Doctor of Philosophy, Case Western Reserve University, 2019, EECS - Computer and Information Sciences

Biomedical ontologies and standardized terminologies play an important role in healthcare information management, extraction, and data integration. The quality of ontologies impacts its usability. One of the quality issues is not conforming lattice property, a generally applicable ontology design principle. Non-lattice structures are often indicative of anomalies in ontological systems and, as such, represent possible areas of focus for subsequent quality assurance work. Quality assurance of ontologies is an indispensable part of the terminology development cycle. This dissertation presents a non-lattice based ontology quality assurance workflow, along with involved approaches, algorithms, and applications. The general steps of non-lattice based ontology quality assurance include: (1) extracting non-lattice fragments; (2) detecting potential defects and proposing remediation suggestions; (3) reviewing and validating these suggested remediations. For (1), a general MapReduce pipeline, called MaPLE (MapReduce Pipeline for Lattice-based Evaluation), is developed for extracting non-lattice fragments in large partially ordered sets. Using MaPLE in a 30-node Hadoop local cloud, we systematically extracted non-lattice fragments in 8 SNOMED CT versions from 2009 to 2014, with an average total computing time of less than 3 hours per version. Compared with previous work, which took about 3 months, MaPLE makes it feasible not only to perform exhaustive structural analysis of large ontological hierarchies but also to systematically track structural changes between versions. Our change analysis showed that the average change rates on the non-lattice pairs are up to 38.6 times higher than the change rates of the background structure (concept nodes). For (2), two methods, NEO and Spark-MCA, are proposed. NEO is a systematic structural approach for embedding of FMA fragments into the Body Structure hierarchy to understand the structural disparity of the subsumption relat (open full item for complete abstract)

Committee: Guo-Qiang Zhang (Advisor); Kenneth Loparo (Committee Chair); Xu Rong (Committee Member); Li Pan (Committee Member) Subjects: Biomedical Research; Computer Science; Health; Information Science

Master of Science, Miami University, 2014, Computer Science and Software Engineering

Ontology alignment (OA) is the process of taking as input two ontologies and producing mappings between the source concepts and the target concepts. Over the last few years, OA systems have made only minor improvements. To improve performance, some OA systems have included a semi-automatic matching approach which incorporates user interaction to assess low confidence mappings. This research investigates replacing the human expert with an automated expert or “oracle” that relies on specialized knowledge sources in the biomedical domain, BioPortal. BioPortal provides access to different resources including a wide variety of ontologies, classes within ontologies and mappings between the classes of different ontologies. A leading OA system LogMap has been used to evaluate the automated expert on the anatomy and Large Biomed Track of the Ontology Alignment Evaluation Initiative (OAEI). The experimental results are reported and show that the automated expert has a positive impact in the Large Biomed Track with four out of six of the track's matching tasks having better OA standard performance measure for F-measure. In the Anatomy Track, using the automated expert improves the OA standard performance measure for precision. However, to the detriment of the recall measure, the result is a slight improvement in the F-measure.

Committee: Valerie Cross (Advisor); Ernesto Jimenez-Ruiz (Committee Member); Dhananjai Rao (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2010, Computer Science and Engineering PhD

Provenance metadata, describing the history or lineage of an entity, is essential for ensuring data quality, correctness of process execution, and computing trust values. Traditionally, provenance management issues have been dealt with in the context of workflow or relational database systems. However, existing provenance systems are inadequate to address the requirements of an emerging set of applications in the new eScience or Cyberinfrastructure paradigm and the Semantic Web. Provenance in these applications incorporates complex domain semantics on a large scale with a variety of uses, including accurate interpretation by software agents, trustworthy data integration, reproducibility, attribution for commercial or legal applications, and trust computation. In this dissertation, we introduce the notion of “semantic provenance” to address these requirements for eScience and Semantic Web applications. In addition, we describe a framework for management of semantic provenance by addressing the three issues of, (a) provenance representation, (b) query and analysis, and (c) scalable implementation. First, we introduce a foundational model of provenance called Provenir to serve as an upper-level reference ontology to facilitate provenance interoperability. Second, we define a classification scheme for provenance queries based on the query characteristics and use this scheme to define a set of specialized provenance query operators. Third, we describe the implementation of a highly scalable query engine to support the provenance query operators, which uses a new class of materialized views based on the Provenir ontology, called Materialized Provenance Views (MPV), for query optimization. We also define a novel provenance tracking approach called Provenance Context Entity (PaCE) for the Resource Description Framework (RDF) model used in Semantic Web applications. PaCE, defined in terms of the Provenir ontology, is an effective and scalable approach for RDF provenance tra (open full item for complete abstract)

Committee: Amit Sheth PhD (Advisor); Krishnaprasad Thirunarayan PhD (Committee Member); Michael Raymer PhD (Committee Member); Nicholas Reo PhD (Committee Member); Olivier Bodenreider PhD (Committee Member); William York PhD (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2007, Engineering : Biomedical Engineering

An important goal for biomedical research is to elucidate causal and modifier networks of human disease. While integrative functional genomics approaches have shown success in the identification of biological modules associated with normal and disease states, a critical bottleneck is representing knowledge capable of encompassing asserted or derivable causality mechanisms. Both single gene and more complex multifactorial diseases often exhibit several phenotypes and a variety of approaches suggest that phenotypic similarity between diseases can be a reflection of shared activities of common biological modules composed of interacting or functionally related genes. Thus, analyzing the overlaps and interrelationships of clinical manifestations of a series of related diseases may provide a window into the complex biological modules that lead to a disease phenotype. In order to evaluate our hypothesis, we are developing a systematic and formal approach to extract phenotypic information present in textual form within Online Mendelian Inheritance in Man (OMIM) and Syndrome DB databases to construct a disease - clinical phenotypic feature matrix to be used by various clustering procedures to find similarity between diseases. Our objective is to demonstrate relationships detectable across a range of disease concept types modeled in UMLS to analyze the detectable clinical overlaps of several Cardiovascular Syndromes (CVS) in OMIM in order to find the associations between phenotypic clusters and the functions of underlying genes and pathways. Most of the current biomedical knowledge is spread across different databases in different formats and mining these datasets leads to large and unmanageable results. Semantic Web principles and standards provide an ideal platform to integrate such heterogeneous information and could allow the detection of implicit relations and the formulation of interesting hypotheses. We implemented a page-ranking algorithm onto Semantic Web to prioriti (open full item for complete abstract)

Committee: Dr. Bruce Aronow (Advisor) Subjects:

Master of Science, The Ohio State University, 2012, Computer Science and Engineering

There is a tremendous change in the amount of electronic data available to us and the manner in which we use it. With the on going “Big Data” movement we are facing the challenge of data “volume, variety and velocity.” The linked data movement and semantic web technologies try to address the issue of data variety. The current demand for advanced data analytics and services have triggered the shift from data services to knowledge services and delivery platforms. Semantics plays a major role in providing richer and more comprehensive knowledge services. We need a stable, sustainable, scalable and verifiable framework for knowledge-based semantic services. We also need a way to validate the “semantic” nature of such services using this framework. Just having a framework is not enough. The usability of this framework should be tested with a good example of a semantic service as a case study in a key research domain. The thesis addresses two research problems. Problem 1: A generalized framework for the development of end-to-end semantic services needs to be established. The thesis proposes such a framework that provides architecture for developing end–to–end semantic services and metrics for measuring its semantic nature. Problem 2: To implement a robust knowledge based service using the architecture proposed by the semantic service framework and its semantic nature can be validated using the proposed framework. ResearchIQ, a semantic search portal for resource discovery in the biomedical research domain, has been implemented. It is intended to serve as the required case study for testing the framework. The architecture of the system follows the design principles of the proposed framework. The ResearchIQ system is truly semantic from end-to-end. The baseline evaluation metrics of the said framework are used to prove this claim. Several key data sources have been integrated in the first version of the ResearchIQ system. It serves as a framework for semantic data integrat (open full item for complete abstract)

Committee: Jayashree Ramanathan PhD (Advisor); Rajiv Ramnath PhD (Committee Member) Subjects: Biomedical Research; Computer Engineering; Computer Science; Information Science; Information Systems; Information Technology