RELICA

Identified fundamental limitations in conventional data modeling approaches. Extensive study of ontological frameworks leading to grounding of our approach in established semantic standards. Developed theoretical framework for unified representation of objects, aspects, states, and causal relationships.

Fact store with graph-based persistence

PostgreSQL based semantic triple storage optimized for ontological operations and traversal

Layered service architecture

Fact access, semantic mapping, API gateway, session management

Declarative query system

Datalog-inspired pull specifications for flexible graph traversal and object materialization

Semantic caching layer

Performance optimization for frequently accessed ontological structures

Persistent subgraph environments

Isolated workspaces for ontology development, testing, and application contexts

Administrative interface

Ontological development and model browsing

API stabilization and documentation

Consistent endpoint design and comprehensive developer reference

Object-Semantic Mapper (OSM)

Core mechanism for bidirectional semantic transformation between application objects and fact sub-graphs

Dual frontend/backend SDK

Shared type definitions across client and server with unified specification format

Temporal modeling support

State-based and fact-validity approaches for tracking change over time

Occurrence modeling with causal chains

Representing events, activities, and their causal relationships within the semantic model

Aspect classification hierarchies

State transition patterns and systematic aspect management across object lifecycles

Meta-spatio-temporal modeling framework

SDK support for objects, aspects, states, occurrences, and causality as first-class constructs

Validation with non-trivial modeling scenarios

Real-world complexity testing including spatial positioning, temporal sequences, and multi-entity relationships

Client SDK ergonomics improvements

Streamlined developer experience with clearer APIs and better error handling

Scratch ontology environments

Client-side development and experimentation without affecting production models

Additional SDK language support

Beyond TypeScript to broaden platform accessibility (e.g. Python)

Administrative interface enhancements

Improved model browsing, ontology workflows, visual development tools, user management, and access control

Query optimization and batch operations

Performance improvements for large-scale ontological operations

Developer preview release

Public access to platform with documentation, examples, and community support channels

Hosted platform

Managed infrastructure for teams and organizations

Reference applications

Demonstration applications showcasing platform development patterns and capabilities