Sparse Feature Encoding for Multimodal Ontology
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Solution Overview
Problem
Existing AI systems lack the ability to utilize highly detailed real-world knowledge, relying on manual datasets and lacking contextual information, which makes them inflexible and prone to errors. Additionally, they struggle with understanding complex relationships and salient features in multimodal data.
Innovation Solution
A method and system that acquire multimodal sensory data, extract salient features, and create data structures to map spatial relationships among objects. This system builds an ontology with typical relationships among object classes and establishes axioms to encode specific and general relationships, enabling deductive reasoning to answer queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual ontological engineering is used to fill knowledge gaps, then knowledge completeness is improved, but labor cost and time consumption increase
Solution Approach 1:
The system automatically extracts salient features from multimodal data and constructs ontology elements without human intervention. The AI system performs self-service by autonomously building knowledge graphs from raw sensory data, eliminating the need for manual ontological engineering while maintaining knowledge completeness.
Solution Approach 2:
Manual labor in ontological engineering is replaced by an automated AI system that processes multimodal data and generates ontology structures. The mechanical process of manual knowledge construction is substituted with an automated computational system that extracts features and builds knowledge graphs autonomously.
2Measurement precision
If the number of training data is increased to improve understanding of complex relationships, then accuracy is improved, but data processing complexity and cost increase
Solution Approach 1:
The system extracts only the most important and relevant features from multimodal data rather than processing all available data. By identifying and extracting salient features that are critical for understanding complex relationships, the system achieves high accuracy while reducing data processing complexity and computational costs.
Solution Approach 2:
The system applies different processing strategies to different types of data and features based on their importance. Rather than uniformly processing all training data, it focuses computational resources on extracting and processing the most critical features that contribute to understanding complex relationships, thereby improving efficiency.
3Device complexity
If existing AI systems use broad databases without contextual information, then system simplicity is maintained, but flexibility and accuracy decrease
Solution Approach 1:
The system pre-processes multimodal data to extract and encode contextual information in the form of salient features before the actual query processing. By performing this feature extraction and contextual encoding in advance, the system maintains operational simplicity during query execution while having rich contextual information available for flexible and accurate responses.
Data Source
AI summary
Systems and methods for answering queries by applying deductive reasoning using a data structure based on knowledge derived from sensory data are provided. A method may include acquiring multimodal sensory data and extracting a plurality of salient features of the one or more objects. Data structures that associate one or more essential characteristics with each of the plurality of salient features may be created and the spatial relationships among the one or more objects in visual scenes may be mapped. An ontology that encompasses the typical relationships among the classes of objects in a plurality of datasets may be created and one or more axioms may be established.