Knowledge Graph for Sleep-Related Respiratory Disorders

Knowledge Graphs can be defined as knowledge repositories that model data in a graph-structured manner. Moreover, this graphs usually encapsule a semantic model or ontology that facilitates sharing of common understanding among people and teams. In the medical domain, knowledge graphs have several applications like structuring research knowledge for broader and faster accessibility or integrating domain knowledge with study results to help in reliable diagnostics. In the field of Sleep Related Respiratory Disorders a knowledge graph would be highly beneficial for domain knowledge representation and integration. The creation of a Medical Knowledge Graph entails various tasks:

    1. Medical ontologies applicable to the field of Sleep Related Respiratory Disorders already exist, but need to be analysed and validated with domain experts. 
    2. Medical knowledge can be found in both structured and unstructured formats, preprocessing pipelines to transform the data into a graph structure reflecting the ontological model. 
    3. Unstructured data in text format may need to be preprocessed with Natural Language Processing techniques called Entity Recognition and Relationship Extraction. 
    4. The graph needs to be stored in appropriate databases that allow for graph querying like Neo4j, GraphDB, etc.
    5. Interacting with and visualising the data most probably need to be handled through microservices and/or off-the-shelf applications like Apache Zeppelin.
    6. Design and development and of a frontend layer for interacting and visualising is also an option to make the knowledge  more accessible.

This research project is associated with the Respire (Responsible Explainable Machine Learning  for Sleep-related Respiratory Disorders: https://www.mn.uio.no/ifi/english/research/projects/respire/) project. There is an opportunity for up to three students to take part in developing the knowledge graph through collaboration with other members of the Respire project. 

Required/recommended qualification:

  • The candidates should have experience with python. 
  • The candidates should have some knowledge about graphs and ontologies. 
  • Ideally a candidate has interest or experience in NLP techniques. 
  • Ideally a candidate has interest or experience in microservices/backend. 
  • Ideally a candidate has interest or experience in data visualisation and/or frontend. 

Contact persons:

Marta Quemada Lopez (martaq@ifi.uio.no)

Vera Goebel (goebel@ifi.uio.no)

Resources

Respire Project

 

Cesar Project (Completed)

 

Sleep-Related Respiratory Disorders

Knowledge Graphs

 

Use Case: Diabetes Knowledge Graph

Publisert 16. aug. 2023 17:07 - Sist endret 28. aug. 2023 11:58

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