OntoGPT

Generation of Ontologies and Knowledge Bases using GPT

A knowledge extraction tool that uses a large language model to extract semantic information from text.

This exploits the ability of ultra-LLMs such as GPT-3 to return user-defined data structures as a response.

Currently there are two different pipelines implemented:

• SPIRES: Structured Prompt Interrogation and Recursive Extraction of Semantics
  • Zero-shot learning approach to extracting nested semantic structures from text
  • Inputs: LinkML schema + text
  • Outputs: JSON, YAML, or RDF or OWL that conforms to the schema
  • Uses text-davinci-003
• HALO: HAllucinating Latent Ontologies
  • Few-shot learning approach to generating/hallucinating a domain ontology given a few examples
  • Uses code-davinci-002

SPIRES: Usage

Given a short text abstract.txt with content such as:

The cGAS/STING-mediated DNA-sensing signaling pathway is crucial for interferon (IFN) production and host antiviral responses

... [snip] ...

The underlying mechanism was the interaction of US3 with β-catenin and its hyperphosphorylation of β-catenin at Thr556 to block its nuclear translocation ... ...

(see full input)

We can extract this into the GO pathway datamodel:

ontogpt extract -t gocam.GoCamAnnotations abstract.txt

Giving schema-compliant yaml such as:

genes:
- HGNC:2514
- HGNC:21367
- HGNC:27962
- US3
- FPLX:Interferon
- ISG
gene_gene_interactions:
- gene1: US3
  gene2: HGNC:2514
gene_localizations:
- gene: HGNC:2514
  location: Nuclear
gene_functions:
- gene: HGNC:2514
  molecular_activity: Transcription
- gene: HGNC:21367
  molecular_activity: Production
...

See full output

note in the above the grounding is very preliminary and can be improved. Ungrounded NamedEntities appear as text.

How it works

1. You provide an arbitrary data model, describing the structure you want to extract text into
   • this can be nested (but see limitations below)
2. provide your preferred annotations for grounding NamedEntity fields
3. ontogpt will:
   • generate a prompt
   • feed the prompt to a language model (currently OpenAI)
   • parse the results into a dictionary structure
   • ground the results using a preferred annotator

Pre-requisites

• python 3.9+
• an OpenAI account
• a BioPortal account (optional, for grounding)

You will need to set both API keys using OAK (which is a dependency of this project)

poetry run runoak set-apikey openai <your openai api key>
poetry run runoak set-apikey bioportal <your bioportal api key>

How to define your own extraction data model

Step 1: Define a schema

See src/ontogpt/templates/ for examples.

Define a schema (using a subset of LinkML) that describes the structure you want to extract from your text.

classes:
  MendelianDisease:
    attributes:
      name:
        description: the name of the disease
        examples:
          - value: peroxisome biogenesis disorder
        identifier: true  ## needed for inlining
      description:
        description: a description of the disease
        examples:
          - value: >-
             Peroxisome biogenesis disorders, Zellweger syndrome spectrum (PBD-ZSS) is a group of autosomal recessive disorders affecting the formation of functional peroxisomes, characterized by sensorineural hearing loss, pigmentary retinal degeneration, multiple organ dysfunction and psychomotor impairment
      synonyms:
        multivalued: true
        examples:
          - value: Zellweger syndrome spectrum
          - value: PBD-ZSS
      subclass_of:
        multivalued: true
        range: MendelianDisease
        examples:
          - value: lysosomal disease
          - value: autosomal recessive disorder
      symptoms:
        range: Symptom
        multivalued: true
        examples:
          - value: sensorineural hearing loss
          - value: pigmentary retinal degeneration
      inheritance:
        range: Inheritance
        examples:
          - value: autosomal recessive
      genes:
        range: Gene
        multivalued: true
        examples:
          - value: PEX1
          - value: PEX2
          - value: PEX3

  Gene:
    is_a: NamedThing
    id_prefixes:
      - HGNC
    annotations:
      annotators: gilda:, bioportal:hgnc-nr

  Symptom:
    is_a: NamedThing
    id_prefixes:
      - HP
    annotations:
      annotators: sqlite:obo:hp

  Inheritance:
    is_a: NamedThing
    annotations:
      annotators: sqlite:obo:hp

• the schema is defined in LinkML
• prompt hints can be specified using the prompt annotation (otherwise description is used)
• multivalued fields are supported
• the default range is string - these are not grounded. E.g. disease name, synonyms
• define a class for each NamedEntity
• for any NamedEntity, you can specify a preferred annotator using the annotators annotation

We recommend following an established schema like biolink, but you can define your own.

Step 2: Compile the schema

Run the make command at the top level. This will compile the schema to pedantic

Step 3: Run the command line

e.g.

ontogpt extract -t  mendelian_disease.MendelianDisease marfan-wikipedia.txt

Web Application

There is a bare bones web application

poetry run web-ontogpt

Note that the agent running uvicorn must have the API key set, so for obvious reasons don't host this publicly without authentication, unless you want your credits drained.

Features

Multiple Levels of nesting

Currently no more than two levels of nesting are recommended.

If a field has a range which is itself a class and not a primitive, it will attempt to nest

E.g. the gocam schema has an attribute:

  attributes:
      ...
      gene_functions:
        description: semicolon-separated list of gene to molecular activity relationships
        multivalued: true
        range: GeneMolecularActivityRelationship

Because GeneMolecularActivityRelationship is inlined it will nest

The generated prompt is:

gene_functions : <semicolon-separated list of gene to molecular activities relationships>

The output of this is then passed through further SPIRES iterations.

Text length limit

Currently SPIRES must use text-davinci-003, which has a total 4k token limit (prompt + completion).

You can pass in a parameter to split the text into chunks, results will be recombined automatically, but more experiments need to be done to determined how reliable this is.

## HALOE: Usage

TODO

## Limitations

### Non-deterministic

This relies on an existing LLM, and LLMs can be fickle in their responses.

### Coupled to OpenAI

You will need an openai account. In theory any LLM can be used but in practice the parser is tuned for OpenAI



# Acknowledgements

This [cookiecutter](https://cookiecutter.readthedocs.io/en/stable/README.html) project was developed from the [sphintoxetry-cookiecutter](https://github.com/hrshdhgd/sphintoxetry-cookiecutter) template and will be kept up-to-date using [cruft](https://cruft.github.io/cruft/).