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NL2TRAPI

A prototype investigation of converting natural language questions to the TRAPI format

First draft plan

  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)
    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking
    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8

Environment Installation

Please install the necessary packages via Conda following the commands below:

# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner

Download Nodesynonymizer

Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.

Download ScispaCy training data

Please download the en_core_sci_lg model from here. In short, this can be accomplished with

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz

Run a draft NER class for biomedical concepts

# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

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A prototype investigation of converting natural language questions to the TRAPI format

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MIT license
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NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

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A prototype investigation of converting natural language questions to the TRAPI format
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Repository files navigation
NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

About
A prototype investigation of converting natural language questions to the TRAPI format
Resources
Readme
MIT license
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2 watching
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GitHub - RTXteam/NL2TRAPI: A prototype investigation of converting natural language questions to the TRAPI format · GitHub
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README.md
Repository files navigation
NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

About
A prototype investigation of converting natural language questions to the TRAPI format
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MIT license
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, 'i');
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(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
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try {
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README.md
Repository files navigation
NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

About
A prototype investigation of converting natural language questions to the TRAPI format
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env
env
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LICENSE
LICENSE
README.md
README.md
Repository files navigation
NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

About
A prototype investigation of converting natural language questions to the TRAPI format
Resources
Readme
MIT license
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Custom properties
Stars
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2 watching
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NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

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NL2TRAPI
A prototype investigation of converting natural language questions to the TRAPI format
First draft plan
  1. Named Entity Recognition (what is a concept and what representes a concrete bioentity)

    1. Sundar: what are the concepts and what are concrete bioentities
    2. Amy: What are the curies for these
  2. Chunyu & David: Named entity linking

    1. stuff like https://ieeexplore.ieee.org/document/6823700
    2. https://medium.com/analytics-vidhya/entity-linking-a-primary-nlp-task-for-information-extraction-22f9d4b90aa8
Environment Installation
Please install the necessary packages via Conda following the commands below:
# Clone the repo
git clone https://github.com/RTXteam/NL2TRAPI.git
cd NL2TRAPI
# Create a conda environment for YACHT
conda env create -f env/llm_ner.yaml
conda activate llm_ner
Download Nodesynonymizer
Please download the node synonymizer database from the arax-databases.rtx.ai and put it under the ./NodeSynonymizer folder. Pleae note that this needs you to have permission to access this server.
Download ScispaCy training data
Please download the en_core_sci_lg model from here. In short, this can be accomplished with
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_lg-0.5.3.tar.gz
Run a draft NER class for biomedical concepts
# Import librariesimportjsonfromsrcsimportTRAPI_NER## initalize trapi ner classtrapi_ner=TRAPI_NER(synonymizer_dir='./NodeSynonymizer', synonymizer_dbname='node_synonymizer_v1.1_KG2.8.0.1.sqlite', linker_name= ['umls', 'mesh'])
## Use Examplessentence="type 2 diabetes"sentence="Diabetes Mellitus, Type 2"sentence="type 1 diabetes"sentence='diabetes'sentence="ibuprofen"sentence="Influenza"sentence="Swine influenza"sentence="Achromobacter"sentence='what gene is assocaited with AK-Taine?'sentence='SDFxddf'sentence='Puerperal fever'sentence='Periodontitis'sentence='hemophilia B'sentence='Christmas Disease'sentence='Filgrastim (USP/INN)'## run ner classres=trapi_ner.get_kg2_match(sentence, remove_mark=True)
json_string=json.dumps(res, indent=4)
print(json_string)

About
A prototype investigation of converting natural language questions to the TRAPI format
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages