
+        "@id": "urn:ngm:class:ai-alignment",
         "label": "Model Alignment"
       },
       {

api/markdown/AI Risks.md

@@ -801,10 +801,6 @@ public:: true
         "@id": "urn:ngm:class:insurance-underwriting",
         "label": "Insurance Underwriting"
       },
-      {
-        "@id": "urn:ngm:class:international-coordination",
-        "label": "International Coordination"
-      },
       {
         "@id": "urn:ngm:class:knowledge-graph-publication-classifier",
         "label": "Public Awareness"