Polymer Knowledge Graph

Build a semantic knowledge graph of polymer materials with WORLD platform. Created database with polyethylene, polypropylene, polycarbonate entities linked to PolyNC prediction model. Demonstrates thing creation, relations, and TTL export.

RadonPy dataset with 1,070 polymers
Semantic relationships (is_a, similar_to)
SPARQL queries for reasoning
Export to Turtle (TTL) format

Chat Dialogs

Explore RadonPy dataset

PROMPT: I want to explore the RadonPy polymer dataset. What's available in WORLD?

CONTEXT: RadonPy contains molecular simulation data for 1,070 amorphous polymers with 157 properties including structural, thermal, mechanical, and optical characteristics.

THINKING: I'll use world_dataset_list to show available datasets and their schemas.

RESULT: RadonPy dataset: PI1070.csv (1.6 MB) Contains 1,070 polymers with 157 properties: - Structural (SMILES, molecular weight) - Quantum (HOMO, LUMO, dipole) - Thermal (Cp, Cv, thermal conductivity) - Mechanical (density, bulk modulus)

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Add polymers to knowledge graph

PROMPT: Let's add some polymers to the knowledge graph. Start with common engineering plastics: Polyethylene, Polypropylene, Polystyrene, PMMA, PET, and Nylon-6.

CONTEXT: Each polymer becomes a "Thing" entity with properties like SMILES and class membership.

THINKING: Using world_thing_add to create entities with PSMILES and polymer class attributes.

RESULT: Added 6 engineering plastics: | Polymer | SMILES | Class | |---------------|---------------------|------------| | Polyethylene | *CC* | Polyolefin | | Polypropylene | *CC(C)* | Polyolefin | | Polystyrene | *CC(c1ccccc1)* | Vinyl | | PMMA | *CC(C)(C(=O)OC)* | Acrylic |

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Create semantic relationships

PROMPT: Now create relationships between the polymers based on their chemical families.

CONTEXT: Relations like "is_a" and "similar_to" enable semantic reasoning and SPARQL queries across the knowledge graph.

THINKING: Creating is_a relations for classification and similar_to for structural similarity.

RESULT: Created semantic relationships: - is_a: Classification hierarchy (polymer to class) - similar_to: Structural similarity between polymers Enables queries like "Find all polyolefins" or "What is similar to PET?"

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Query with SPARQL

PROMPT: Can you query the knowledge graph using SPARQL to find all polyolefins?

CONTEXT: SPARQL is the standard query language for semantic web and knowledge graphs.

THINKING: Writing SPARQL query to find polymers with is_a Polyolefin relation.

RESULT: Query: SELECT ?polymer WHERE { ?polymer world:is_a world:Polyolefin } Results: | polymer | similar | |---------------|-----------------| | Polyethylene | Polypropylene | | Polypropylene | - |

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Export to TTL format

PROMPT: Save this knowledge graph so I can share it with my team.

CONTEXT: Turtle (TTL) is a compact RDF serialization format compatible with triple stores like Apache Jena, Blazegraph, and GraphDB.

THINKING: Using world_save to export the knowledge graph to polymer_ontology.ttl.

RESULT: Exported to polymer_ontology.ttl - Load back with world_load - Import into triple stores - Share with collaborators - Version control in Git

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Technical Details

Dataset Query

INPUT:

{
  "tool": "world_dataset_list"
}

OUTPUT:

{
  "dataset": "RadonPy",
  "file": "PI1070.csv",
  "size": "1.6 MB",
  "polymers": 1070,
  "properties": 157
}

Add Things

INPUT:

{
  "tool": "world_thing_add",
  "things": [
    {"name": "Polyethylene", "smiles": "*CC*", "class": "Polyolefin"},
    {"name": "Polypropylene", "smiles": "*CC(C)*", "class": "Polyolefin"}
  ]
}

Create Relations

INPUT:

{
  "tool": "world_relation_add",
  "relations": [
    {"subject": "Polyethylene", "predicate": "is_a", "object": "Polyolefin"},
    {"subject": "Polyethylene", "predicate": "similar_to", "object": "Polypropylene"}
  ]
}

SPARQL Query

INPUT:

SELECT ?polymer ?similar
WHERE {
  ?polymer world:is_a world:Polyolefin .
  OPTIONAL { ?polymer world:similar_to ?similar }
}

TTL Export

OUTPUT:

@prefix world: <https://paramus.ai/world> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

world:Polyethylene a world:Polymer ;
    world:smiles "*CC*" ;
    world:is_a world:Polyolefin ;
    world:similar_to world:Polypropylene .

world:Polypropylene a world:Polymer ;
    world:smiles "*CC(C)*" ;
    world:is_a world:Polyolefin .

Tools Used

world_dataset_list world_thing_add world_relation_add world_sparql_query world_save

Outcomes

Created knowledge graph with 6 polymers and 3 classes
Established is_a and similar_to semantic relations
Successfully queried with SPARQL
Exported to portable TTL format

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