Building Farmer’s Profiles from Text and Unstructured Information
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 https://omdena.com/projects.
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.
- 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.
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