PHD, Kent State University, 2015, College of Arts and Sciences / Department of Computer Science

Currently, the most common method to access and utilize data on the Web is through the use of search engines. Classical Information Retrieval (IR) techniques, which the search engines depend on, have many limitations due to the string search mechanism. The problem is that these search techniques are not aware of the context of data on the Web. The underlying reason is the data on the Web was conventionally published as dumps of raw data in various file formats or wrapped in HTML markup. These data representations do not retain a substantial part of the semantics of the underlying data. The Semantic Web, also considered as Web 3.0, began to emerge as its standards and technologies developed rapidly in the recent years. With the continuing development of Semantic Web technologies, there has been significant progress including explicit semantics with data on the Web in RDF data model. This dissertation proposes a semantic search framework to support efficient keyword-based semantic search on RDF data utilizing near neighbor explorations. Also, a pairwise entity similarity metric is proposed for calculating the similarities of entities in the RDF graph. Additionally, we introduce a novel algorithm for generating the summary graph structure, which helps reduce the computational complexity for graph explorations automatically from underlying RDF data using the pairwise entity similarity metric. The framework augments the search results with the resources in close proximity by utilizing the entity type semantics. Along with the search results, the system generates a relevance confidence score measuring the inferred semantic relatedness of returned entities based on the degree of similarity. Furthermore, the evaluations assessing the effectiveness of the framework and the accuracy of the results are presented.

Committee: Austin Melton (Advisor) Subjects: Computer Science

PHD, Kent State University, 2015, College of Arts and Sciences / Department of Computer Science

Interoperability in healthcare is stated as the ability of health information systems to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities. The current healthcare information technology environment breeds incredibly complex data ecosystems. In many cases pertinent patient records are collected in multiple systems, often supplied by competing manufacturers with diverse data formats. This causes inefficiencies in data interoperability, as different formats of data create barriers in exchanging health information. This dissertation presents a semantic framework for healthcare information interoperability. We propose a system for translation of healthcare instance data, based on structured mapping definitions and using RDF as a common information representation to achieve semantic interoperability between different data models. Moreover, we introduce an entity similarity metric that utilizes the Jaccard index with the common relations of the data entities and common string literal words referenced by the data entities and augmented with data entity neighbors similarity. The precision of the similarity metric is enhanced by incorporating the auto-generated importance weights of the entity descriptors in the RDF representation of the dataset. Furthermore, we provide an automatic classification method, which we call summary graph generation, based on the pairwise entity similarities, and we propose that the summary graph can further be utilized for interoperability purposes. Finally, we present a suggestion based semi-automatic instance matching system and we test it on the RDF representation of a healthcare dataset. The system utilizes the entity similarity metric, and it presents similar node pairs to the user for possible instance matching. Based on the user feedback, it merges the matched nodes and suggests more matching pairs depending on the common relations and neigh (open full item for complete abstract)

Committee: Austin Melton (Advisor); Angela Guercio (Committee Member); Ye Zhao (Committee Member); Alan Brandyberry (Committee Member); Helen Piontkivska (Committee Member); Javed I. Khan (Committee Chair); James L. Blank (Other) Subjects: Computer Science; Health Care; Health Sciences; Information Systems; Information Technology; Medicine