Bio-medical Ontologies Maintenance and Change Management

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170 D. Apiletti et al. of possible schema constraints. However, since they are inferred from data, they highlight possible schema properties, but do not represent actual schema properties. An instance constraint may become a schema property when it is validated by an application domain expert. Since our aim is to infer unknown constraints from data, we will focus on instances constraints. We will denote instance constraints with the term constraint in the remainder of this chapter. For example, a constraint may be inferred by analyzing the values of the Age attribute. If it always has positive values, we can infer the (instance) domain constraint Age>0. Instead, by analyzing a mark database, we may detect a tuple constraint between the values of the attributes Mark and Honours. If the Honours attribute is true, Mark should take the highest value. Instead, if Mark does not take the highest value, Honours is false. If all the values of two attributes are linked by tuple constraints, then there is a functional dependency between the attributes. A functional dependency states that if in a relation two rows agree on the value of a set of attributes X, then they also agree on the value of a set of attributes Y. The dependency is written as X → Y. For example, in a relation such as Buyers (Name, Address, City, Nation, Age, Product), there is a functional dependency City → Nation, because for each row the value of the attribute City identifies the value of the attribute Nation (i.e., a city always belongs to the same nation). Unfortunately, real datasets are affected by errors. Errors can be divided into two categories: syntactic and semantic. Among syntactic errors there are incompleteness (due to the lack of attribute values), inaccuracy (due to the presence of errors and outliers), lexical errors, domain format errors and irregularities. Among semantic errors there are discrepancies, due to a conflict between some attribute values (i.e. age and date of birth), ambiguities, due to the presence of synonyms, homonyms or abbreviations, redundancies, due to the presence of duplicate information, inconsistencies, due to an integrity constraint violation (i.e. the attribute age must be a value grater than 0) or a functional constraint violation (i.e. if the attribute married is false, the attribute wife must be null), invalidities, due to the presence of tuples that do not display anomalies of the classes defined above but still do not represent valid entities [12]. Public genomic and proteomic databases may be affected by the aforementioned errors, mainly because they grew some years ago, under the pressing need of storing the large amount of genetic information available at the time, without having neither a standard method to collect it, nor a standard format to represent it. Due to these problems it is difficult to clearly describe the actual relationships among data. Recently, a significant effort has been devoted to the integration of distributed heterogeneous databases, where researchers continuously store their new experimental results. However, the existence of erroneous or poor data may harmfully affect any further elaboration or application. Errors can occur in different phases of data production: experiment (unnoticed experimental setup failure or systematic errors), analysis (misinterpretation of information), transformation (from one representation into another), propagation
Extraction of Constraints from Biological Data 171 (erroneous data used for the generation of new data), and staleness (unnoticed changes in data could produce the falsification of other data which depend on it). These problems lead to semantic errors and the resulting information does not represent the real-world facts correctly. Data dependencies inherent to data production process and data usage make genome data predestined for transmitting errors [21]. Most existing works, such as [11], [23] and [20], focus on inaccuracy, lexical errors, redundancy problems and enforcement of integrity constraints, but ignore the functional constraint violations. Moreover, due to the large amount of data in existing databases, a tool for automatically detecting relationships among data can be useful for biological specialists to improve the domain knowledge. The objective is to define an algorithm that automatically infers rules from data: rules can be maintained by means of an incremental approach even if data are updated. We propose a method to discover tuple constraints and functional dependencies among data by means of association rule mining. Constraints and dependencies show semantic relationships among attributes in a database schema. Their knowledge can be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. Association rules are a well-known data mining tool. They have been applied to biological data cleaning for detecting outliers and duplicates [17], and to Gene Ontology for finding relationships among terms of the three ontology levels (cellular components, molecular functions and biological processes) [18], [6], but not for finding constraints, dependencies or anomalies. By means of association rules we detect correlation relationships among attribute values. Then, by analyzing the support and confidence of each rule, (probabilistic) tuple constraints and functional dependencies may be detected. They may both show the presence of erroneous data and highlight novel semantic information. We present experiments on biological databases and we validate our method by verifying its correctness and completeness. Finally, we show how, by means of a further analysis of the obtained results, domain knowledge and data quality may be improved. 2 Application of Constraint Extraction Recently, new ways to mine patterns and constraints in biological databases have been proposed. In [16] the authors focus on constrained pattern mining on the “transposed” database, thus facing a smaller search space when the number of attributes (genes) is orders of magnitude larger than the number of objects (experiments). They present a theoretical framework for database and constraint transposition, discuss the properties of constraint transposition and look into classical constraints. Our approach does not require the data to be transposed and aims at discovering constraints among attributes instead of constraints associated with patterns.
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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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Substructure Analysis of Metabolic
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entry compound relation subtype com
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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