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

Understanding users' latent intents behind search queries is essential for satisfying a user's search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Hence, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and forms one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd-sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been eva (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Michael Raymer Ph.D. (Committee Member); Jyotishman Pathak Ph.D. (Committee Member) Subjects: Computer Science

Master of Science, Miami University, 2024, Computer Science and Software Engineering

Drawing inspiration from lateral thinking, synergetics, psychology, creativity, and business, this research project employs an interdisciplinary approach to investigate the research process which drives innovation in the field of artificial intelligence. This research project explores methods for harnessing the synergy present in the latest, neuro-symbolic paradigm of artificial intelligence, while noting similarities between the first two waves of AI and dual process theory. It attempts to integrate unconventional, yet potentially promising interdisciplinary ideas into a proof of concept, including creative tools and techniques like the Six Thinking Hats, methods of psychotherapy, including cognitive behavioral therapy and internal family systems, as well as principles related to conflict resolution and ``tensegrity". The proof of concept is a hybrid semantic search system for research papers in computer science, constructed using a process of rapid prototyping and iteration, with special consideration for evaluating how more modular, interpretable, and human-centric approaches to system design can help narrow the gap between cutting-edge AI research and ethical, practical application in business. This research is conducted with the hope of opening the research field to greater creative possibility, as well as deliberate action towards creating more sustainable and human-centric artificial intelligence systems.

Committee: Daniela Inclezan (Advisor); Hakam Alomari (Committee Member); John Femiani (Committee Member) 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

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

Master of Science, The Ohio State University, 2013, Computer Science and Engineering

The software architecture greatly determines the quality of the system. Evaluating the architecture during the early stage of development can reduce the risk. When used appropriately, it will have a favorable effect on the system. Architectural Tradeoff Analysis Method (ATAM) is an architecture evaluation technique for understanding the tradeoffs in the architecture of software systems. This thesis describes the application of ATAM to evaluate the query engine component of the ResearchIQ. ResearchIQ is a semantically anchored resource discovery tool, which will help the researchers in the domain of clinical and translation science to discover the resources in a simplified manner. The primary goal of ResearchIQ is the delivery of search results effectively to the researchers. A large part of thesis is devoted in evaluating the architectural alternatives of the query engine component of ResearchIQ using ATAM. Three initial architectures alternatives are presented in the thesis. The thesis introduces the system (ResearchIQ) being evaluated, its business drivers and background. It also provides a general overview of the ATAM process describes the application of the ATAM to the ResearchIQ system and presents the important results. The document is intended as a report to develop a prototype implementation that led to the final framework that enhances the performance of ResearchIQ.

Committee: Jayashree Ramanathan Dr (Advisor); Rajiv Ramnath Dr (Advisor) Subjects: Computer Science

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

Digital photography technologies permit quick and easy uploading of any image to the web. Millions of images being are uploaded on the World Wide Web every day by a wide range of users. Most of the uploaded images are not readily accessible as they are not organized so as to allow efficient searching, retrieval, and ultimately browsing. Currently major commercial search engines utilize a process known as Annotation Based Image Retrieval (ABIR) to execute search requests focused on retrieving an image. Even though the information sought is an image, the ABIR technique primarily relies on textual information associated with an image to complete the search and retrieval process. For the first phase of the study, using the game of cricket as the domain, this research compared the performance of three commonly used search engines for image retrieval: Google, Yahoo and MSN Live. Factors used for the evaluation of these search engines include query types, number of images retrieved, and the type of search engine. Results of the empirical evaluation show that while the Google search engine performed better than Yahoo and MSN Live in situations where there is no refiner, the performance of all three search engines dropped drastically when a refiner was added. The other methodology to search for images is Content Based Image Retrieval (CBIR) which searches for the images based on the image features such as color, texture, and shape is still at a nascent stage and has not been incorporated in the commercial search engines. The image features are at a low level compared to the high level textual features. The gap between the low level image features and the high level textual features is termed as Semantic Gap. Semantic gap has been the factor that limits the Content Based algorithms to perform effectively. This research addresses the issue of the image retrieval problem by systematically coupling the ABIR and the CBIR algorithms and uses the human input wherever needed to re (open full item for complete abstract)

Committee: S. Narayanan PhD, PE (Advisor); Jennie Gallimore PhD (Committee Member); Fred Garber PhD (Committee Member); Yan Liu PhD (Committee Member); Richard Koubek PhD (Committee Member) Subjects: Engineering

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