Building a Polymer Knowledge Graph with WORLD Platform

Scenario

A materials scientist wants to build a knowledge graph of polymers with their properties from the RadonPy dataset, enabling semantic queries and relationship discovery across 1,070 polymer structures.

Interactive Dialog

USER:

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

ASSISTANT:

[Calls: world_dataset_list]

RadonPy Dataset Overview

The RadonPy dataset contains molecular simulation data for **1,070 amorphous polymers**. **File**: PI1070.csv (1.6 MB) **Columns**: 157 properties including: - Structural (SMILES, molecular weight) - Quantum mechanical (HOMO, LUMO, dipole) - Thermal (Cp, Cv, thermal conductivity) - Mechanical (density, bulk modulus) - Optical (refractive index)

Citation

Hayashi et al. (2022) "RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics" npj Computational Materials DOI: https://doi.org/10.1038/s41524-022-00906-4

USER:

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

ASSISTANT:

[Calls: world_thing_add]

Polymers Added to Knowledge Graph

Successfully added 6 engineering plastics as entities: | Polymer | SMILES | Class | |---------|--------|-------| | Polyethylene | *CC* | Polyolefin | | Polypropylene | *CC(C)* | Polyolefin | | Polystyrene | *CC(c1ccccc1)* | Vinyl | | PMMA | *CC(C)(C(=O)OC)* | Acrylic | | PET | *OC(=O)c1ccc(C(=O)OCC)cc1* | Polyester | | Nylon-6 | *NCCCCCC(=O)* | Polyamide |

USER:

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

ASSISTANT:

[Calls: world_relation_add]

Building Polymer Ontology

Created semantic relationships in the knowledge graph: - **is_a**: Classification hierarchy (polymer to class) - **similar_to**: Structural similarity between polymers These relations enable reasoning like: "Find all polyolefins" or "What is similar to PET?"

USER:

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

ASSISTANT:

[Calls: world_sparql_query]

SPARQL Query

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

Query Results

| polymer | similar | |---------|--------| | Polyethylene | Polypropylene | | Polypropylene | - |

Insight

SPARQL enables powerful semantic queries across the polymer knowledge graph. You can ask complex questions like: - "Find polymers similar to PET" - "List all polyamides with their properties" - "What classes exist in the ontology?"

USER:

Show me the current state of the knowledge graph.

ASSISTANT:

[Calls: world_stats]

Knowledge Graph Statistics

| Metric | Value | |--------|-------| | Total Things | 9 | | Total Triples | 17 | | Polymers | 6 | | Classes | 3 |

Graph Visualization

``` Polyolefin / \ PE --- PP Vinyl Acrylic Polyester Polyamide | | | | PS PMMA PET --- Nylon-6 ```

USER:

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

ASSISTANT:

[Calls: world_save]

Knowledge Graph Saved

The polymer knowledge graph has been exported to `polymer_ontology.ttl`. This file can be: - Loaded back into WORLD with `world_load` - Imported into triple stores (Apache Jena, Blazegraph, GraphDB) - Shared with collaborators - Version controlled in Git - Queried with standard SPARQL tools

Sample TTL Output

```turtle @prefix world: . @prefix rdfs: . 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 . ```

Learning Outcomes

Authenticate and connect to WORLD platform

Used JWT authentication to access WORLD JSON-RPC API

Browse installed datasets

Listed RadonPy dataset with 1,070 polymers and 157 properties

Create knowledge graph entities

Added 6 polymer entities with SMILES and descriptions

Build semantic relationships

Created is_a and similar_to relations for polymer ontology

Execute SPARQL queries

Queried the graph for polyolefins using SPARQL SELECT

Export knowledge graph

Saved to Turtle format for sharing and interoperability

Key Concepts

Knowledge Graph

A graph-structured database storing entities (things) and their relationships, enabling semantic reasoning and discovery beyond tabular data

SPARQL

Query language for RDF graphs, using triple patterns (subject-predicate-object) to find matching data

Turtle (TTL)

Human-readable serialization format for RDF, using prefix:name notation (e.g., world:Polyethylene)

Ontology

Formal specification of concepts and relationships in a domain, enabling machines to reason about polymer chemistry

Triple

Basic unit of RDF: (subject, predicate, object) - e.g., (Polyethylene, is_a, Polyolefin)

Semantic Relationship

Typed connection between entities (is_a, similar_to, has_property) that captures meaning and enables inference

Tools Working

OK world_dataset_list Listed RadonPy dataset with full metadata
OK world_thing_add Added 6 polymer entities to graph
OK world_relation_add Created 8 semantic relations
OK world_sparql_query Executed SPARQL SELECT successfully
OK world_stats Retrieved graph statistics (17 triples)
OK world_save Exported to polymer_ontology.ttl