The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining

Sargsyan, Astghik; Wegner, Philipp; Gebel, Stephan; Kaladharan, Abish; Sethumadhavan, Priya; Lage-Rupprecht, Vanessa; Darms, Johannes; Schultz, Bruce; Klein, Jürgen; Jacobs, Marc; Madan, Sumit; Hofmann‐Apitius, Martin; Kodamullil, Alpha Tom

doi:10.1093/bioadv/vbad033

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…For example, the Human Disease Ontology (DO) [ 53 ] covers many human diseases. However, researchers studying epilepsy needed a more detailed ontology and created the Epilepsy Ontology [ 54 ]. BKO fusion can be applied to take information from separate ontologies and combine them into one.…”

Section: A Detailed Examplementioning

confidence: 99%

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness

Santos,

Jurmain,

Ragazzi

2024

PLoS ONE

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The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain.

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Section: A Detailed Examplementioning

confidence: 99%

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness

Santos,

Jurmain,

Ragazzi

2024

PLoS ONE

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Brain Injury Knowledge Ontology (BIKO) for traumatic brain injury: Formalizing concepts and methods used in translational traumatic brain injury research

Surles-Zeigler,

Sincomb,

Dixon

et al. 2023

Preprint

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Traumatic brain injury (TBI) is an insult to the brain resulting from an external force and is a significant cause of morbidity and mortality in the United States. No effective clinical therapeutics currently exist for this injury. Although several therapies and procedures have been deemed successful for TBI treatment in preclinical research studies, they have yet to be translated into human patients. These discouraging results have left many scientists questioning the role of animal models in drug discovery for TBI.One major hurdle in translating the knowledge obtained in the laboratory to the clinic is the methodological variance across these studies. This variance can hinder the ability to draw conclusions from conflicting studies and aggregate data across various research studies, which ultimately impedes the ability to aggregate data across these studies. Therefore, addressing this variance is crucial for bridging the gap between the laboratory and the clinic. The increasing volume of papers and associated data being published every day makes this hurdle even more difficult to overcome. The initial steps to address these knowledge gaps are identifying these studies and creating a shared knowledge framework for mapping their terminology. We are developing the Brain Injury Knowledge Ontology (BIKO) to create a standardized model to describe methods and outcome measures used within preclinical and clinical TBI therapy studies to facilitate comparison across studies and models. The first version of BIKO focuses on modeling the major preclinical TBI models, e.g., Controlled Cortical Impact Model, Fluid Percussion Model, and Weight-Drop Model), major neurological injuries related to these models and their relationship to clinical pathophysiology. We show how BIKO provides a machine-readable way to represent the methodologies used in TBI therapeutic studies to compare models across clinically relevant features.

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The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining

Cited by 2 publications

References 6 publications

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness

Brain Injury Knowledge Ontology (BIKO) for traumatic brain injury: Formalizing concepts and methods used in translational traumatic brain injury research

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