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Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and
Eugene Santos1, Jacob Jurmain1, Anthony Ragazzi1
1Thayer School of Engineering, Dartmouth College, Hanover, NH, United States of America.
Plos One
|March 27, 2024
Summary
This study introduces a novel ontology framework that integrates description logic with probabilistic semantics to effectively model and reason with uncertain information, overcoming limitations of current systems for a more robust semantic web.
Area of Science:
- Computer Science
- Artificial Intelligence
- Knowledge Representation
Background:
- Current ontologies struggle to represent uncertain or stochastic information, hindering the development of a truly semantic web.
- Existing methods often require data normalization or rejection, leading to information loss and potential inaccuracies.
- The omnipresence of uncertainty in the real world necessitates explicit modeling within ontologies for effective knowledge engineering.
Purpose of the Study:
- To present an ontology framework capable of explicitly modeling real-world uncertainty and integrating it into reasoning processes.
- To enable the representation of stochastic, uncertain, or incomplete subject matter within ontologies.
- To develop a method for fusing multiple conflicting ontologies into a unified, consistently reasoned knowledge base.
Main Methods:
- A seamless synthesis of description logic and probabilistic semantics is proposed.
- A link between ontology assertions and random variables facilitates automated probability distribution construction for inferencing.
- Probabilistic semantics are employed to resolve conflicts between assertions during ontology fusion, preserving potentially valid knowledge.
Main Results:
- The framework successfully represents stochastic and uncertain subject matter.
- Ontology fusion using probabilistic semantics resolves conflicts without data deletion or extensive consistency checks.
- Emergent inferences, not present in individual ontologies, can be derived from the fused knowledge base.
Conclusions:
- The proposed ontology framework offers a robust solution for modeling and reasoning with uncertainty.
- This approach enhances knowledge representation capabilities, paving the way for more comprehensive semantic web applications.
- The ability to fuse conflicting ontologies and derive novel insights significantly advances the field of ontology research.
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