# OARD-KG Chat Transcript

👤 **User**
Give an overview of the @oard-kg

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🧠 **Assistant**
Here's a comprehensive overview of the **OARD-KG** (Open Annotations for Rare Diseases Knowledge Graph):

**What it is.** The OARD-KG (Open Annotations for Rare Diseases Knowledge Graph) is an RDF triplestore maintained by the WengLab Informatics Research group at Columbia University. It surfaces clinically derived associations between rare diseases and phenotypes, extracted from real-world electronic health records using ontology mapping and natural language processing applied to billing codes, lab tests, and clinical narratives.

**Domain.** Rare disease · clinical phenotyping · health informatics. Identifiers come from three standard biomedical namespaces: MONDO (diseases), HPO (phenotypic features), and UMLS.

**Core entity types.** The graph has ten classes organized into three conceptual tiers:

- *Biomedical entities* — `Disease` and `PhenotypicFeature`, the subjects and objects of all clinical associations.
- *Associations* — `DiseaseToPhenotypicFeatureAssociation`, `PhenotypicFeatureToPhenotypicFeatureAssociation`, and `EntityToDiseaseAssociation`, linking entities with standardized Biolink predicates.
- *Evidence layer* — each association links to `Study` nodes, which in turn produce `ConceptCountAnalysisResult` (co-occurrence counts, concept pair counts, dataset counts) and `LogOddsAnalysisResult` (log odds ratio, 95% CI, sample size).

**What makes it useful.** Unlike curated ontology databases, OARD-KG is grounded in real patient records, so it captures rare disease–phenotype co-occurrences that appear in clinical practice rather than only those documented in the literature. The statistical evidence layer (log odds ratios with confidence intervals) lets researchers filter associations by strength and reproducibility, making it well-suited for phenotype-driven diagnosis, knowledgebase enrichment, and evidence synthesis.

**Access.** SPARQL endpoint at `https://apps.okn.us/oard-kg/sparql`, with a Linked Data Fragments interface at `https://apps.okn.us/ldf/oard-kg`.

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👤 **User**
Visualize the schema with the visualize_schema tool

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🧠 **Assistant**
Here's the cleaned schema diagram for OARD-KG:

```mermaid
classDiagram
direction TB

class Disease {
  string label
  string category
}

class PhenotypicFeature {
  string label
  string category
}

class Dataset {
  string label
  string category
}

class Study {
  string category
}

class DiseaseToPhenotypicFeatureAssociation {
  string predicate
  string category
}

class EntityToDiseaseAssociation {
  string predicate
  string category
}

class PhenotypicFeatureToPhenotypicFeatureAssociation {
  string predicate
  string category
}

class ConceptCountAnalysisResult {
  int concept_count_object
  int concept_count_subject
  int concept_pair_count
  int dataset_count
  string category
}

class LogOddsAnalysisResult {
  float log_odds_ratio
  float log_odds_ratio_95_ci
  int total_sample_size
  string category
}

Disease --> DiseaseToPhenotypicFeatureAssociation : subject
PhenotypicFeature --> DiseaseToPhenotypicFeatureAssociation : object
Disease --> EntityToDiseaseAssociation : object
PhenotypicFeature --> EntityToDiseaseAssociation : subject
PhenotypicFeature --> PhenotypicFeatureToPhenotypicFeatureAssociation : subject
PhenotypicFeature --> PhenotypicFeatureToPhenotypicFeatureAssociation : object

DiseaseToPhenotypicFeatureAssociation --> Study : has_supporting_studies
EntityToDiseaseAssociation --> Study : has_supporting_studies
PhenotypicFeatureToPhenotypicFeatureAssociation --> Study : has_supporting_studies

Study --> ConceptCountAnalysisResult : has_study_results
Study --> LogOddsAnalysisResult : has_study_results

ConceptCountAnalysisResult --> Dataset : supporting_data_set
```

The schema flows top-to-bottom through three tiers: biomedical entities (`Disease`, `PhenotypicFeature`, `Dataset`) at the top feed into three association classes, which each link to `Study` nodes, which produce two types of analysis results — `ConceptCountAnalysisResult` (co-occurrence stats) and `LogOddsAnalysisResult` (odds ratios with confidence intervals). No edges carry properties in this graph, so all relationships are simple labeled arrows.

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*Created by [mcp-proto-okn](https://github.com/sbl-sdsc/mcp-proto-okn) 0.6.0 on 2026-05-01*

*Model: claude-sonnet-4-6*
