Efficient Discovery of Ontology Functional Dependencies

Poor data quality has become a pervasive issue due to the increasing complexity and size of modern datasets. Constraint based data cleaning techniques rely on integrity constraints as a benchmark to identify and correct errors. Data values that do not satisfy the given set of constraints are flagged as dirty, and data updates are made to re-align the data and the constraints. However, many errors often require user input to resolve due to domain expertise defining specific terminology and relationships. For example, in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be captured in a pharmaceutical ontology. While functional dependencies (FDs) have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., is-a) defined by an ontology. In this paper, we take a first step towards extending the set of data quality constraints used in data cleaning by defining and discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out theoretical and practical foundations for OFDs, including a set of sound and complete axioms, and a linear inference procedure. We then develop effective algorithms for discovering OFDs, and a set of optimizations that efficiently prune the search space. Our experimental evaluation using real data show the scalability and accuracy of our algorithms.Comment: 12 page

Comorbidities in children hospitalized with severe acute malnutrition

Background: As per the National Family Health Survey-4 data, 7.9% of under-five children in the state of Tamil Nadu are severely wasted. The outcome of hospitalized severe acute malnutrition (SAM) children is dependent on the comorbidities present. Objective: The objective of this study is to describe the comorbid conditions in SAM children hospitalized in a tertiary care center. Methodology: This study was a hospital-based descriptive study, conducted from July 2015 to June 2016. A total number of 200 children, who were admitted with SAM as per the World Health Organization criteria, were included in the study. Systemic illness, anemia, vitamin deficiencies, sepsis, retroviral infection, tuberculosis, pneumonia, acute gastroenteritis, urinary tract infection (UTI), measles, skin infections, and worm infestations were the comorbidities considered. Results: Among 200 hospitalized SAM children, the median (interquartile) age was 15 (11–21.75) months; there were 93 (46.5%) boys. Acute gastroenteritis (57.5%) was the most common comorbidity, followed by pneumonia (44.5%), anemia (27%), systemic illness (17%), worm infestation (13.5%), UTI (13.5%), sepsis (13%), skin infection (8%), measles (6%), vitamin deficiency (4%), retroviral infections (3.5%), and tuberculosis (1%). The case fatality rate was 10.5%. Conclusion: Prompt identification of comorbidities is crucial in hospitalized SAM children, which will pave way for their treatment, resulting in better outcomes

DISCOVERING ONTOLOGY FUNCTIONAL DEPENDENCIES DISCOVERING ONTOLOGY FUNCTIONAL DEPENDENCIES TITLE: Discovering Ontology Functional Dependencies AUTHOR

Abstract Functional Dependencies (FDs) are commonly used in data cleaning to identify dirty and inconsistent data values. However, many errors require user input for specific domain knowledge. For example, let us consider the drugs, Advil and Crocin. FDs will consider these two drugs different because they are not syntactically equal. However, Advil and Crocin are synonyms as they are two different drugs with similar chemical compounds but marketed under distinct names in different countries. While FDs have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., synonym, Is-A (Inheritance)) defined by ontologies. In this thesis, we take a first step to discover a new dependency called Ontology Functional Dependencies (OFDs). OFDs model attribute relationships based on relationships in a given ontology. We present two effective algorithms to discover OFDs using synonyms and inheritance relationships. Our discovery algorithms search for minimal OFDs and prune the redundant ones. Both algorithms traverse the search lattice in a level-wise Breadth First Search (BFS) manner. In addition, we have developed a set of pruning rules so that we can avoid considering unnecessary candidates in the search lattice. We present an experimental study describing the performanc