{ "schemaVersion": "1.0", "item": { "slug": "ontology-mapper", "name": "Ontology Mapper", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/datadrivenconstruction/ontology-mapper", "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/ontology-mapper", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/ontology-mapper", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ontology-mapper", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "claw.json", "instructions.md", "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/ontology-mapper" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/ontology-mapper", "agentPageUrl": "https://openagent3.xyz/skills/ontology-mapper/agent", "manifestUrl": "https://openagent3.xyz/skills/ontology-mapper/agent.json", "briefUrl": "https://openagent3.xyz/skills/ontology-mapper/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Overview", "body": "Based on DDC methodology (Chapter 2.2), this skill maps construction data to standard ontologies like IFC, COBie, Uniclass, and OmniClass, enabling semantic interoperability between systems.\n\nBook Reference: \"Доминирование открытых данных\" / \"Open Data Dominance\"" }, { "title": "Quick Start", "body": "from dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import List, Dict, Optional, Set, Tuple\nfrom datetime import datetime\nimport json\nimport re\n\nclass OntologyType(Enum):\n \"\"\"Standard construction ontologies\"\"\"\n IFC = \"ifc\" # Industry Foundation Classes\n COBIE = \"cobie\" # Construction Operations Building Information Exchange\n UNICLASS = \"uniclass\" # UK classification\n OMNICLASS = \"omniclass\" # North American classification\n MASTERFORMAT = \"masterformat\" # CSI MasterFormat\n UNIFORMAT = \"uniformat\" # CSI UniFormat\n CUSTOM = \"custom\" # Custom ontology\n\nclass MappingConfidence(Enum):\n \"\"\"Confidence level of mapping\"\"\"\n EXACT = \"exact\" # 100% match\n HIGH = \"high\" # 90%+ match\n MEDIUM = \"medium\" # 70-90% match\n LOW = \"low\" # 50-70% match\n UNCERTAIN = \"uncertain\" # <50% match\n\nclass RelationType(Enum):\n \"\"\"Types of relationships between concepts\"\"\"\n EQUIVALENT = \"equivalent\" # Same concept\n BROADER = \"broader\" # Source is more specific\n NARROWER = \"narrower\" # Source is more general\n RELATED = \"related\" # Related but not equivalent\n PART_OF = \"part_of\" # Component relationship\n HAS_PART = \"has_part\" # Contains components\n\n@dataclass\nclass OntologyConcept:\n \"\"\"Concept in an ontology\"\"\"\n id: str\n name: str\n ontology: OntologyType\n definition: Optional[str] = None\n parent_id: Optional[str] = None\n synonyms: List[str] = field(default_factory=list)\n properties: Dict[str, str] = field(default_factory=dict)\n\n@dataclass\nclass SemanticMapping:\n \"\"\"Mapping between two concepts\"\"\"\n source_concept: str\n source_ontology: OntologyType\n target_concept: str\n target_ontology: OntologyType\n relation: RelationType\n confidence: MappingConfidence\n notes: Optional[str] = None\n created_by: str = \"auto\"\n created_at: datetime = field(default_factory=datetime.now)\n\n@dataclass\nclass MappingResult:\n \"\"\"Result of ontology mapping operation\"\"\"\n source_field: str\n source_value: str\n mappings: List[SemanticMapping]\n best_match: Optional[SemanticMapping] = None\n unmapped: bool = False\n\n@dataclass\nclass OntologyMappingReport:\n \"\"\"Complete mapping report\"\"\"\n total_fields: int\n mapped_fields: int\n unmapped_fields: int\n mappings: List[MappingResult]\n coverage: float\n confidence_distribution: Dict[str, int]\n recommendations: List[str]\n\n\nclass OntologyMapper:\n \"\"\"\n Map construction data to standard ontologies.\n Based on DDC methodology Chapter 2.2.\n \"\"\"\n\n def __init__(self):\n self.ontologies = self._load_ontologies()\n self.mapping_rules = self._load_mapping_rules()\n self.synonym_map = self._build_synonym_map()\n\n def _load_ontologies(self) -> Dict[OntologyType, Dict[str, OntologyConcept]]:\n \"\"\"Load standard construction ontologies\"\"\"\n ontologies = {}\n\n # IFC Schema (simplified)\n ontologies[OntologyType.IFC] = {\n \"IfcWall\": OntologyConcept(\"IfcWall\", \"Wall\", OntologyType.IFC,\n \"A vertical construction that bounds or subdivides spaces\"),\n \"IfcSlab\": OntologyConcept(\"IfcSlab\", \"Slab\", OntologyType.IFC,\n \"A horizontal planar building element\"),\n \"IfcBeam\": OntologyConcept(\"IfcBeam\", \"Beam\", OntologyType.IFC,\n \"A horizontal structural member\"),\n \"IfcColumn\": OntologyConcept(\"IfcColumn\", \"Column\", OntologyType.IFC,\n \"A vertical structural member\"),\n \"IfcDoor\": OntologyConcept(\"IfcDoor\", \"Door\", OntologyType.IFC,\n \"A building element for access\"),\n \"IfcWindow\": OntologyConcept(\"IfcWindow\", \"Window\", OntologyType.IFC,\n \"A building element for light and ventilation\"),\n \"IfcRoof\": OntologyConcept(\"IfcRoof\", \"Roof\", OntologyType.IFC,\n \"A building element covering a building\"),\n \"IfcStair\": OntologyConcept(\"IfcStair\", \"Stair\", OntologyType.IFC,\n \"A vertical circulation element\"),\n \"IfcSpace\": OntologyConcept(\"IfcSpace\", \"Space\", OntologyType.IFC,\n \"A defined volume of air\"),\n \"IfcBuildingStorey\": OntologyConcept(\"IfcBuildingStorey\", \"Building Storey\",\n OntologyType.IFC, \"A horizontal aggregation of spaces\"),\n }\n\n # COBie (simplified)\n ontologies[OntologyType.COBIE] = {\n \"Floor\": OntologyConcept(\"Floor\", \"Floor\", OntologyType.COBIE,\n \"A floor or level in a building\"),\n \"Space\": OntologyConcept(\"Space\", \"Space\", OntologyType.COBIE,\n \"A spatial region\"),\n \"Type\": OntologyConcept(\"Type\", \"Type\", OntologyType.COBIE,\n \"A product type or specification\"),\n \"Component\": OntologyConcept(\"Component\", \"Component\", OntologyType.COBIE,\n \"An individual product instance\"),\n \"Zone\": OntologyConcept(\"Zone\", \"Zone\", OntologyType.COBIE,\n \"A spatial grouping of spaces\"),\n \"System\": OntologyConcept(\"System\", \"System\", OntologyType.COBIE,\n \"A building system or network\"),\n }\n\n # Uniclass (simplified)\n ontologies[OntologyType.UNICLASS] = {\n \"Ss_25\": OntologyConcept(\"Ss_25\", \"Wall Systems\", OntologyType.UNICLASS),\n \"Ss_30\": OntologyConcept(\"Ss_30\", \"Roof Systems\", OntologyType.UNICLASS),\n \"Ss_32\": OntologyConcept(\"Ss_32\", \"Floor Systems\", OntologyType.UNICLASS),\n \"Ss_35\": OntologyConcept(\"Ss_35\", \"Stair Systems\", OntologyType.UNICLASS),\n \"Pr_20\": OntologyConcept(\"Pr_20\", \"Structural Products\", OntologyType.UNICLASS),\n \"Pr_30\": OntologyConcept(\"Pr_30\", \"Wall Products\", OntologyType.UNICLASS),\n \"Pr_35\": OntologyConcept(\"Pr_35\", \"Door Products\", OntologyType.UNICLASS),\n \"Pr_40\": OntologyConcept(\"Pr_40\", \"Window Products\", OntologyType.UNICLASS),\n }\n\n # MasterFormat (simplified)\n ontologies[OntologyType.MASTERFORMAT] = {\n \"03\": OntologyConcept(\"03\", \"Concrete\", OntologyType.MASTERFORMAT),\n \"04\": OntologyConcept(\"04\", \"Masonry\", OntologyType.MASTERFORMAT),\n \"05\": OntologyConcept(\"05\", \"Metals\", OntologyType.MASTERFORMAT),\n \"06\": OntologyConcept(\"06\", \"Wood and Plastics\", OntologyType.MASTERFORMAT),\n \"07\": OntologyConcept(\"07\", \"Thermal and Moisture Protection\", OntologyType.MASTERFORMAT),\n \"08\": OntologyConcept(\"08\", \"Doors and Windows\", OntologyType.MASTERFORMAT),\n \"09\": OntologyConcept(\"09\", \"Finishes\", OntologyType.MASTERFORMAT),\n \"22\": OntologyConcept(\"22\", \"Plumbing\", OntologyType.MASTERFORMAT),\n \"23\": OntologyConcept(\"23\", \"HVAC\", OntologyType.MASTERFORMAT),\n \"26\": OntologyConcept(\"26\", \"Electrical\", OntologyType.MASTERFORMAT),\n }\n\n return ontologies\n\n def _load_mapping_rules(self) -> List[SemanticMapping]:\n \"\"\"Load predefined mapping rules between ontologies\"\"\"\n rules = [\n # IFC to COBie\n SemanticMapping(\"IfcBuildingStorey\", OntologyType.IFC, \"Floor\",\n OntologyType.COBIE, RelationType.EQUIVALENT, MappingConfidence.EXACT),\n SemanticMapping(\"IfcSpace\", OntologyType.IFC, \"Space\",\n OntologyType.COBIE, RelationType.EQUIVALENT, MappingConfidence.EXACT),\n\n # IFC to Uniclass\n SemanticMapping(\"IfcWall\", OntologyType.IFC, \"Ss_25\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcRoof\", OntologyType.IFC, \"Ss_30\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcSlab\", OntologyType.IFC, \"Ss_32\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcDoor\", OntologyType.IFC, \"Pr_35\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcWindow\", OntologyType.IFC, \"Pr_40\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n\n # IFC to MasterFormat\n SemanticMapping(\"IfcDoor\", OntologyType.IFC, \"08\",\n OntologyType.MASTERFORMAT, RelationType.BROADER, MappingConfidence.MEDIUM),\n SemanticMapping(\"IfcWindow\", OntologyType.IFC, \"08\",\n OntologyType.MASTERFORMAT, RelationType.BROADER, MappingConfidence.MEDIUM),\n ]\n return rules\n\n def _build_synonym_map(self) -> Dict[str, List[str]]:\n \"\"\"Build synonym mappings for fuzzy matching\"\"\"\n return {\n \"wall\": [\"partition\", \"barrier\", \"divider\"],\n \"door\": [\"entrance\", \"portal\", \"opening\"],\n \"window\": [\"glazing\", \"fenestration\", \"opening\"],\n \"floor\": [\"slab\", \"deck\", \"storey\", \"level\"],\n \"roof\": [\"roofing\", \"covering\", \"canopy\"],\n \"beam\": [\"girder\", \"joist\", \"lintel\"],\n \"column\": [\"pillar\", \"post\", \"pier\"],\n \"stair\": [\"stairway\", \"staircase\", \"steps\"],\n \"space\": [\"room\", \"area\", \"zone\"],\n \"concrete\": [\"cement\", \"reinforced\"],\n \"steel\": [\"metal\", \"iron\"],\n }\n\n def map_field(\n self,\n field_name: str,\n field_value: str,\n source_ontology: Optional[OntologyType] = None,\n target_ontology: OntologyType = OntologyType.IFC\n ) -> MappingResult:\n \"\"\"\n Map a single field to target ontology.\n\n Args:\n field_name: Name of the field\n field_value: Value to map\n source_ontology: Source ontology if known\n target_ontology: Target ontology to map to\n\n Returns:\n Mapping result with possible matches\n \"\"\"\n mappings = []\n\n # Normalize the value\n normalized = self._normalize_value(field_value)\n\n # Check direct matches in existing rules\n for rule in self.mapping_rules:\n if rule.target_ontology == target_ontology:\n if self._matches(normalized, rule.source_concept):\n mappings.append(rule)\n\n # Check target ontology directly\n target_concepts = self.ontologies.get(target_ontology, {})\n for concept_id, concept in target_concepts.items():\n similarity = self._calculate_similarity(normalized, concept)\n if similarity > 0.5:\n confidence = self._similarity_to_confidence(similarity)\n mappings.append(SemanticMapping(\n source_concept=field_value,\n source_ontology=source_ontology or OntologyType.CUSTOM,\n target_concept=concept_id,\n target_ontology=target_ontology,\n relation=RelationType.EQUIVALENT if similarity > 0.9 else RelationType.RELATED,\n confidence=confidence\n ))\n\n # Sort by confidence\n confidence_order = [\n MappingConfidence.EXACT,\n MappingConfidence.HIGH,\n MappingConfidence.MEDIUM,\n MappingConfidence.LOW,\n MappingConfidence.UNCERTAIN\n ]\n mappings.sort(key=lambda m: confidence_order.index(m.confidence))\n\n return MappingResult(\n source_field=field_name,\n source_value=field_value,\n mappings=mappings,\n best_match=mappings[0] if mappings else None,\n unmapped=len(mappings) == 0\n )\n\n def _normalize_value(self, value: str) -> str:\n \"\"\"Normalize a value for matching\"\"\"\n # Remove common prefixes\n prefixes = [\"ifc\", \"cobie\", \"type\", \"element\"]\n normalized = value.lower().strip()\n\n for prefix in prefixes:\n if normalized.startswith(prefix):\n normalized = normalized[len(prefix):]\n\n return normalized.strip(\"_- \")\n\n def _matches(self, value: str, concept: str) -> bool:\n \"\"\"Check if value matches concept\"\"\"\n normalized_value = self._normalize_value(value)\n normalized_concept = self._normalize_value(concept)\n return normalized_value == normalized_concept\n\n def _calculate_similarity(\n self,\n value: str,\n concept: OntologyConcept\n ) -> float:\n \"\"\"Calculate similarity between value and concept\"\"\"\n value_lower = value.lower()\n concept_name_lower = concept.name.lower()\n concept_id_lower = concept.id.lower()\n\n # Exact match\n if value_lower == concept_name_lower or value_lower == concept_id_lower:\n return 1.0\n\n # Partial match in name\n if value_lower in concept_name_lower or concept_name_lower in value_lower:\n return 0.8\n\n # Check synonyms\n for key, synonyms in self.synonym_map.items():\n if key in value_lower:\n if key in concept_name_lower:\n return 0.9\n for syn in synonyms:\n if syn in concept_name_lower:\n return 0.7\n\n # Definition match\n if concept.definition:\n if value_lower in concept.definition.lower():\n return 0.6\n\n return 0.0\n\n def _similarity_to_confidence(self, similarity: float) -> MappingConfidence:\n \"\"\"Convert similarity score to confidence level\"\"\"\n if similarity >= 0.95:\n return MappingConfidence.EXACT\n elif similarity >= 0.8:\n return MappingConfidence.HIGH\n elif similarity >= 0.6:\n return MappingConfidence.MEDIUM\n elif similarity >= 0.4:\n return MappingConfidence.LOW\n else:\n return MappingConfidence.UNCERTAIN\n\n def map_schema(\n self,\n schema: Dict[str, List[str]],\n target_ontology: OntologyType = OntologyType.IFC\n ) -> OntologyMappingReport:\n \"\"\"\n Map entire schema to target ontology.\n\n Args:\n schema: Dictionary of field names to sample values\n target_ontology: Target ontology\n\n Returns:\n Complete mapping report\n \"\"\"\n all_mappings = []\n confidence_dist = {c.value: 0 for c in MappingConfidence}\n\n for field_name, sample_values in schema.items():\n # Use first sample value\n value = sample_values[0] if sample_values else field_name\n\n result = self.map_field(field_name, value, target_ontology=target_ontology)\n all_mappings.append(result)\n\n if result.best_match:\n confidence_dist[result.best_match.confidence.value] += 1\n\n mapped = sum(1 for m in all_mappings if not m.unmapped)\n unmapped = len(all_mappings) - mapped\n coverage = mapped / len(all_mappings) if all_mappings else 0\n\n recommendations = self._generate_recommendations(all_mappings, coverage)\n\n return OntologyMappingReport(\n total_fields=len(all_mappings),\n mapped_fields=mapped,\n unmapped_fields=unmapped,\n mappings=all_mappings,\n coverage=coverage,\n confidence_distribution=confidence_dist,\n recommendations=recommendations\n )\n\n def _generate_recommendations(\n self,\n mappings: List[MappingResult],\n coverage: float\n ) -> List[str]:\n \"\"\"Generate recommendations for improving mappings\"\"\"\n recommendations = []\n\n if coverage < 0.7:\n recommendations.append(\n f\"Low mapping coverage ({coverage:.0%}). Consider adding custom mappings.\"\n )\n\n low_confidence = [m for m in mappings\n if m.best_match and m.best_match.confidence\n in [MappingConfidence.LOW, MappingConfidence.UNCERTAIN]]\n if low_confidence:\n recommendations.append(\n f\"{len(low_confidence)} mappings have low confidence. Review manually.\"\n )\n\n unmapped = [m for m in mappings if m.unmapped]\n if unmapped:\n fields = [m.source_field for m in unmapped[:5]]\n recommendations.append(\n f\"Unmapped fields: {', '.join(fields)}. Add custom mappings.\"\n )\n\n return recommendations\n\n def create_mapping(\n self,\n source: str,\n source_ontology: OntologyType,\n target: str,\n target_ontology: OntologyType,\n relation: RelationType = RelationType.EQUIVALENT,\n notes: Optional[str] = None\n ) -> SemanticMapping:\n \"\"\"Create a new manual mapping\"\"\"\n mapping = SemanticMapping(\n source_concept=source,\n source_ontology=source_ontology,\n target_concept=target,\n target_ontology=target_ontology,\n relation=relation,\n confidence=MappingConfidence.EXACT,\n notes=notes,\n created_by=\"manual\"\n )\n self.mapping_rules.append(mapping)\n return mapping\n\n def export_mappings(self, format: str = \"json\") -> str:\n \"\"\"Export all mappings\"\"\"\n if format == \"json\":\n mappings_data = []\n for rule in self.mapping_rules:\n mappings_data.append({\n \"source\": rule.source_concept,\n \"source_ontology\": rule.source_ontology.value,\n \"target\": rule.target_concept,\n \"target_ontology\": rule.target_ontology.value,\n \"relation\": rule.relation.value,\n \"confidence\": rule.confidence.value\n })\n return json.dumps(mappings_data, indent=2)\n else:\n raise ValueError(f\"Unsupported format: {format}\")\n\n def generate_report(self, report: OntologyMappingReport) -> str:\n \"\"\"Generate mapping report\"\"\"\n output = f\"\"\"\n# Ontology Mapping Report\n\n## Summary\n- **Total Fields:** {report.total_fields}\n- **Mapped Fields:** {report.mapped_fields}\n- **Unmapped Fields:** {report.unmapped_fields}\n- **Coverage:** {report.coverage:.0%}\n\n## Confidence Distribution\n\"\"\"\n for conf, count in report.confidence_distribution.items():\n if count > 0:\n output += f\"- **{conf.title()}:** {count}\\n\"\n\n output += \"\\n## Recommendations\\n\"\n for rec in report.recommendations:\n output += f\"- {rec}\\n\"\n\n output += \"\\n## Mappings\\n\"\n for mapping in report.mappings[:20]:\n status = \"✓\" if not mapping.unmapped else \"✗\"\n target = mapping.best_match.target_concept if mapping.best_match else \"unmapped\"\n conf = mapping.best_match.confidence.value if mapping.best_match else \"-\"\n output += f\"- {status} {mapping.source_field}: {mapping.source_value} → {target} ({conf})\\n\"\n\n return output" }, { "title": "Map Field to IFC", "body": "mapper = OntologyMapper()\n\n# Map a single field\nresult = mapper.map_field(\n field_name=\"element_type\",\n field_value=\"Wall\",\n target_ontology=OntologyType.IFC\n)\n\nif result.best_match:\n print(f\"Mapped to: {result.best_match.target_concept}\")\n print(f\"Confidence: {result.best_match.confidence.value}\")" }, { "title": "Map Entire Schema", "body": "# Define schema with sample values\nschema = {\n \"element_type\": [\"Wall\", \"Door\", \"Window\"],\n \"level\": [\"Level 1\", \"Level 2\"],\n \"material\": [\"Concrete\", \"Steel\"],\n \"room_type\": [\"Office\", \"Corridor\"]\n}\n\nreport = mapper.map_schema(schema, target_ontology=OntologyType.IFC)\n\nprint(f\"Coverage: {report.coverage:.0%}\")\nprint(f\"Mapped: {report.mapped_fields}/{report.total_fields}\")" }, { "title": "Create Custom Mappings", "body": "# Add custom mapping\nmapper.create_mapping(\n source=\"CustomWallType\",\n source_ontology=OntologyType.CUSTOM,\n target=\"IfcWall\",\n target_ontology=OntologyType.IFC,\n relation=RelationType.EQUIVALENT,\n notes=\"Custom wall type from legacy system\"\n)" }, { "title": "Quick Reference", "body": "ComponentPurposeOntologyMapperMain mapping engineOntologyTypeStandard ontologies (IFC, COBie, etc.)SemanticMappingMapping between conceptsMappingResultResult of mapping operationRelationTypeRelationship typesMappingConfidenceConfidence levels" }, { "title": "Resources", "body": "Book: \"Data-Driven Construction\" by Artem Boiko, Chapter 2.2\nWebsite: https://datadrivenconstruction.io" }, { "title": "Next Steps", "body": "Use open-data-integrator for open data\nUse data-model-designer for schema design\nUse bim-validation-pipeline for validation" } ], "body": "Ontology Mapper\nOverview\n\nBased on DDC methodology (Chapter 2.2), this skill maps construction data to standard ontologies like IFC, COBie, Uniclass, and OmniClass, enabling semantic interoperability between systems.\n\nBook Reference: \"Доминирование открытых данных\" / \"Open Data Dominance\"\n\nQuick Start\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import List, Dict, Optional, Set, Tuple\nfrom datetime import datetime\nimport json\nimport re\n\nclass OntologyType(Enum):\n \"\"\"Standard construction ontologies\"\"\"\n IFC = \"ifc\" # Industry Foundation Classes\n COBIE = \"cobie\" # Construction Operations Building Information Exchange\n UNICLASS = \"uniclass\" # UK classification\n OMNICLASS = \"omniclass\" # North American classification\n MASTERFORMAT = \"masterformat\" # CSI MasterFormat\n UNIFORMAT = \"uniformat\" # CSI UniFormat\n CUSTOM = \"custom\" # Custom ontology\n\nclass MappingConfidence(Enum):\n \"\"\"Confidence level of mapping\"\"\"\n EXACT = \"exact\" # 100% match\n HIGH = \"high\" # 90%+ match\n MEDIUM = \"medium\" # 70-90% match\n LOW = \"low\" # 50-70% match\n UNCERTAIN = \"uncertain\" # <50% match\n\nclass RelationType(Enum):\n \"\"\"Types of relationships between concepts\"\"\"\n EQUIVALENT = \"equivalent\" # Same concept\n BROADER = \"broader\" # Source is more specific\n NARROWER = \"narrower\" # Source is more general\n RELATED = \"related\" # Related but not equivalent\n PART_OF = \"part_of\" # Component relationship\n HAS_PART = \"has_part\" # Contains components\n\n@dataclass\nclass OntologyConcept:\n \"\"\"Concept in an ontology\"\"\"\n id: str\n name: str\n ontology: OntologyType\n definition: Optional[str] = None\n parent_id: Optional[str] = None\n synonyms: List[str] = field(default_factory=list)\n properties: Dict[str, str] = field(default_factory=dict)\n\n@dataclass\nclass SemanticMapping:\n \"\"\"Mapping between two concepts\"\"\"\n source_concept: str\n source_ontology: OntologyType\n target_concept: str\n target_ontology: OntologyType\n relation: RelationType\n confidence: MappingConfidence\n notes: Optional[str] = None\n created_by: str = \"auto\"\n created_at: datetime = field(default_factory=datetime.now)\n\n@dataclass\nclass MappingResult:\n \"\"\"Result of ontology mapping operation\"\"\"\n source_field: str\n source_value: str\n mappings: List[SemanticMapping]\n best_match: Optional[SemanticMapping] = None\n unmapped: bool = False\n\n@dataclass\nclass OntologyMappingReport:\n \"\"\"Complete mapping report\"\"\"\n total_fields: int\n mapped_fields: int\n unmapped_fields: int\n mappings: List[MappingResult]\n coverage: float\n confidence_distribution: Dict[str, int]\n recommendations: List[str]\n\n\nclass OntologyMapper:\n \"\"\"\n Map construction data to standard ontologies.\n Based on DDC methodology Chapter 2.2.\n \"\"\"\n\n def __init__(self):\n self.ontologies = self._load_ontologies()\n self.mapping_rules = self._load_mapping_rules()\n self.synonym_map = self._build_synonym_map()\n\n def _load_ontologies(self) -> Dict[OntologyType, Dict[str, OntologyConcept]]:\n \"\"\"Load standard construction ontologies\"\"\"\n ontologies = {}\n\n # IFC Schema (simplified)\n ontologies[OntologyType.IFC] = {\n \"IfcWall\": OntologyConcept(\"IfcWall\", \"Wall\", OntologyType.IFC,\n \"A vertical construction that bounds or subdivides spaces\"),\n \"IfcSlab\": OntologyConcept(\"IfcSlab\", \"Slab\", OntologyType.IFC,\n \"A horizontal planar building element\"),\n \"IfcBeam\": OntologyConcept(\"IfcBeam\", \"Beam\", OntologyType.IFC,\n \"A horizontal structural member\"),\n \"IfcColumn\": OntologyConcept(\"IfcColumn\", \"Column\", OntologyType.IFC,\n \"A vertical structural member\"),\n \"IfcDoor\": OntologyConcept(\"IfcDoor\", \"Door\", OntologyType.IFC,\n \"A building element for access\"),\n \"IfcWindow\": OntologyConcept(\"IfcWindow\", \"Window\", OntologyType.IFC,\n \"A building element for light and ventilation\"),\n \"IfcRoof\": OntologyConcept(\"IfcRoof\", \"Roof\", OntologyType.IFC,\n \"A building element covering a building\"),\n \"IfcStair\": OntologyConcept(\"IfcStair\", \"Stair\", OntologyType.IFC,\n \"A vertical circulation element\"),\n \"IfcSpace\": OntologyConcept(\"IfcSpace\", \"Space\", OntologyType.IFC,\n \"A defined volume of air\"),\n \"IfcBuildingStorey\": OntologyConcept(\"IfcBuildingStorey\", \"Building Storey\",\n OntologyType.IFC, \"A horizontal aggregation of spaces\"),\n }\n\n # COBie (simplified)\n ontologies[OntologyType.COBIE] = {\n \"Floor\": OntologyConcept(\"Floor\", \"Floor\", OntologyType.COBIE,\n \"A floor or level in a building\"),\n \"Space\": OntologyConcept(\"Space\", \"Space\", OntologyType.COBIE,\n \"A spatial region\"),\n \"Type\": OntologyConcept(\"Type\", \"Type\", OntologyType.COBIE,\n \"A product type or specification\"),\n \"Component\": OntologyConcept(\"Component\", \"Component\", OntologyType.COBIE,\n \"An individual product instance\"),\n \"Zone\": OntologyConcept(\"Zone\", \"Zone\", OntologyType.COBIE,\n \"A spatial grouping of spaces\"),\n \"System\": OntologyConcept(\"System\", \"System\", OntologyType.COBIE,\n \"A building system or network\"),\n }\n\n # Uniclass (simplified)\n ontologies[OntologyType.UNICLASS] = {\n \"Ss_25\": OntologyConcept(\"Ss_25\", \"Wall Systems\", OntologyType.UNICLASS),\n \"Ss_30\": OntologyConcept(\"Ss_30\", \"Roof Systems\", OntologyType.UNICLASS),\n \"Ss_32\": OntologyConcept(\"Ss_32\", \"Floor Systems\", OntologyType.UNICLASS),\n \"Ss_35\": OntologyConcept(\"Ss_35\", \"Stair Systems\", OntologyType.UNICLASS),\n \"Pr_20\": OntologyConcept(\"Pr_20\", \"Structural Products\", OntologyType.UNICLASS),\n \"Pr_30\": OntologyConcept(\"Pr_30\", \"Wall Products\", OntologyType.UNICLASS),\n \"Pr_35\": OntologyConcept(\"Pr_35\", \"Door Products\", OntologyType.UNICLASS),\n \"Pr_40\": OntologyConcept(\"Pr_40\", \"Window Products\", OntologyType.UNICLASS),\n }\n\n # MasterFormat (simplified)\n ontologies[OntologyType.MASTERFORMAT] = {\n \"03\": OntologyConcept(\"03\", \"Concrete\", OntologyType.MASTERFORMAT),\n \"04\": OntologyConcept(\"04\", \"Masonry\", OntologyType.MASTERFORMAT),\n \"05\": OntologyConcept(\"05\", \"Metals\", OntologyType.MASTERFORMAT),\n \"06\": OntologyConcept(\"06\", \"Wood and Plastics\", OntologyType.MASTERFORMAT),\n \"07\": OntologyConcept(\"07\", \"Thermal and Moisture Protection\", OntologyType.MASTERFORMAT),\n \"08\": OntologyConcept(\"08\", \"Doors and Windows\", OntologyType.MASTERFORMAT),\n \"09\": OntologyConcept(\"09\", \"Finishes\", OntologyType.MASTERFORMAT),\n \"22\": OntologyConcept(\"22\", \"Plumbing\", OntologyType.MASTERFORMAT),\n \"23\": OntologyConcept(\"23\", \"HVAC\", OntologyType.MASTERFORMAT),\n \"26\": OntologyConcept(\"26\", \"Electrical\", OntologyType.MASTERFORMAT),\n }\n\n return ontologies\n\n def _load_mapping_rules(self) -> List[SemanticMapping]:\n \"\"\"Load predefined mapping rules between ontologies\"\"\"\n rules = [\n # IFC to COBie\n SemanticMapping(\"IfcBuildingStorey\", OntologyType.IFC, \"Floor\",\n OntologyType.COBIE, RelationType.EQUIVALENT, MappingConfidence.EXACT),\n SemanticMapping(\"IfcSpace\", OntologyType.IFC, \"Space\",\n OntologyType.COBIE, RelationType.EQUIVALENT, MappingConfidence.EXACT),\n\n # IFC to Uniclass\n SemanticMapping(\"IfcWall\", OntologyType.IFC, \"Ss_25\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcRoof\", OntologyType.IFC, \"Ss_30\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcSlab\", OntologyType.IFC, \"Ss_32\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcDoor\", OntologyType.IFC, \"Pr_35\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n SemanticMapping(\"IfcWindow\", OntologyType.IFC, \"Pr_40\",\n OntologyType.UNICLASS, RelationType.RELATED, MappingConfidence.HIGH),\n\n # IFC to MasterFormat\n SemanticMapping(\"IfcDoor\", OntologyType.IFC, \"08\",\n OntologyType.MASTERFORMAT, RelationType.BROADER, MappingConfidence.MEDIUM),\n SemanticMapping(\"IfcWindow\", OntologyType.IFC, \"08\",\n OntologyType.MASTERFORMAT, RelationType.BROADER, MappingConfidence.MEDIUM),\n ]\n return rules\n\n def _build_synonym_map(self) -> Dict[str, List[str]]:\n \"\"\"Build synonym mappings for fuzzy matching\"\"\"\n return {\n \"wall\": [\"partition\", \"barrier\", \"divider\"],\n \"door\": [\"entrance\", \"portal\", \"opening\"],\n \"window\": [\"glazing\", \"fenestration\", \"opening\"],\n \"floor\": [\"slab\", \"deck\", \"storey\", \"level\"],\n \"roof\": [\"roofing\", \"covering\", \"canopy\"],\n \"beam\": [\"girder\", \"joist\", \"lintel\"],\n \"column\": [\"pillar\", \"post\", \"pier\"],\n \"stair\": [\"stairway\", \"staircase\", \"steps\"],\n \"space\": [\"room\", \"area\", \"zone\"],\n \"concrete\": [\"cement\", \"reinforced\"],\n \"steel\": [\"metal\", \"iron\"],\n }\n\n def map_field(\n self,\n field_name: str,\n field_value: str,\n source_ontology: Optional[OntologyType] = None,\n target_ontology: OntologyType = OntologyType.IFC\n ) -> MappingResult:\n \"\"\"\n Map a single field to target ontology.\n\n Args:\n field_name: Name of the field\n field_value: Value to map\n source_ontology: Source ontology if known\n target_ontology: Target ontology to map to\n\n Returns:\n Mapping result with possible matches\n \"\"\"\n mappings = []\n\n # Normalize the value\n normalized = self._normalize_value(field_value)\n\n # Check direct matches in existing rules\n for rule in self.mapping_rules:\n if rule.target_ontology == target_ontology:\n if self._matches(normalized, rule.source_concept):\n mappings.append(rule)\n\n # Check target ontology directly\n target_concepts = self.ontologies.get(target_ontology, {})\n for concept_id, concept in target_concepts.items():\n similarity = self._calculate_similarity(normalized, concept)\n if similarity > 0.5:\n confidence = self._similarity_to_confidence(similarity)\n mappings.append(SemanticMapping(\n source_concept=field_value,\n source_ontology=source_ontology or OntologyType.CUSTOM,\n target_concept=concept_id,\n target_ontology=target_ontology,\n relation=RelationType.EQUIVALENT if similarity > 0.9 else RelationType.RELATED,\n confidence=confidence\n ))\n\n # Sort by confidence\n confidence_order = [\n MappingConfidence.EXACT,\n MappingConfidence.HIGH,\n MappingConfidence.MEDIUM,\n MappingConfidence.LOW,\n MappingConfidence.UNCERTAIN\n ]\n mappings.sort(key=lambda m: confidence_order.index(m.confidence))\n\n return MappingResult(\n source_field=field_name,\n source_value=field_value,\n mappings=mappings,\n best_match=mappings[0] if mappings else None,\n unmapped=len(mappings) == 0\n )\n\n def _normalize_value(self, value: str) -> str:\n \"\"\"Normalize a value for matching\"\"\"\n # Remove common prefixes\n prefixes = [\"ifc\", \"cobie\", \"type\", \"element\"]\n normalized = value.lower().strip()\n\n for prefix in prefixes:\n if normalized.startswith(prefix):\n normalized = normalized[len(prefix):]\n\n return normalized.strip(\"_- \")\n\n def _matches(self, value: str, concept: str) -> bool:\n \"\"\"Check if value matches concept\"\"\"\n normalized_value = self._normalize_value(value)\n normalized_concept = self._normalize_value(concept)\n return normalized_value == normalized_concept\n\n def _calculate_similarity(\n self,\n value: str,\n concept: OntologyConcept\n ) -> float:\n \"\"\"Calculate similarity between value and concept\"\"\"\n value_lower = value.lower()\n concept_name_lower = concept.name.lower()\n concept_id_lower = concept.id.lower()\n\n # Exact match\n if value_lower == concept_name_lower or value_lower == concept_id_lower:\n return 1.0\n\n # Partial match in name\n if value_lower in concept_name_lower or concept_name_lower in value_lower:\n return 0.8\n\n # Check synonyms\n for key, synonyms in self.synonym_map.items():\n if key in value_lower:\n if key in concept_name_lower:\n return 0.9\n for syn in synonyms:\n if syn in concept_name_lower:\n return 0.7\n\n # Definition match\n if concept.definition:\n if value_lower in concept.definition.lower():\n return 0.6\n\n return 0.0\n\n def _similarity_to_confidence(self, similarity: float) -> MappingConfidence:\n \"\"\"Convert similarity score to confidence level\"\"\"\n if similarity >= 0.95:\n return MappingConfidence.EXACT\n elif similarity >= 0.8:\n return MappingConfidence.HIGH\n elif similarity >= 0.6:\n return MappingConfidence.MEDIUM\n elif similarity >= 0.4:\n return MappingConfidence.LOW\n else:\n return MappingConfidence.UNCERTAIN\n\n def map_schema(\n self,\n schema: Dict[str, List[str]],\n target_ontology: OntologyType = OntologyType.IFC\n ) -> OntologyMappingReport:\n \"\"\"\n Map entire schema to target ontology.\n\n Args:\n schema: Dictionary of field names to sample values\n target_ontology: Target ontology\n\n Returns:\n Complete mapping report\n \"\"\"\n all_mappings = []\n confidence_dist = {c.value: 0 for c in MappingConfidence}\n\n for field_name, sample_values in schema.items():\n # Use first sample value\n value = sample_values[0] if sample_values else field_name\n\n result = self.map_field(field_name, value, target_ontology=target_ontology)\n all_mappings.append(result)\n\n if result.best_match:\n confidence_dist[result.best_match.confidence.value] += 1\n\n mapped = sum(1 for m in all_mappings if not m.unmapped)\n unmapped = len(all_mappings) - mapped\n coverage = mapped / len(all_mappings) if all_mappings else 0\n\n recommendations = self._generate_recommendations(all_mappings, coverage)\n\n return OntologyMappingReport(\n total_fields=len(all_mappings),\n mapped_fields=mapped,\n unmapped_fields=unmapped,\n mappings=all_mappings,\n coverage=coverage,\n confidence_distribution=confidence_dist,\n recommendations=recommendations\n )\n\n def _generate_recommendations(\n self,\n mappings: List[MappingResult],\n coverage: float\n ) -> List[str]:\n \"\"\"Generate recommendations for improving mappings\"\"\"\n recommendations = []\n\n if coverage < 0.7:\n recommendations.append(\n f\"Low mapping coverage ({coverage:.0%}). Consider adding custom mappings.\"\n )\n\n low_confidence = [m for m in mappings\n if m.best_match and m.best_match.confidence\n in [MappingConfidence.LOW, MappingConfidence.UNCERTAIN]]\n if low_confidence:\n recommendations.append(\n f\"{len(low_confidence)} mappings have low confidence. Review manually.\"\n )\n\n unmapped = [m for m in mappings if m.unmapped]\n if unmapped:\n fields = [m.source_field for m in unmapped[:5]]\n recommendations.append(\n f\"Unmapped fields: {', '.join(fields)}. Add custom mappings.\"\n )\n\n return recommendations\n\n def create_mapping(\n self,\n source: str,\n source_ontology: OntologyType,\n target: str,\n target_ontology: OntologyType,\n relation: RelationType = RelationType.EQUIVALENT,\n notes: Optional[str] = None\n ) -> SemanticMapping:\n \"\"\"Create a new manual mapping\"\"\"\n mapping = SemanticMapping(\n source_concept=source,\n source_ontology=source_ontology,\n target_concept=target,\n target_ontology=target_ontology,\n relation=relation,\n confidence=MappingConfidence.EXACT,\n notes=notes,\n created_by=\"manual\"\n )\n self.mapping_rules.append(mapping)\n return mapping\n\n def export_mappings(self, format: str = \"json\") -> str:\n \"\"\"Export all mappings\"\"\"\n if format == \"json\":\n mappings_data = []\n for rule in self.mapping_rules:\n mappings_data.append({\n \"source\": rule.source_concept,\n \"source_ontology\": rule.source_ontology.value,\n \"target\": rule.target_concept,\n \"target_ontology\": rule.target_ontology.value,\n \"relation\": rule.relation.value,\n \"confidence\": rule.confidence.value\n })\n return json.dumps(mappings_data, indent=2)\n else:\n raise ValueError(f\"Unsupported format: {format}\")\n\n def generate_report(self, report: OntologyMappingReport) -> str:\n \"\"\"Generate mapping report\"\"\"\n output = f\"\"\"\n# Ontology Mapping Report\n\n## Summary\n- **Total Fields:** {report.total_fields}\n- **Mapped Fields:** {report.mapped_fields}\n- **Unmapped Fields:** {report.unmapped_fields}\n- **Coverage:** {report.coverage:.0%}\n\n## Confidence Distribution\n\"\"\"\n for conf, count in report.confidence_distribution.items():\n if count > 0:\n output += f\"- **{conf.title()}:** {count}\\n\"\n\n output += \"\\n## Recommendations\\n\"\n for rec in report.recommendations:\n output += f\"- {rec}\\n\"\n\n output += \"\\n## Mappings\\n\"\n for mapping in report.mappings[:20]:\n status = \"✓\" if not mapping.unmapped else \"✗\"\n target = mapping.best_match.target_concept if mapping.best_match else \"unmapped\"\n conf = mapping.best_match.confidence.value if mapping.best_match else \"-\"\n output += f\"- {status} {mapping.source_field}: {mapping.source_value} → {target} ({conf})\\n\"\n\n return output\n\nCommon Use Cases\nMap Field to IFC\nmapper = OntologyMapper()\n\n# Map a single field\nresult = mapper.map_field(\n field_name=\"element_type\",\n field_value=\"Wall\",\n target_ontology=OntologyType.IFC\n)\n\nif result.best_match:\n print(f\"Mapped to: {result.best_match.target_concept}\")\n print(f\"Confidence: {result.best_match.confidence.value}\")\n\nMap Entire Schema\n# Define schema with sample values\nschema = {\n \"element_type\": [\"Wall\", \"Door\", \"Window\"],\n \"level\": [\"Level 1\", \"Level 2\"],\n \"material\": [\"Concrete\", \"Steel\"],\n \"room_type\": [\"Office\", \"Corridor\"]\n}\n\nreport = mapper.map_schema(schema, target_ontology=OntologyType.IFC)\n\nprint(f\"Coverage: {report.coverage:.0%}\")\nprint(f\"Mapped: {report.mapped_fields}/{report.total_fields}\")\n\nCreate Custom Mappings\n# Add custom mapping\nmapper.create_mapping(\n source=\"CustomWallType\",\n source_ontology=OntologyType.CUSTOM,\n target=\"IfcWall\",\n target_ontology=OntologyType.IFC,\n relation=RelationType.EQUIVALENT,\n notes=\"Custom wall type from legacy system\"\n)\n\nQuick Reference\nComponent\tPurpose\nOntologyMapper\tMain mapping engine\nOntologyType\tStandard ontologies (IFC, COBie, etc.)\nSemanticMapping\tMapping between concepts\nMappingResult\tResult of mapping operation\nRelationType\tRelationship types\nMappingConfidence\tConfidence levels\nResources\nBook: \"Data-Driven Construction\" by Artem Boiko, Chapter 2.2\nWebsite: https://datadrivenconstruction.io\nNext Steps\nUse open-data-integrator for open data\nUse data-model-designer for schema design\nUse bim-validation-pipeline for validation" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/datadrivenconstruction/ontology-mapper", "publisherUrl": "https://clawhub.ai/datadrivenconstruction/ontology-mapper", "owner": "datadrivenconstruction", "version": "2.1.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/ontology-mapper", "downloadUrl": "https://openagent3.xyz/downloads/ontology-mapper", "agentUrl": "https://openagent3.xyz/skills/ontology-mapper/agent", "manifestUrl": "https://openagent3.xyz/skills/ontology-mapper/agent.json", "briefUrl": "https://openagent3.xyz/skills/ontology-mapper/agent.md" } }