10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Map construction data to standard ontologies. Create semantic mappings between different data schemas
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skillhub install datadrivenconstruction/ontology-mapperExaminez la source et la compatibilité, puis suivez les étapes d’installation.
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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.
Book Reference: "Доминирование открытых данных" / "Open Data Dominance"
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Set, Tuple
from datetime import datetime
import json
import re
class OntologyType(Enum):
"""Standard construction ontologies"""
IFC = "ifc" # Industry Foundation Classes
COBIE = "cobie" # Construction Operations Building Information Exchange
UNICLASS = "uniclass" # UK classification
OMNICLASS = "omniclass" # North American classification
MASTERFORMAT = "masterformat" # CSI MasterFormat
UNIFORMAT = "uniformat" # CSI UniFormat
CUSTOM = "custom" # Custom ontology
class MappingConfidence(Enum):
"""Confidence level of mapping"""
EXACT = "exact" # 100% match
HIGH = "high" # 90%+ match
MEDIUM = "medium" # 70-90% match
LOW = "low" # 50-70% match
UNCERTAIN = "uncertain" # <50% match
class RelationType(Enum):
"""Types of relationships between concepts"""
EQUIVALENT = "equivalent" # Same concept
BROADER = "broader" # Source is more specific
NARROWER = "narrower" # Source is more general
RELATED = "related" # Related but not equivalent
PART_OF = "part_of" # Component relationship
HAS_PART = "has_part" # Contains components
@dataclass
class OntologyConcept:
"""Concept in an ontology"""
id: str
name: str
ontology: OntologyType
definition: Optional[str] = None
parent_id: Optional[str] = None
synonyms: List[str] = field(default_factory=list)...
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# Ontology Mapper
## Overview
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.
**Book Reference:** "Доминирование открытых данных" / "Open Data Dominance"
## Quick Start
```python
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Set, Tuple
from datetime import datetime
import json
import re
class OntologyType(Enum):
"""Standard construction ontologies"""
IFC = "ifc" # Industry Foundation Classes
COBIE = "cobie" # Construction Operations Building Information Exchange
UNICLASS = "uniclass" # UK classification
OMNICLASS = "omniclass" # North American classification
MASTERFORMAT = "masterformat" # CSI MasterFormat
UNIFORMAT = "uniformat" # CSI UniFormat
CUSTOM = "custom" # Custom ontology
class MappingConfidence(Enum):
"""Confidence level of mapping"""
EXACT = "exact" # 100% match
HIGH = "high" # 90%+ match
MEDIUM = "medium" # 70-90% match
LOW = "low" # 50-70% match
UNCERTAIN = "uncertain" # <50% match
class RelationType(Enum):
"""Types of relationships between concepts"""
EQUIVALENT = "equivalent" # Same concept
BROADER = "broader" # Source is more specific
NARROWER = "narrower" # Source is more general
RELATED = "related" # Related but not equivalent
PART_OF = "part_of" # Component relationship
HAS_PART = "has_part" # Contains components
@dataclass
class OntologyConcept:
"""Concept in an ontology"""
id: str
name: str
ontology: OntologyType
definition: Optional[str] = None
parent_id: Optional[str] = None
synonyms: List[str] = field(default_factory=list)...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
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