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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Bootstrapping Adversarial Learning of Biomedical Ontology Alignments
Ramon M Maldonado1, Sanda M Harabagiu1
1University of Texas at Dallas, Richardson, TX, U.S.A.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|April 21, 2020
Summary
This study introduces KAEGAN, a novel approach for aligning biomedical ontologies using knowledge graph embeddings. Jointly learning alignment and representation enhances performance over isolated methods.
Area of Science:
- Biomedical Informatics
- Artificial Intelligence
- Knowledge Representation
Background:
- Automatic alignment of biomedical ontologies is crucial due to their increasing complexity and widespread use.
- Neural learning techniques can leverage knowledge graph embeddings for ontology representation and alignment.
Purpose of the Study:
- To present the Knowledge-graph Alignment & Embedding Generative Adversarial Network (KAEGAN).
- To demonstrate KAEGAN's capability in representing relational knowledge and aligning biomedical ontologies using semantics.
Main Methods:
- KAEGAN utilizes a Generative Adversarial Network (GAN) framework.
- The model is trained using bootstrapping for iterative alignment improvement.
- It learns knowledge embeddings from distinct biomedical ontologies.
Main Results:
- Experimental results indicate promising performance for KAEGAN.
- Jointly learning ontology alignment and knowledge representation yields superior outcomes.
- The approach effectively utilizes ontology semantics for alignment.
Conclusions:
- KAEGAN offers a promising method for automated biomedical ontology alignment.
- Integrating knowledge representation with alignment learning enhances overall effectiveness.
- This work advances the field of biomedical knowledge graph integration.
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