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Building Farmer’s Profiles from Text and Unstructured Information

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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at

The problem

The problem this project is trying to address is how to map the relationships between concepts in different ontologies or knowledge bases. An ontology is a formal representation of knowledge that defines the concepts and relationships within a specific domain. Ontology mapping involves identifying corresponding concepts across different ontologies and establishing relationships between them. The challenge with it is that ontologies can be created and organized differently, and may use different terminologies or vocabularies to describe the same concepts. Additionally, a lot of information is unstructured, meaning it is not organized in a specific way that can be easily understood by computers.

Machine learning can be used to analyze and process unstructured information and map relationships between concepts in different ontologies. This involves training machine learning models on large amounts of data to identify patterns and relationships between concepts. The resulting model can then be used to automatically map the relationships between concepts in different ontologies. The goal of ontology mapping from text and unstructured information is to improve the interoperability and integration of knowledge resources, which can help to enable more efficient and accurate knowledge sharing and data integration across different systems and applications. This can be especially useful in fields such as healthcare, where data is often stored in different systems and ontologies, and integrating this data can help improve patient care and outcomes.

The project goals

The ultimate project goal is to create a framework for farm-related ontology Mapping from textual and unstructured data.

Project Scope:

  • Implement part of speech tagging and entity recognition
  • Use this structured data to guide the creation of the “farm profile,” Clippy style
  • Working with subject matter experts to highlight important claims in the information they’re acquiring in their own workflow
  • An important challenge is reducing friction in this to an absolute minimum.

**More details will be shared once the project is started.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities

Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus

Skill requirements

Good English

Machine Learning Engineer

Experience working with Smartphone Sensor Data is a plus.

This project is hosted with our friends at

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