Ontology Embedding for Virtual Environment Transformation
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Solution Overview
Problem
Existing processes for transforming real-world operations into immersive virtual environments are inefficient, as they often require recreating content from scratch, dismissing existing knowledge and leading to incomplete and error-prone implementations due to the complexity and computational expense of generating and validating domain models.
Innovation Solution
The method involves determining a source ontology for a real-world environment, generating embeddings using machine learning techniques, and refining them using neuro-symbolic AI to create a target ontology for the virtual environment, enabling a systematic and comprehensive transformation while preserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain models are generated and validated from scratch for virtual environment transformation, then transformation completeness can be improved, but computational expense and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by generating embeddings of source domain ontologies before the actual transformation process. These embeddings capture the semantic structure and relationships of the source domain, allowing the system to quickly map concepts to the target virtual environment without regenerating domain models from scratch during transformation.
Solution Approach 2:
The system creates simplified copies of source domain knowledge in the form of embeddings and knowledge graphs. Instead of recreating complete domain models for each transformation, the system uses these compressed representations to efficiently transfer and adapt knowledge to the target domain, significantly reducing computational overhead.
2Measurement precision
If domain models are generated and validated from scratch, then transformation accuracy can be improved, but computational resources required increase
Solution Approach 1:
The system extracts only the essential semantic information from source domain ontologies to create compact embedding representations. This extraction process removes redundant details while preserving the core conceptual structure, enabling accurate transformations with minimal computational resources.
Solution Approach 2:
The system transforms ontologies into a different parameter representation (embeddings) that captures the same semantic information in a more efficient format. This parameter transformation allows the system to maintain transformation accuracy while reducing the computational complexity of subsequent mapping operations.
3Reliability
If existing knowledge is dismissed and content is recreated from scratch, then transformation reliability can be improved, but loss of information increases
Solution Approach 1:
The system uses embeddings as an intermediary representation that bridges the source and target domains. This intermediary captures the semantic essence of existing knowledge in a format that can be easily mapped to the target virtual environment, preventing information loss while ensuring transformation reliability.
Solution Approach 2:
The system creates a composite representation combining source ontology structure with target domain context. This composite knowledge structure integrates existing domain knowledge with new virtual environment requirements, preserving valuable information while adapting to the target domain.
Data Source
AI summary
A domain transformation system may determine a source ontology for a source domain of a first environment. The source domain relates to a process performed in the first environment. The process may be transformed from the source domain to a target domain of a second environment, such as a virtual environment. The domain transformation system may determine a first portion of a target ontology for the target domain. The domain transformation system may generate, using a machine learning technique, a first embedding of the source ontology and a second embedding of the target ontology. The domain transformation system may generate a joint embedding based on the first embedding and the second embedding domain transformation system and may determine a second portion of the target ontology, based on the joint embedding, using a transfer learning technique. The domain transformation system may refine the target ontology using a neuro-symbolic artificial intelligence technique.