Bio-medical Ontologies Maintenance and Change Management

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156 A. Shaban-Nejad and V. Haarslev Fig. 2. A general view of the RLR framework The Representation phase updates the representations (either formal or diagrammatical) of the new knowledge in a consistent way. For formal representation, we use Description Logic (DL) (Baader et al. 2003), and for diagrammatical representation, we employ a method based on discrete state model and category theory (Shaban-Nejad and Haarslev 2007(a)). The Legitimation stage determines the consistency and validity of a change. This phase assesses the impact of a potential change before the change is actually made. Software agents can search, notify, perceive and collect information about different actions from multiple sources in a frequently changing environment. Intelligent agents perform rationally in dynamic environments and provide prompt responses. In our framework, we have employed various types of agents: the change capture, learner, reasoning and negotiation agents. The change capture agents perceive and capture different changes and accumulate the related information within the change logs. The learner agent can use these records of changes, which occur over and over in a change management procedure, to design a change pattern. This means that after several changes—most likely in different releases—the direction and rate of change can be predicted with reasonable accuracy. The learner agent and the change patterns are essential in the Reproduction phase. A reasoning agent controls the logical validity of the ontological structure and reveals inconsistencies, hidden dependencies, redundancies and misclassifications. RACER (Haarslev and Möller 2001) has been used in RLR as a Description Logic reasoning agent. The negotiation agent acts as a mediator to assist the ontology engineer and other autonomous agents in negotiations concerning the realization of particular change. The negotiation outcome will be confirmed, deleted, or modified by human experts in accordance to the purpose of the system. We have used category theory for mathematical notation, which is independent of a specific choice of ontology language and any particular implementation, as
Bio-medical Ontologies Maintenance and Change Management 157 the main formalism of the RLR framework. Furthermore, we will demonstrate the applicability of category theory for representing changes in the ontological structure. 6.1 Representation of Evolving Ontologies Using Category Theory We noticed that the main issues in change management strategies are simply problems about the representation of change. Category theory is an intuitive domain of mathematics, introduced in 1945 (Eilenberg and MacLane 1945). Category theory is grounded in logic and algebra, and allows an ontology engineer to define different states of conceptual models to represent the reality. Categories can be used for specifying different objects, capturing their interactions and the relations between them, determining patterns of interacting objects and decomposing a composite object into its basic elements (Ehresmann and Vanbremeersch 2006). Categorical representations consist of diagrams with arrows (i.e., f: X�Y). A typical category includes classes of objects and morphisms (arrows), and for each morphism f, there is one object as the domain of f (i.e., X) and one object as the codomain (i.e., Y). In addition, for each object, X, we can define an identity morphism (id x ) which has domain and codomain X. Also, for each pair of morphisms f: X→Y and g: Y→Z, (in other words, cod(f) = dom(g)) a composite morphism (g o f : X→Z) exists. The representation of a category can be formalized using the notation of a diagram. Ontologies in their simplest form can be seen as the categorization of things in the real world. It is quite common to use diagrammatical notations to analyze ontological structures. For example, the UMLS Semantic Network (SN) has been modeled as a graph, whose nodes denote concepts (McCray 2003). Category theory, with its analytical characteristics, can act as the prospective knowledge representation language, particularly for modeling dynamic abstract models, where ontologies can be defined as an interconnected hierarchy of categories (Healy and Caudell 2006) with directed ontological relationships as “morphisms” (Krötzsch 2005). As a knowledge representation language, category theory improves the incremental analysis of changes in ontological structures. Some of the primitive constructors of category theory, which we use in our framework for ontology change management, are as follows: primary categorical objects and morphisms, functors, pushouts, pullbacks, natural transformations and isomorphisms. Functors are a special type of mapping between categories, defined as morphisms in the category of all small categories (classes are defined as categories) (Awodey 2006). As defined in (Van Oosten 2002), assume A, B are two categories, so a functor F: A→B is joint functions: F 0 : A 0 →B 0 and F 1 : A 1 →B 1 , such that: ∀f: α→ β∈ A 1 => F 1 (f): F 0 (α) → F 0 (β) ∈ B 1 ∀ α∈ A 0 => F 1 (idα) = id F0( α ) ∀ g o f ∈ A 1 => F 1 (g o f) = F 1 (g) o F 1 (f) ∈ B 1 A categorical model can represent transitions between different states of an ontological structure. To represent and track this kind of transition, we represent the conceptualization of things indexed by time (i.e., from the FungalWeb Ontology
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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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Classifying Patterns in Bioinformat
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Classifying Patterns in Bioinformat
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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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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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