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Multiple Ontologies in Healthcare Information
Technology: Motivations and Recommendation
for Ontology Mapping and Alignment
Colin Puri1, Karthik Gomadam1, Prateek Jain2, Peter Yeh1, Kunal Verma1
1Accenture
2Kno.e.sis
Technology Labs, San Jose, CA
Center Wright State University, Dayton, OH
Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
Outline
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Introduction
Current Approaches
Ontology Mappings
Our Point of View & Recommendation: BLOOMS
Questions
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Introduction
• Key Issues
– No single ontology can meet the growing needs of healthcare
– Heterogeneous landscape
– Existing ontologies must be integrated to support data analysis
• Integration of patient data and health sources allows for mining and
answering of key questions
– What treatments were administered to other patients with similar health conditions?
– What was the efficacy of such treatments when administered to patients with a
given physiological profile?
– What medications are currently being prescribed to the patient and how do they
constrain available treatment options?
– How can one meaningfully find and and utilize the vast amounts of medical
knowledge, such as codified medical vocabularies, scientific publications, and
findings from clinical trials, available in the public domain?
– How can the health and wellness information stored by a patient in PHRs and other
PHR-based applications be used to improve the quality of care?
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Outline
•
•
•
•
•
Introduction
Current Approaches
Ontology Mappings
Our Point of View & Recommendation: BLOOMS
Questions
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Current Efforts & Approaches
• A patient's medical record captures multiple aspects of his/her health
Information can come from multiple sources (e.g. EMR systems,
PHR applications, etc.).
• Integration into a coherent view requires combining multiple
ontologies such as:
– SnoMed
– Gene Ontology
• Examples Current efforts:
– UMLS
• Existing Challenges
– Syntactic differences between ontologies
– Deep semantic differences
– Generation of mappings
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Outline
•
•
•
•
•
Introduction
Current Approaches
Ontology Mappings
Our Point of View & Recommendation: BLOOMS
Questions
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Ontology Mapping
• Ontology Mapping and Alignment Strategies Include:
– Machine Learning
– Rule Based Mapping
– Logic Driven Frameworks
• Categories of Ontology Mapping
– Global ontology view to local ontology view
– Semantic mappings between local and target entities
– Mappings for enablement of ontology reuse by integration and
alignment
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Outline
•
•
•
•
•
Introduction
Current Approaches
Ontology Mappings
Our Point of View & Recommendation: BLOOMS
Questions
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BLOOMS Approach
• For each concept name in the ontology
– Identify article in Wikipedia corresponding to the concept.
– Each article related to the concept indicates a sense of the usage of the
word.
• For each article found in the previous step
– Identify the Wikipedia category to which it belongs.
– For each category found, find its parent categories till level 4.
• Once the “BLOOMS tree” for each of the sense of the source
concept is created (Ti), utilize it for comparison with the “BLOOMS
tree” of the target concepts (Tj).
– BLOOMS trees are created for individual senses of the concepts.
BLOOMS
Available for download at: http://wiki.knoesis.org/index.php/BLOOMS
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BLOOMS
Conclusion
• We have presented a system called BLOOMS for performing
ontology alignment using contextual information.
• BLOOMS can be extended to utilize datasource of choice such as
UMLS.
• To the best of our knowledge, BLOOMS is the only system which
utilizes contextual information present in ontology and Wikipedia
category hierarchy for ontology matching.
• BLOOMS significantly outperforms state of the art solutions for the
task of ontology alignment [1,2].
References
① Prateek Jain,Peter Z. Yeh, Kunal Verma, Reymonrod Vasquez, Mariana
Damova, Pascal Hitzler and Amit P. Sheth, “Contextual Ontology
Alignment of LOD with an Upper Ontology: A Case Study with Proton”.In
Proceedings of the 8th Extended Semantic Web Conference 2011,
volume 6643 of Lecture Notes in Computer Science, Heidelberg, 2011.
Springer Berlin.
② Prateek Jain, Pascal Hitzler, Amit P. Sheth, Kunal Verma and Peter Z.
Yeh, “Ontology Alignment for Linked Open Data”. In Proceedings of the
9th International Semantic Web Conference 2010, Shanghai, China,
November 7th-11th, 2010,volume 6496 of Lecture Notes in Computer
Science, pages 402-417, Heidelberg, 2010. Springer Berlin.
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Questions
• Any Questions?
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