Bio-medical Ontologies Maintenance and Change Management

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168 A. Shaban-Nejad and V. Haarslev FWOnt: FungalWeb Ontology GO: Gene Ontology HL7: Health Level 7 KR: Knowledge Representation MeSH: Medical Subject Headings NLM: The National Library of Medicine OBO: Open Biological Ontologies OWL: The Web Ontology Language RACER: Renamed Abox and Concept Expression Reasoner RLR: Representation, Legitimation and Reproduction SPARQL: SPARQL Protocol and RDF Query Language SWRL: The Semantic Web Rule Language TA: Terminologia Anatomica UMLS: The Unified Medical Language System
Extraction of Constraints from Biological Data Daniele Apiletti, Giulia Bruno, Elisa Ficarra, and Elena Baralis Department of Control and Computer Engineering (DAUIN), Politecnico di Torino C.so Duca degli Abruzzi 24, 10129 Torino, Italy 1 Introduction Data constraints are used in structured and unstructured databases to capture realworld semantics observed in the modeled application domain. In our context, a constraint can be defined as a set of predicates P 1 ∧ P 2 ∧ ... P k . Each predicate is in the form C 1θ C 2 , where C 1 is an attribute, θ is a comparison operator and C 2 is either an attribute or a constant [15]. Constraints are assertions on permissible or consistent database states, and specify certain properties of data that need to be satisfied by valid instances of the database. Constraints show dependencies among data and add semantics to a database schema. Thus, they are useful for studying various problems such as database design, query optimization and dimensional reduction. Constraints are usually introduced at design time to describe a priori knowledge. Consequently, the valid instances of the database are those satisfying simultaneously all constraints. However, collected data can hide interesting and previously unknown information because of unstated constraints. For example, this happens when data is the result of an integration process of several sources or when it represents dynamic aspects. Furthermore, the design process is not always complete and the constraint definition may be omitted from the design. The analysis of heterogeneous data with the aim of detecting implicit information is in important and useful task, which may become complex due to the size of datasets. Among the numerous constraint types, we focus on table constraints, which refer to a single relation of the database. Examples of such constraints are domain constraints and tuple constraints. A domain constraint restricts allowed values of a single attribute, i.e. it describes its domain. A tuple constraint limits the allowed values for several (related) attributes of the same tuple. Constraints are properties of the database schema, thus they can not be directly inferred from data. We will denote this type of constraints as schema constraints. However, if the constraints are not a priori known, they can be hypothesized by analyzing database instances. We can not directly infer schema constraints from data, but we can infer instance constraints, i.e., constraints which represent the current relationships holding among data. Instance constraints represent a snapshot on the current database state A.S. Sidhu et al. (Eds.): Biomedical Data and Applications, SCI 224, pp. 169–186. springerlink.com © Springer-Verlag Berlin Heidelberg 2009
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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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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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