Bio-medical Ontologies Maintenance and Change Management

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160 A. Shaban-Nejad and V. Haarslev Fig. 5 demonstrates a portion of the FungalWeb application in categorical representation. Fig. 5. A category model of portion of the FungalWeb application Most common operations during ontology evolution, such as adding or deleting a class/property/relationship, combining two classes into one, or adding a generalization/association relationship, can be represented using category theory (Shaban-Nejad and Haarslev 2007(b)). Based on our application, we designed our class diagrams following Whitmire (1997) to track different states of our ontological structure (Fig. 6). The Op i arrows in this figure represent the operations, which cause an ontological state to change to another state. For instance, in Fig. 11, the operation Op 1 causes the transition of an object from state St 1 to state St 2 . The Op r (reverse operation) returns the system to its initial state (St 1 ). For more details see (Shaban-Nejad and Haarslev 2007, 2008) Fig. 6. A class diagram to track different operations and states in an evolving structure Summary Biology is known as a field with continuous evolution. As the knowledge about biology rapidly grows and new methods become available, one can anticipate a
Bio-medical Ontologies Maintenance and Change Management 161 fundamental change in the way we capture the knowledge of the real world. One of the important activities in knowledge representation and bioinformatics is properly responding to changes and coping with the ontological evolution. Research on ontology change management is an ongoing effort that is still in its early stages. Many tools and techniques are available to assist ontology engineers to implement changes. However, they still have long road ahead to be considered for practical usage due to following issues: - Lack of formal change models with clear semantics - Inconsistencies among change models and log models - Too much reliance on human decisions - Reproducibility of the results cannot be guaranteed - Little or no support for the representation of complex changes - Lack of formal evaluation methods - Little support for handling changes in conceptualization The proposed RLR framework can be used to Represent, Legitimate and Reproduce the changes and their effects. Using this framework can help capture, track, represent and manage the changes in a formal and consistent way, which enables the system to create reproducible results. In this framework, all changes should be represented in either formal or diagrammatical representations. The change then should be legitimated and validated logically, by a reasoning agent, and it should be approved publicly and by experts. In order to reproduce the results of changes and automate the change management process, agents can be used to learn the patterns of changes, using learner agents, while the negotiation agent acts as a mediator that allows the ontology engineer and other autonomous agents to negotiate the proper implementation of a specific change. We are currently using the RLR framework to manage the evolving structure of the FungalWeb Ontology. Due to importance of the Representation phase, we concentrated more on this issue in this manuscript. For diagrammatical representation, we proposed using category theory, which has significant potential as a supplementary tool for capturing and representing the full semantics of ontology-driven applications. Category theory can provide a formal basis for analyzing complex evolving biomedical ontologies. Our future research will be focused on improving the reasoning process and generalizing the usage of category theory along with other formalisms to improve ontological conceptualization change management. Due to the multidisciplinary nature of research on ontology change management, any advances in this field would be highly dependent on advances in the research of various related topics, such as ontology integration, translation, merging or alignment, and computer-human interactions. References Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., et al.: Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000) Awodey, S.: Category Theory. Oxford University Press, Oxford (2006)
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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72 L. Stanescu, D. Dan Burdescu, an
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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