Ontology Spatial Query System Using Geometric Mapping
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Solution Overview
Problem
Existing medical ontologies, such as SNOMED, are limited in their ability to extract spatial information related to anatomical structures, primarily relying on 'is part of' and 'is a' relations, which are insufficient for complex queries like those in radiation therapy, and lack geometric or metric information.
Innovation Solution
An information retrieval system that maps queries to hierarchic graph data structures, linking linguistic descriptors to geometric models to compute new spatial or metric relations, allowing dynamic evaluation of spatial queries and integration of geometric data into the ontology, enabling the derivation of novel spatial relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional medical ontologies (SNOMED) are used with limited relation types, then the ontology structure remains simple and manageable, but the ability to extract spatial information and answer complex medical queries is insufficient
Solution Approach 1:
The system segments the complex spatial reasoning task into multiple independent relation types (spatial, metric, topological, directional) that can be processed separately. Each relation type is handled by dedicated mapping components, allowing the system to extract comprehensive spatial information without overwhelming the ontology structure with a single complex relation mechanism
Solution Approach 2:
The patent adds a new dimension to traditional ontologies by integrating geometric models and spatial coordinates alongside the existing hierarchical concept structure. This transforms the ontology from a purely conceptual framework to a multi-dimensional system that combines linguistic descriptors with spatial and metric properties, enabling complex spatial queries without fundamentally altering the core ontology architecture
2Measurement precision
If geometric models are integrated with ontologies to enable spatial queries, then spatial information extraction is enhanced, but the system complexity and computational requirements increase
Solution Approach 1:
The patent introduces intermediary mapping components (concept mapper, metric mapper, geo-mapper) that serve as mediators between the ontology and geometric models. These mappers translate queries and data between different representations, enabling precise spatial computation without requiring direct integration of geometric complexity into the ontology core. The intermediaries manage the computational burden and simplify the system architecture
Solution Approach 2:
The integrated system achieves multi-functionality by combining ontology-based conceptual reasoning with geometric-based spatial computation in a unified framework. The same system can handle both traditional medical concept queries and complex spatial-metric queries, eliminating the need for separate systems and reducing overall architectural complexity despite the enhanced capabilities
3Adaptability or versatility
If static ontology entries are used for spatial relations, then the ontology remains stable and easy to maintain, but the system cannot dynamically evaluate new spatial or metric queries
Solution Approach 1:
The system implements dynamics by introducing algorithmic spatial relations that can be executed at query time rather than pre-computed and stored. The metric component dynamically evaluates spatial and metric relations based on current geometric model data, allowing the system to adapt to new queries and data without requiring static pre-definition of all possible relations. This maintains ontology stability while enabling versatile query evaluation
Solution Approach 2:
The system enables self-service by allowing queries to automatically trigger the appropriate mapping and computation processes. When a spatial or metric query is received, the system autonomously routes it through the concept mapper, metric mapper, and geo-mapper components, which self-organize to evaluate the query using the integrated ontology and geometric models without requiring manual configuration or intervention
Data Source
AI summary
An information retrieval system (IPS). The system comprises an input interface (IN) for receiving a query related to an object of interest. A concept mapper (CM) is configured to map the query to one or more associated concept entries of a hierarchic graph data structure (ONTO). The entries in said structure encode linguistic descriptors of components of a model (GM) for said object (OB). A metric-mapper (MM) is configured to map the query to one or more metric relationship descriptors. A geo-mapper (GEO) is configured to map said concept entries against the geometric model linked to the hierarchic graph data structure to obtain spatio-numerical data associated with said linguistic descriptors. A metric component (MTC) is configured to compute one or more metric or spatial relationships between said object components based on the spatio-numerical data and the one or more metric relationship descriptors.