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INVESTIGATING IMPLICIT KNOWLEDGE IN ONTOLOGIES WITH APPLICATION TO THE ANATOMICAL DOMAIN
2003
Biocomputing 2004
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This paper investigates implicit knowledge in two ontologies of anatomy: the Foundational Model of Anatomy and GALEN. ...
The application of these findings to ontology auditing, validation, and maintenance is discussed, as well as the application to ontology integration. ...
Thanks also to Pieter Zanstra at Kermanog for providing us with an extended license for the GALEN server. ...
doi:10.1142/9789812704856_0024
fatcat:nhfgfuta6bhb7aux5jv2istexq
Investigating implicit knowledge in ontologies with application to the anatomical domain
2004
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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This paper investigates implicit knowledge in two ontologies of anatomy: the Foundational Model of Anatomy and GALEN. ...
The application of these findings to ontology auditing, validation, and maintenance is discussed, as well as the application to ontology integration. ...
Thanks also to Pieter Zanstra at Kermanog for providing us with an extended license for the GALEN server. ...
pmid:14992508
pmcid:PMC4351965
fatcat:vaqyoj4alnft7civb74x4bb63e
Desiderata for domain reference ontologies in biomedicine
2006
Journal of Biomedical Informatics
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Domain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain. ...
ii) to infer new knowledge in ontologies of more complex entities, and (iii) to manage and help reasoning about individual data. ...
Acknowledgment The author thanks Olivier Bodenreider and Olivier Dameron for providing valuable insight. ...
doi:10.1016/j.jbi.2005.09.002
pmid:16266830
fatcat:rs266b2rh5bo5dvyccqzrgkcrq
NLM Anatomical Ontology Alignment System. Results of the 2006 Ontology Alignment Contest
2006
International Semantic Web Conference
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National Library of Medicine, we have developed domain knowledge-based techniques for aligning large anatomical ontologies, with the objective of exploring approaches to aligning representations of anatomy ...
When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with and reasoning across these ...
Here, domain knowledge was required to assess what relations can be automatically extracted with high accuracy in the particular context of anatomical terms. ...
dblp:conf/semweb/ZhangB06
fatcat:saorbo5zi5cznd2zkq3fqzq5gu
Intelligent Medical Image SearchIntelligente Bildsuche in der Medizin
2009
it - Information Technology
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In this article we present the project MEDICO that strives to implement intelligent medical image search by means of machine learning algorithms and semantic technologies. ...
Zusammenfassung Mit dem technologischen Forschritt in der medizinischen Bildgebung gewinnen medizinische Bilder in der Früherkennung, Diagnose, Behandlungsplanung und bei Nachfolgeuntersuchungen zunehmend ...
Based on the comprehensive representation of the domain knowledge, hidden and implicit knowledge can be derived and enhance medical image search. ...
doi:10.1524/itit.2009.0553
fatcat:343lq4l55rcc3crmadpthfweyu
An Intelligent System Approach for Integrating Anatomical Ontologies
2012
Biotechnology & Biotechnological Equipment
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To achieve the degree of intelligence that is needed, the AnatOM program utilizes the large amount of high-quality data (knowledge) available in several widely popular and generally recognized knowledge ...
Anatomical (phenotypic) ontologies of various species nowadays typically contain from few thousands to few tens of thousands of terms and relations (which is a very small number compared to the count of ...
When considering simple, limited application domains and developing Ai systems and Ai agent for them, the choice of a knowledge representation model is not that important as it is easy to come up with ...
doi:10.5504/bbeq.2012.0010
fatcat:x4pixqcqqfehdgbe2dwmdrti5i
A Semi-automated Method for Domain-Specific Ontology Creation from Medical Guidelines
[article]
2022
arXiv
pre-print
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A key component in automated conversation summarization is the matching of the knowledge graph of the consultation transcript with a medical domain ontology for the interpretation of the consultation conversation ...
The automated capturing and summarization of medical consultations has the potential to reduce the administrative burden in healthcare. ...
We make a step in this direction by laying down the formal foundations for the construction of semi-automated systems that support the interpretation of conversations using ontologies. ...
arXiv:2111.15338v2
fatcat:hjteclezprdqlcxuzwvpqorkhq
Law and order: Assessing and enforcing compliance with ontological modeling principles in the Foundational Model of Anatomy
2006
Computers in Biology and Medicine
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As a case study, we investigate the degree to which the Foundational Model of Anatomy (FMA)-a large ontology of anatomy-complies with these 15 principles. ...
Implicit relations-embedded in concept names or inferred from a combination of explicit relations-are used in this process in addition to the relations explicitly represented. ...
The authors would like to thank Anand Kumar for suggesting this study and for helpful discussions. ...
doi:10.1016/j.compbiomed.2005.04.007
pmid:16144698
pmcid:PMC1784072
fatcat:3k5nipxlyvctrmvf33ukrjgazy
Aligning representations of anatomy using lexical and structural methods
2003
AMIA Annual Symposium Proceedings
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The importance of taking advantage of implicit domain knowledge acquired through complementation,augmentation, and inference is discussed. ...
The objective of this experiment is to develop methods for aligning two representations of anatomy (the Foundational Model of Anatomy and GALEN) at the lexical and structural level. ...
Thanks also to Pieter Zanstra at Kermanog for providing us with an extended license for the GALEN server. ...
pmid:14728274
pmcid:PMC1480279
fatcat:3avua5dwpbbjxhn2gxftwu4yce
The Foundational Model of Anatomy in OWL 2 and its use
2013
Artificial Intelligence in Medicine
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The main strength of the method is to leverage OWL 2 expressiveness and to rely on the naming conventions of the FMA, to make explicit some implicit semantics, while improving its ontological model and ...
The method designed to represent the FMA ontology in OWL 2 presented in this article is general and may be extended to other ontologies. ...
We want to thank the anonymous reviewers for their comments, very helpful to improve that document. The authors thank Leslie Block for her valuable advice in the editing of this manuscript. ...
doi:10.1016/j.artmed.2012.11.002
pmid:23273493
fatcat:bwk5qypsqnhzramjvd2eu46ajq
Image metadata reasoning for improved clinical decision support
2012
Network Modeling Analysis in Health Informatics and Bioinformatics
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The resulting combined data serve as input to an automatic reasoning process in order to stage lymphoma patients automatically. ...
In this article we describe how we made use of reasoning technologies to post-process medical image annotations in the context of the automated staging process of lymphoma patients. ...
The responsibility for this publication lies with the authors. ...
doi:10.1007/s13721-012-0003-9
dblp:journals/netmahib/ZillnerS12
fatcat:4w4h36vgozfdpcpx3yu3phmpk4
Reasoning-Based Patient Classification for Enhanced Medical Image Annotation
[chapter]
2010
Lecture Notes in Computer Science
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We will exemplify the reasoning steps of the methodology along a use case for automatic lymphoma patient staging. ...
Medical imaging plays an important role in today's clinical daily tasks, such as patient screening, diagnosis, treatment planning and follow up. ...
The responsibility for this publication lies with the author. We are also thankful to Kamal Najib for his support with implementation tasks. ...
doi:10.1007/978-3-642-13486-9_17
fatcat:j27bobkf3bet7kt4ypaesi435i
A Case Study in Ontologies for Probabilistic Networks
[chapter]
2002
Research and Development in Intelligent Systems XVIII
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Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. ...
To study the usefulness of ontologies for this purpose, we have constructed an ontology for the domain of oesophageal cancer, based upon a real-life probabilistic network for the staging of cancer of the ...
We are grateful to Babs Taal and Berthe Aleman from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, who provided the domain knowledge for the construction of the oesophagus network. ...
doi:10.1007/978-1-4471-0119-2_18
fatcat:37lmiyey6zcxnab74likdamlii
Foundational model of neuroanatomy: implications for the Human Brain Project
2001
Proceedings. AMIA Symposium
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In order to meet the need for a controlled terminology in neuroinformatics, we have integrated the extensive terminology of NeuroNames into the Foundational Model of anatomy. ...
We illustrate the application of foundational principles for the establishment of an inheritance hierarchy, which accommodates anatomical attributes of neuroanatomical concepts and provides the foundation ...
Acknowledgements This work was supported in part by NIH contract LM13506 and grants LM06316 and Human Brain Project grant DC02310. ...
pmid:11825226
pmcid:PMC2243655
fatcat:t6kubyl2mnbchlc2j6qdm36nwe
Experience in Aligning AnatomicalOntologies
2007
International Journal on Semantic Web and Information Systems (IJSWIS)
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When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with these ontologies and reasoning ...
The four anatomical ontologies under investigation are the Foundational Model of Anatomy, GALEN, the Adult Mouse Anatomical Dictionary and the NCI Thesaurus. ...
of the GALEN at the University of Manchester, UK; to Martin Ringwald's group at the Jackson Laboratory for help with the Adult Mouse Anatomical Dictionary; and to Sherri de Coronado at ...
doi:10.4018/jswis.2007040101
pmid:18974854
pmcid:PMC2575410
fatcat:ppvoham76fbe3hynhiipvvw3da
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