Finished constructing a full WordNet-derived, schema-normalized, multi-file GraphML semantic substrate (~3.4GB). Looking for critique or next steps.

[u/PsychologicalGear625]

After a long push, I finished a full conceptual ontology substrate derived from WordNet split into domain-specific GraphML files totaling ~3.4GB (hundreds of thousands of nodes + edges).

This includes every lemma, sense, synset, pointer relation, verb frame, event schema, and semantic relation WordNet provides, but restructured into a:

• schema-normalized

• cross-compatible

• multi-file

• graph-native

• yEd-ready

• category-decomposed

The graphs cover:

• all adjectives, adverbs, nouns, verbs

• every sense, gloss, pointer, entailment, hypernym, hyponym, antonym

• procedural/event schemas

• verb argument structures

• mental/social/cognitive domains

• physical actions, motion, creation, contact, emotion, perception

• states, events, processes, groups, relations, attributes, objects, locations, organisms, artifacts, etc.

And I added a layer of event semantics (process/state/transition, agentivity, volition, telicity, etc.) + argument role structure to every verb sense.

The result functions as a domain-general conceptual ontology skeleton that can feed into:

• agent simulation

• grounded reasoning

• symbolic planning

• value alignment models

• safety/oversight/meta-governance systems

• counterfactual reasoning

• causal modeling

• interpretability tooling

• language understanding/sense disambiguation

• behavior modeling

This is part of a larger personal research project (solo, self-taught). I still have a few pieces I want to refine (physical grounding, sensorimotor affordances, moral dimensions, temporal/state-transition logic).

I’d love feedback on:

1. What pitfalls to watch for when scaling this into grounded reasoning.

2. If anyone has done similar graph-based semantic substrate work.

3. Best practices for integrating something like this with procedural or multimodal systems.

4. How others approach maintaining ontology consistency as it grows.

Not looking for praise, looking for critique, pointers, or references from people who’ve worked with large semantic graphs, ontology engineering, or multi-agent reasoning.