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

Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD

Linked data has experienced accelerated growth in recent years due to its interlinking ability across disparate sources, made possible via machine-processable RDF data. Today, a large number of organizations, including governments and news providers, publish data in RDF format, inviting developers to build useful applications through reuse and integration of structured data. This has led to tremendous increase in the amount of RDF data on the web. Although the growth of RDF data can be viewed as a positive sign for semantic web initiatives, it causes performance bottlenecks for RDF data management systems that store and provide access to data. In addition, a growing number of ontologies and vocabularies make retrieving data a challenging task. The aim of this research is to show how alignments in the Linked Data can be exploited to compress and query the linked datasets. First, we introduce two compression techniques that compress RDF datasets through identification and removal of semantic and contextual redundancies in linked data. Logical Linked Data Compression is a lossless compression technique which compresses a dataset by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Contextual Linked Data Compression is a lossy compression technique which compresses datasets by performing schema alignment and instance matching followed by pruning of alignments based on confidence value and subsequent grouping of equivalent terms. Depending on the structure of the dataset, the first technique was able to prune more than 50% of the triples. Second, we propose an Alignment based Linked Open Data Querying System (ALOQUS) that allows users to write query statements using concepts and properties not present in linked datasets and show that querying does not require a thorough understanding of the individual datasets and interconnecting relationships. Finally, we present LinkGen, a multipurpose synthetic Linke (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Guozhu Dong Ph.D. (Committee Member); Krishnaprasad Thirunaraya Ph.D. (Committee Member); Michelle Cheatham Ph.D. (Committee Member); Subhashini Ganapathy Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2008, Computer Science and Engineering PhD

Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. The analytical process often requires uncovering and analyzing complex thematic relationships between disparate people, places and events. Fundamentally new query operators based on the graph structure of Semantic Web data models, such as semantic associations, are proving useful for this purpose. However, these analysis mechanisms are primarily intended for thematic relationships. This dissertation proposes a framework built around the RDF data model for analysis of thematic, spatial and temporal relationships between named entities. We present a spatiotemporal modeling approach that uses an upper-level ontology in combination with temporal RDF graphs. A set of query operators that use graph patterns to specify a form of context are formally defined, and an extension of the W3C-recommended SPARQL query language to support these query operators is presented. We also describe an efficient implementation of the framework that extends a state-of-the-art commercial database system. We demonstrate the scalability of our approach with a performance study using both synthetic and real-world RDF datasets of over 25 million triples.

Committee: Amit Sheth PhD (Advisor); Krishnaprasad Thirunarayan PhD (Committee Member); Soon Chung PhD (Committee Member); Christopher Barton PhD (Committee Member); Kate Beard PhD (Committee Member) Subjects: Computer Science

Master of Science, Miami University, 2023, Mechanical and Manufacturing Engineering

Silver sulfide has garnered significant interest across a range of applications, such as resistive-switching, atomic switches, and neuromorphic systems, due to its exceptional superionic properties and memristive characteristics. An inherent challenge in atomic switches is the loss of accuracy over time, attributed to the accumulation of disorder within the silver sulfide crystal used in these switches. During the RESET and SET operations of the atomic switch, only a portion of the Silver Sulfide undergoes a transformation, resulting in a metaphase state of the atomic switch crystal. This constraint restricts the lifespan and reliability of the atomic switch. To address this, the present study employs MD simulation to investigate the local structural topology of polycrystalline silver sulfide. A numerical method was developed to quantitatively measure Relative Electrical Conductivity by examining the metaphase state of the silver sulfide atomic switch at a molecular level; this research aims to enhance our fundamental understanding of its behavior which can contribute to the development of improved atomic switch designs, and performance enhancements. In addition, it will be demonstrated in this study how the application of graph theory-based descriptors can link the structural characteristics of polycrystalline silver sulfide to their electrical properties.

Committee: Mehdi Zanjani (Advisor); Carter Hamilton (Committee Member); Giancarlo Corti (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Wright State University, 2019, Computer Science

Over the years, the semantic web has emerged as a new generation of the world wide web featuring advanced technologies and research contributions. It has revolutionized the usage of information by allowing users to capture and publish machine-understandable data and expedite methods such as ontologies to perform the same. These ontologies help in the formal representation of a specified domain and foster comprehensive machine understanding. Although, the engineering of ontologies and usage of logic have been an integral part of the web semantics, new areas of research such as the semantic web search, linking and usage of open data on the web, and the subsequent use of these technologies in building semantic web applications have also become significant in recent times. One such research contribution that we are going to focus on is the browsing of linked RDF data. Semantic web advocates the methodology of linked data to publish structured data on the web. Most of the linked data is available as browsable RDF data which is built using triples that define statements in the form of subject-predicate-object. These triples can be tabulated by sorting the three parts into separate columns. To browse the linked data of semantic web, several web browsers such as CubicWeb, VisiNav and Pubby were designed. These browsers provide the users with a tabular browsing experience displaying the data in nested tables. Also, they help users navigate through various subjects and their respective objects with the help of links associated with them. Several other browsers such as Tabulator were developed which enable real-time editing of semantic web resources\cite{berners2008tabulator} However, with the tabulated interface, users may sometimes find it difficult to realize the relationships between the various documents. Also navigating using the links between subjects and its predicates inside the documents is more time consuming which makes the overall user experience tedious. To i (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Mateen M. Rizki Ph.D. (Committee Member); Yong Pei Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD

Semantic Web technologies such as RDF and OWL have become World Wide Web Consortium (W3C) standards for knowledge representation and reasoning. RDF triples about triples, or meta triples, form the basis for a contextualized knowledge graph. They represent the contextual information about individual triples such as the source, the occurring time or place, or the certainty. However, an efficient RDF representation for such meta-knowledge of triples remains a major limitation of the RDF data model. The existing reification approach allows such meta-knowledge of RDF triples to be expressed in RDF by using four triples per reified triple. While reification is simple and intuitive, this approach does not have a formal foundation and is not commonly used in practice as described in the RDF Primer. This dissertation presents the foundations for representing, querying, reasoning and traversing the contextualized knowledge graphs (CKG) using Semantic Web technologies. A triple-based compact representation for CKGs. We propose a principled approach and construct RDF triples about triples by extending the current RDF data model with a new concept, called singleton property (SP), as a triple identifier. The SP representation needs two triples to the RDF datasets and can be queried with SPARQL. A formal model-theoretic semantics for CKGs. We formalize the semantics of the singleton property and its relationships with the triple it represents. We extend the current RDF model-theoretic semantics to capture the semantics of the singleton properties and provide the interpretation at three levels: simple, RDF, and RDFS. It provides a single interpretation of the singleton property semantics across applications and systems. A sound and complete inference mechanism for CKGs. Based on the semantics we propose, we develop a set of inference rules for validating and inferring new triples based on the SP syntax. We also derive different sets of context-based inference rules using latti (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Olivier Bodenreider Ph.D. (Committee Member); Kemafor Anyanwu Ph.D. (Committee Member); Ramanathan Guha Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD

The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored. In this dissertation, we focus on creating both \textit{comprehensive} and \textit{concise} entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Committee Co-Chair); Krishnaprasad Thirunarayan Ph.D. (Committee Co-Chair); Keke Chen Ph.D. (Committee Member); Gong Cheng Ph.D. (Committee Member); Edward Curry Ph.D. (Committee Member); Hamid Motahari Nezhad Ph.D. (Committee Member) Subjects: Computer Science

Master of Sciences, Case Western Reserve University, 2017, EECS - Electrical Engineering

The problem of graph summarization has practical applications involving visualization and graph compression. As graph-structured databases become popular and large, summarizing and compressing graph-structured databases can become more and more useful. We explore the use of a particular family of graph summarization algorithms we call Summaries with Supernodes, Superedges, and Corrections (SSSC) and the feasibility of using SSSC algorithms when summarizing large Resource Description Framework (RDF) graph datasets. We also propose optimizations to the Uniform Randomized SSSC algorithm by using trained heuristics to pick seed nodes. We also show how SSSC summaries may be stored in a similar manner as RDF triple stores, and we discuss possibilities for future work involving localized SSSC algorithms.

Committee: Zehra Ozsoyoglu (Committee Chair); Connamacher Harold (Committee Member); Koyuturk Mehmet (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2016, Computer Science

This thesis entitled A Performance Analysis Framework for Coreference Resolution Algorithms, focuses on the topic of coreference resolution of semantic datasets. In order for Big Data analytics to be effective, it is essential to develop automated algorithms capable of integrating multiple datasets that contain data about a particular person or other entity. Accomplishing this necessitates coreference resolution; for example, determining that J. Doe in one dataset refers to the same person as Jonathan Doe Jr. in another dataset. There are many existing coreference resolution algorithms, but there are only a few basic design decisions to be made by such systems when it comes to how to compare two individual instances. An analysis framework is presented that assesses the impact of different choices for these design decisions on coreference resolution in terms of precision, recall, and F-measure.

Committee: Michelle Cheatham Ph.D. (Advisor); Mateen Rizki Ph.D. (Committee Member); Tanvi Banerjee Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

PHD, Kent State University, 2016, College of Arts and Sciences / Department of Computer Science

The World Wide Web enables an easy, instant access to a huge quantity of information. Over the last few decades, a number of improvements have been achieved that helped the web reach its current state. However, the current Internet links documents together without understanding them, and thus, makes the content of web only human-readable rather than machine-understandable. Therefore, there is a growing need for an efficient web to make information machine understandable rather than only machine processable to reach to the web of knowledge. To cure this problem, the Semantic Web or what is called “web of meaning” tries to shift the thinking of published data in the form of web pages to allow machines to understand the contents. That is, computers are able to interoperate and think on our behalf, opening up several different perspectives. However, with the increasing quantity of semantic data, there is a need for efficient and scalable performance from semantic repositories which store and from which must be retrieving a large datasets contain Resource Description Framework -RDF- triples. This is a major obstacle to reaching the goal of the Semantic Web, and this problem is magnified by the unpredictable nature of the data encoded in RDF. Additionally, current RDF stores, in general, scale poorly, which may exacerbate the performance behavior for querying and retrieving RDF triples. As a consequence, we proposed new semantic storage models for managing RDF data in relational databases to show how a state-of-the-art scaling method can be improved with ontology-based techniques for speed and high scalability.

Committee: Austin Melton (Committee Chair); Angela Guercio (Committee Member); Ye Zhao (Committee Member); Alan Brandyberry (Committee Member); Mark Lewis (Committee Member) Subjects: Computer Science; Information Technology

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

Doctor of Philosophy, Case Western Reserve University, 2016, EECS - Computer and Information Sciences

Providing an efficient and expressive querying technique for graph structured RDF data is an emergent problem as large amounts of RDF data are available from applications in many areas. Current techniques do not fully satisfy this goal due to the nature of the RDF model which requires highly flexible use of keywords, and a structure expression in query language. Viewing RDF as graphs requires additional graph-based functionalities, such as querying a path or a tree connection. We propose a querying framework, called RDF-h, which uses the query template as a basic query unit, and supports both partially entered keywords and query conditions based on graph-structure. In order to provide efficient query evaluation, signature-based index is utilized. Though most existing techniques which utilize signature-based index claim its benefits on all datasets and queries. The effectiveness of signature-based pruning varies greatly among different RDF datasets and highly related with their dataset characteristics. The performance benefits from signature-based pruning depend not only on the size of the RDF graphs, but also the underlying graph structure and the complexity of queries. We propose several dataset evaluation metrics, namely, coverage and coherence, relationship specialty and literal diversity to understand the query performance differences among real and synthetic RDF datasets. Based on these results, we further propose an application-specific framework, called RBench, to generate RDF benchmarks. By evaluating the characteristics of RDF datasets and the complexity of query templates, RDF-h selectively utilizes signature-based pruning when it is considered to be beneficial. Two aspects of RDF-h framework are evaluated in experiments: 1. extensive query performance evaluation based on randomly generated queries for different datasets; 2. utilization of RDF-h for biomedical applications. For random query evaluation, the RDF-h algorithm can automatically capture freque (open full item for complete abstract)

Committee: Meral Özsoyoglu (Advisor); Gultekin Özsoyoglu (Committee Member); Mehmet Koyutürk (Committee Member); Marc Buchner (Committee Member); Soumya Ray (Committee Member); Andy Podgurski (Committee Member); Xiang Zhang (Committee Member) Subjects: Computer Science

Master of Sciences, Case Western Reserve University, 2016, EECS - Computer and Information Sciences

Query interface is an important tool in accessing graph databases. Traditional text-based query interfaces only provide access to databases through text queries and results, without presenting the internal graph structure. In this thesis we present a graphical query interface for exploratory querying of graph structured data, called Graph by Example (GBE). Our interface introduces a Draw and Play feature, which enables users to query graph structured data intuitively using graph templates. GBE interface also provides exploratory querying features facilitating the editing and reuse of previous queries so that users can reformulate their queries and resubmit based on the results of the previous queries. These features makes the interface simpler and easier to use compared to traditional text based query interfaces. We implement this query interface utilizing an RDF querying framework in a previous research. We also demonstrate the interface's functions and features through examples in real world databases and benchmarks.

Committee: Meral Ozsoyoglu (Advisor); Mehmet Koyuturk (Committee Member); Michael Lewicki (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2012, Computer Science

The terms Semantic Web and OWL are relatively new and growing concepts in the World Wide Web. Because these concepts are so new there are relatively few applications and/or tools for utilizing the potential power of this new concept. Although there are many components to the Semantic Web, this thesis will focus on the research question, "How do we go about developing a web crawler for the Semantic Web that locates and retrieves OWL documents." Specifically for this thesis, we hypothesize that by giving URIs to OWL documents, including all URIs from within these OWL documents, priority over other types of references, then we will locate more OWL documents than by any other type of traversal. We reason that OWL documents have proportionally more references to other OWL documents than non-OWL documents do, so that by giving them priority we should have located more OWL files when the crawl terminates, than by any other traversal method. In order to develop such an OWL priority queue, we needed to develop some heuristics to predict OWL documents during real-time parsing of Semantic Web documents. These heuristics are based on filename extensions and OWL language constructs, which are not absolute when predicting a document type before retrieval. However, if our reasoning is correct, then URIs found in an OWL document will likely lead to more OWL documents, such that when the crawl ends because of reaching a maximum document limit, we will have retrieved more OWL documents than by other methods such as breadth-first or load-balanced. We conclude our research with an evaluation of our results to test the validity of our hypothesis and to see if it is worthy of future research.

Committee: Pascal Hitzler PhD (Committee Chair); Gouzhu Dong PhD (Committee Member); Krishnaprasad Thirunarayan PhD (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2011, Computer Science

The emergence of dynamic information sources - like social, mobile and sensors, has led to ginormous streams of real time data on the web also called, the era of Big Data [1]. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. Gigaom article on Big data shows, how the total information generated by these dynamic information sources has completely surpassed the total storage capacity. Thus keeping in mind the problem of ever-increasing data, this thesis focuses on semantically integrating and analyzing multiple, multimodal, heterogeneous streams of weather data with the goal of creating meaningful thematic abstractions in real-time. This is accomplished by implementing an infrastructure for creating and mining thematic abstractions over massive amount of real-time sensor streams. Evaluation section shows 69% data reduction with this approach.

Committee: Amit Sheth PhD (Advisor); Ramakanth Kavaluru PhD (Committee Member); Krishnaprasad Thirunarayan PhD (Committee Member) Subjects: Computer Science; Geographic Information Science

PhD, University of Cincinnati, 2009, Engineering : Biomedical Engineering

Finding connections between an existing drug product and its new application areas evolves to be one of the alternative and efficient strategies for new drug development. However, the identification of such connections remains highly dependent on serendipitous observation and educated guess. A forecasting informatics model that can improve data capture, integration, prediction and interpretation of potential new therapeutic indications for drugs based on integrated biomedical knowledge around drug and disease mechanisms is highly desirable. To pursue a systematic approach to the discovery of novel and inferable relationships between drugs and diseases based on mechanistic knowledge, we aim to develop a semantic infrastructure that integrates heterogeneous data from pharmacological and biological domains to allow efficient mining of non-trivial connections among biomedical and pharmacological entities across knowledge domains. In this work, we devised a Disease-Drug Correlation Ontology (DDCO), an ontological framework to integrate varied datasets extracted from pharmacological and biological domains. The DDCO, formalized in OWL, allows the integrated representation of multiple sources of ontologies, controlled vocabularies, and data schemas. We used the DDCO framework to integrate and represent a collection of data sources including DrugBank, EntrezGene, OMIM, KEGG, BioCarta, Reactome, Human Phenome, and UMLS, and constructed a comprehensive Pharmacome-Diseasome network using RDF, which represents data in conceptual graphic format. We established and validated the multiple applications using the constructed knowledge base. More importantly, we implemented graph theoretic-based network ranking algorithms onto disease-specific BioRDF to identify and prioritize drugs for new therapeutic utilities. The work is a pioneering effort in leveraging on Semantic Web principles and technologies to apply on pharmaceutical development problems from data integration, knowledge rep (open full item for complete abstract)

Committee: Bruce Aronow PhD (Committee Chair); Anil Jegga DVM, MRes (Committee Member); Marepalli Rao PhD (Committee Member); Eric Newman PhD (Committee Member) Subjects: Bioinformatics

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:

MLIS, Kent State University, 2013, College of Communication and Information / School of Information

Linked Data has become an increasingly important and valuable way for sharing data across the Internet. It is the basis for the Semantic Web and allows organizations to not only easily share data, but also connect data with other related data. Visual Resource Association (VRA) Core 4 is an XML schema-based data model for cataloging cultural objects and visual resources. Using the existing VRA Core 4 restricted XML schema, a new data model was developed that took advantage of popular domain specific vocabularies. Using popular vocabularies such as Schema.org, helps ensure that data will be interoperable with other data and can potentially help improve visibility on the Internet. Using the data model as a reference, an ontology was developed using Protege ontology editor. It illustrated how popular domain specific vocabularies can be combined with the existing VRA data model to create a new semantically-rich model that still retains the specificity and detail of the original XML restricted schema. In addition to developing a new VRA data model, an XSLT stylesheet was created that demonstrated how existing XML based records could be converted into RDF data. The stylesheet was used to successfully convert a 4,150 record collection from the University of Notre Dame into RDF triples. The XSLT templates used in the stylesheet were able to not only convert the existing XML elements/attributes into RDF classes/properties but also convert the existing controlled vocabulary terms into functioning http URIs representing concepts. The study successfully demonstrated that existing data models can be enhanced to incorporate Linked Data and that existing datasets of implementation-specific XML records can be converted into RDF triples with properties defined by popular RDF vocabularies using an XSLT stylesheet.

Committee: Marcia Zeng Ph.D. (Advisor); Yin Zhang Ph.D. (Committee Member); Athena Salaba Ph.D. (Committee Member) Subjects: Information Science; Library Science