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

Doctor of Philosophy (PhD), Wright State University, 2009, Computer Science and Engineering PhD

During the past seven years services centric computing has emerged as the preferred approach to architect complex software. Software is increasingly developed by integrating remotely existing components, popularly called services. This architectural paradigm, also called Service Oriented Architecture (SOA), brings with it the benefits of interoperability, agility and flexibility to software design and development. One can easily add or change new features to existing systems, either by the addition of new services or by replacing existing ones. Two popular approaches have emerged for realizing SOA. The first approach is based on the SOAP protocol for communication and the Web Service Description Language (WSDL) for service interface description. SOAP and WSDL are built over XML, thus guaranteeing minimal structural and syntactic interoperability. In addition to SOAP and WSDL, the WS-* (WS-Star) stack or SOAP stack comprises other standards and specification that enable features such as security and services integration. More recently, the RESTful approach has emerged as an alternative to the SOAP stack. This approach advocates the use of the HTTP operations of GET/PUT/POST/DELETE as standard service operations and the REpresentational State Transfer (REST) paradigm for maintaining service states. The RESTful approach leverages on the HTTP protocol and has gained a lot of traction, especially in the context of consumer Web applications such as Maps. Despite their growing adoption, the stated objectives of interoperability, agility, and flexibility have been hard to achieve using either of the two approaches. This is largely because of the various heterogeneities that exist between different service providers. These heterogeneities are present both at the data and the interaction levels. Fundamental to addressing these heterogeneities are the problems of service Description, Discovery, Data mediation and Dynamic configuration. Currently, service description (open full item for complete abstract)

Committee: Amit Sheth PhD (Committee Chair); Michael Raymer PhD (Committee Member); Lakshmish Ramaswamy PhD (Committee Member); Shu Schiller PhD (Committee Member); Guozhou Dong PhD (Committee Member); Krishnaprasad Thirunarayan PhD (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2013, Computer Science

With sensors, storage, and bandwidth becoming ever cheaper, there has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web and then interpreting their observations is a non-trivial challenge. The Open Geospatial Consortium (OGC) defines a web service specification known as the Sensor Observation Service (SOS) that is designed to standardize the way sensors and sensor data are discovered and accessed on the Web. Though this standard goes a long way in providing interoperability between sensor data producers and consumers, it is predicated on the idea that the consuming application is equipped to handle raw sensor data. Sensor data consuming end-points are generally interested in not just the raw data itself, but rather actionable information regarding their environment. The approaches for dealing with this are either to make each individual consuming application smarter or to make the data served to them smarter. This thesis presents an application of the latter approach, which is accomplished by providing a more meaningful representation of sensor data by leveraging semantic web technologies. Specifically, this thesis describes an approach to sensor data modeling, reasoning, discovery, and query over richer semantic data derived from raw sensor descriptions and observations. The artifacts resulting from this research include: - an implementation of an SOS service which hews to both Sensor Web and Semantic Web standards in order to bridge the gap between syntactic and semantic sensor data consumers and that has been proven by use in a number of research applications storing large amounts of data, which serves as - an example of an approach for designing applications which integrate syntactic services over semantic models and allow for interactions with external reasoning systems. As more sensors and observations move o (open full item for complete abstract)

Committee: Krishnaprasad Thirunarayan Ph.D. (Advisor); Amit Sheth Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member) Subjects: Computer Science; Geographic Information Science; Information Systems; Remote Sensing; Systems Design; Web Studies

Doctor of Philosophy (PhD), Wright State University, 2013, Computer Science and Engineering PhD

Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices. Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates predict over 50 billion sensors connected to the Web by 2020. While the challenge of data deluge is formidable, a resolution has profound implications. The ability to translate low-level data into high-level abstractions closer to human understanding and decision-making has the potential to disrupt data-driven interdisciplinary sciences, such as environmental science, healthcare, and bioinformatics, as well as enable other emerging technologies, such as the Internet of Things. The ability to make sense of sensory input is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Machine perception is a hard problem in computer science, with many fundamental issues that are yet to be adequately addressed, including: (a) annotation of sensor data, (b) interpretation of sensor data, and (c) efficient implementation and execution. This dissertation presents a semantics-based machine perception framework to address these issues. The tangible primary contributions created to support the thesis of this dissertation include the development of a Semantic Sensor Observation Service (SemSOS) for accessing and querying sensor data on the Web, an ontology of perception (Intellego) that provides a formal semanti (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Payam Barnaghi Ph.D. (Committee Member); Satya Sahoo Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Science

Doctor of Philosophy (PhD), Wright State University, 2015, Computer Science and Engineering PhD

Ontology alignment plays a key role in enabling interoperability among various data sources present in the web. The nature of the world is such, that the same concepts differ in meaning, often so slightly, which makes it difficult to relate these concepts. It is the omni-present heterogeneity that is at the core of the web. The research work presented in this dissertation, is driven by the goal of providing a robust ontology alignment language for the semantic web, as we show that description logics based alignment languages are not suitable for aligning ontologies. The adoption of the semantic web technologies has been consistently on the rise over the past decade, and it continues to show promise. The core component of the semantic web is the set of knowledge representation languages -- mainly the W3C (World Wide Web Consortium) standards Web Ontology Language (OWL), Resource Description Framework (RDF), and Rule Interchange Format (RIF). While these languages have been designed in order to be suitable for the openness and extensibility of the web, they lack certain features which we try to address in this dissertation. One such missing component is the lack of non-monotonic features, in the knowledge representation languages, that enable us to perform common sense reasoning. For example, OWL supports the open world assumption (OWA), which means that knowledge about everything is assumed to be possibly incomplete at any point of time. However, experience has shown that there are situations that require us to assume that certain parts of the knowledge base are complete. Employing the Closed World Assumption (CWA) helps us achieve this. Circumscription is a very well-known approach towards CWA, which provides closed world semantics by employing the idea of minimal models with respect to certain predicates which are closed. We provide the formal semantics of the notion of Grounded Circumscription, which is an extension of circumscription with desirable propert (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Krzysztof Janowicz Ph.D. (Committee Member); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Prabhaker Mateti Ph.D. (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

Doctor of Philosophy (PhD), Wright State University, 2012, Computer Science and Engineering PhD

The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 295 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud, as we will illustrate,are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short. This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.

Committee: Amit Sheth PhD (Advisor); Pascal Hitzler PhD (Committee Member); Krishnaprasad Thirunarayan PhD (Committee Member); Kunal Verma PhD (Committee Member); Peter Yeh PhD (Committee Member) Subjects: Computer Science

Master of Science, University of Akron, 2008, Computer Science

Web Services are proving to be a convenient way to integrate distributed software applications. As service-oriented architecture is getting popular, vast numbers of web services have been developed all over the world. But it is a challenging task to find the relevant or similar web services using web services registry such as UDDI. Current UDDI search uses keywords from web service and company information in its registry to retrieve web services. This information cannot fully capture user's needs and may miss out on potential matches. Underlying functionality and semantics of web services need to be considered. In this study, we explore semantics of web services using WSDL operation names and parameter names along with WordNet. We compute semantic similarity of web services and use this data to generate clusters. Then, we use a novel approach to represent the clusters and utilize that information to further predict similarity of any new web services. This approach has really yielded good results and can be efficiently used by any web service search engine to retrieve similar or related web services.

Committee: Chien-Chung Chan (Advisor) Subjects: Computer Science

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy (PhD), Wright State University, 2022, Computer Science and Engineering PhD

This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug's legalization and the sentiment analysis of drug consumption on Twitter. We collected 7.5 million data from August 2015 to March 2016. This work leveraged a longstanding, multidisciplinary collaboration between researchers at the Population & Center for Interventions, Treatment, and Addictions Research (CITAR) in the Boonshoft School of Medicine and the Department of Computer Science and Engineering. In addition, we aimed to develop and deploy an innovative prediction analysis algorithm for eDrugTrends, capable of semi-automated processing of Twitter data to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. In addition, the study included aim four, a use case study defined by tweets content analyzing PLWH, medication patterns, and identifying keyword trends via Twitter-based, user-generated content. This case study leveraged a multidisciplinary collaboration between researchers at the Departments of Family Medicine and Population and Public Health Sciences at Wright State University's Boonshoft School of Medicine and the Department of Computer Science and Engineering. We collected 65K data from February 2022 to July 2022 with the U.S.-based HIV knowledge domain recruited via the Twitter API streaming platform. For knowledge discovery, domain knowledge plays a significant role in powering many intelligent (open full item for complete abstract)

Committee: Michael Raymer Ph.D. (Committee Co-Chair); Tim Crawford Ph.D. (Committee Co-Chair); Reza Sadeghi Ph.D. (Committee Member); Lingwei Chen Ph.D. (Committee Member); Mahdiyeh Zabihimayvan Ph.D. (Committee Member); Thomas Wischgoll Ph.D. (Committee Member) Subjects: Artificial Intelligence; Behavioral Sciences; Bioinformatics; Biomedical Research; Computer Science; Health Care; Public Health

PHD, Kent State University, 2021, College of Communication and Information

The coronavirus disease 2019 (COVID-19) pandemic has revealed challenges and opportunities for data analytics, semantic interoperability, and decision making. The sharing of COVID-19 data has become crucial for leveraging research, testing drug effectiveness and therapeutic strategies, and developing policies for control, intervention, and potential eradication of this disease. Translating healthcare data between various clinical coding schemes is critical to their functioning, and semantic mappings must be established to ensure interoperability. Using design science research methodology as a guide, this work explains 1) how an ETL (Extract Transform Load) workflow tool could support the task of clinical coding scheme mapping, 2) how the mapping output from such a tool could support or affect annotation of clinical trials, particularly those used in COVID-19 research and 3) whether aspects of the socio-technical model could be leveraged to explain and assess mapping to achieve semantic interoperability in clinical coding schemes. Research outcomes include a reproducible and shareable artifact, that can be utilized beyond the domain of biomedicine in addition to observations and recommendations from the knowledge gained during the design and evaluation process of the artifact development.

Committee: Marcia Zeng (Advisor); Athena Salaba (Committee Member); Mary Anthony (Committee Member); Yi Hong (Committee Member); Rebecca Meehan (Committee Member) Subjects: Bioinformatics; Information Science

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

In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic Web Reasoning and querying. Researchers at Data Semantic Laboratory(DaSe lab) have developed a method to train a deep learning model which is based on End-to-End memory network over RDF knowl- edge graphs which can be able to perform reasoning over new RDF graph with the help of triple normalization with high precision and recall when compared to traditional deduc- tive algorithms. Researchers have also found out that its 40 times faster to train than the non-normalized model on a dataset which they have performed experiments on. They have created efficient model capable of transferring its reasoning ability ( by applying normal- ization ) from one domain to another without any re/pre-training or fine-tunning over new domain which constitutes Transfer learning. In this thesis, we are testing the transfer learning approach on the research which is done by Bassem Makni and James Hendler ”Deep Learning for Noise-tolerant RDFS reasoning”. The main limitation of their approach is that the training is done on a dataset that uses only on ontology for the inference. We found out that their approach is not suitable for Transfer Learning which will help to reason over different ontologies/domains.

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

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

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

One hotly debated research topic is, “What is the best approach for modeling ontologies?”. In the earlier stages of modeling ontologies, researchers have favored the usage of description logic to capture knowledge. One such choice is the Web Ontology Language (OWL) that is based on description logic. Many tools were designed around this principle and are still widely being used to model and explore ontologies. However, not all users find description logic to be intuitive, at least not without an extensive background in formal logics. Due to this, researchers have tried to explore other ways that will enable such users to model ontologies intuitively. One such approach was the exploration of rule-based paradigm. For many users rule-based approach was more natural and intuitive way of representing knowledge. Hence, Semantic Web Rule Language (SWRL) became an extremely popular choice among researchers and naive users. However, one major problem with SWRL is that machines cannot readily process it. Therefore, researchers at Data Semantics Laboratory (DaSe Lab) created an interactive plugin, called ROWLTab, that efficiently converts rules into OWL axioms. This plugin allows a user to create ontologies using SWRL syntax and convert it into OWL axioms. However, the tool does not support translation of rules that contains a disjunction and existential quantifiers in the consequent. In this paper we will discuss the modifications that were successfully made to the existing plugin to support the translation of right-hand existential and disjunction rules. We posit that this modification gives the user the ability to model more realistic and complex ontologies. Lastly, our evaluation shows that the tool can add 89% of the rules that contains an existential quantifier and 65% of the rules that contains a disjunction. We also discuss why we were unable to insert the remaining rules more in detail

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

Master of Science in Computer Engineering (MSCE), Wright State University, 2018, Computer Engineering

When presented with many search results, finding information or patterns within the data poses a challenge. This thesis presents the design, implementation and evaluation of a visualization enabling users to browse through voluminous information and comprehend the data. Implemented with the JavaScript library Data Driven Documents (D3), the visualization represents the search as clusters of similar documents grouped into bubbles with the contents depicted as word-clouds. Highly interactive features such as touch gestures and intuitive menu actions allow for expeditious exploration of the search results. Other features include drag-and-drop functionality for articles among bubbles, merging nodes, and refining the search by selecting specific terms or articles to receive more similar results. A user study consisting of a survey questionnaire and user tracking data demonstrated that in comparison to a standard text-browser for viewing search results, the visualization performs commensurate or better on most metrics.

Committee: Thomas Wischgoll Ph.D. (Advisor); Michael Raymer Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Computer Engineering; 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 Science in Cyber Security (M.S.C.S.), Wright State University, 2016, Computer Engineering

Data generalization (anonymization) is a widely misunderstood technique for preserving individual privacy in non-interactive data publishing. Easily avoidable anonymization failures are still occurring 14 years after the discovery of basic techniques to protect against them. Identities of individuals in anonymized datasets are at risk of being disclosed by cyber attackers who exploit these failures. To demonstrate the importance of proper data anonymization we present three perspectives on data anonymization. First, we examine several de-anonymization attacks to formalize the anatomy used to conduct attacks on anonymous data. Second, we examine the vulnerabilities of an anonymous nursing workforce survey to convey how this attack anatomy can still be applied to recently published anonymous datasets. We then analyze the impact proper generalization techniques have on the nursing workforce data utility. Finally, we propose the impact emerging technologies will have on de-anonymization attack sophistication and feasibility in the future.

Committee: Michelle Cheatham Ph.D. (Committee Chair); John Gallagher Ph.D. (Committee Member); Thomas Wischgoll Ph.D. (Committee Member); Robert Fyffe Ph.D. (Other); Mateen Rizki Ph.D. (Other) Subjects: Computer Engineering; Computer Science; Information Science; Information Technology

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

Doctor of Philosophy (PhD), Wright State University, 2016, Computer Science and Engineering PhD

The vision of the Semantic Web is to provide structure and meaning to the data on the Web. Knowledge representation and reasoning play a crucial role in accomplishing this vision. OWL (Web Ontology Language), a W3C standard, is used for representing knowledge. Reasoning over the ontologies is used to derive logical consequences. A fixed set of rules are run on an ontology iteratively until no new logical consequences can be derived. All existing reasoners run on a single machine, possibly using multiple cores. Ontologies (sometimes loosely referred to as knowledge bases) that are automatically constructed can be very large. Single machine reasoners will not be able to handle these large ontologies. They are constrained by the memory and computing resources available on a single machine. In this dissertation, we use distributed computing to find scalable approaches to ontology reasoning. In particular, we explore four approaches that use a cluster of machines for ontology reasoning -- 1) A MapReduce approach named MR-EL where reasoning happens in the form of a series of map and reduce jobs. Termination is achieved by eliminating the duplicate consequences. The MapReduce approach is simple, fault tolerant and less error-prone due to the usage of a framework that handles aspects such as communication, synchronization etc. But it is very slow and does not scale well with large ontologies. 2) Our second approach named DQuEL is a distributed version of a sequential reasoning algorithm used in the CEL reasoner. Each node in the cluster applies all of the rules and generates partial results. The reasoning process terminates when each node in the cluster has no more work to do. DQuEL works well on small and medium sized ontologies but does not perform well on large ontologies. 3) The third approach, named DistEL, is a distributed fixpoint iteration approach where each node in the cluster applies only one rule to a subset of the ontology. This happens iteratively until al (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Prabhaker Mateti Ph.D. (Committee Member); Derek Doran Ph.D. (Committee Member); Freddy Lecue Ph.D. (Committee Member); Frederick Maier Ph.D. (Committee Member) Subjects: Computer Science