DSpace@RPI
DSpace@RPI is a repository of Rensselaer Polytechnic Institute's theses and dissertations which are available in digital format, largely from 2006 to present, along with other selected resources.
Recent Submissions
Item Semantics-based Framework for Incentivized Research Data Sharing(LibraryPress@UF, 2023-05-08)We present a framework for incentivized research data sharing using an ontology called the Data Sharing Ontology (DSO). The DSO captures the semantics of academic research data sharing and provides an operational specification for data sharing between researchers. The DSO includes a two-part incentive mechanism to confirm citations and reward reproducible research methods. The proposed solution is demonstrated using a dataset-sharing decentralized application use case. The paper's contributions provide a scalable technique for creating, curating, publishing, and consuming web-based, structured, and reusable datasets, including semantically annotated knowledge graphs.Item Facilitating Reuse of Mental Health Questionnaires via Knowledge Graphs(KGC, 2023-05-08)Questionnaires are one of the most common instrument types for screening patients for mental disorders. They are composed of items whose answers are typically scored to determine the elevation on a specified dimension, and hence the statistical probabilities associated with the corresponding disorder or diagnosis. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) questionnaire, for instance, measure levels of depression and anxiety respectively, and can be used to support diagnosis of depression and generalized anxiety disorder. Some questionnaires are multidimensional, such as the Revised Children's Anxiety and Depression Scale (RCADS), and can thereby estimate elevations on multiple dimensions that underlie a variety of disorders. Mental health screening questionnaires are designed so that each item assesses specific symptoms whose pattern of co-occurence (often organized in a subscale) allows estimation of how likely such symptoms would occur in the absence of the disorder whose symptoms the items represent. Questionnaire users typically estimate how likely a set of co-occuring symptoms would be (i.e., a score) in the general population as a strategy to estimate the likelihood that the respondent has a disorder warranting mental health services. The RCADS is a 47-item, youth self-report questionnaire with subscales (separation anxiety disorder, social phobia, generalized anxiety disorder, panic disorder, obsessive compulsive disorder, and major depressive disorder). It also yields a Total Anxiety Scale (sum of the 5 anxiety subscales) and a Total Internalizing Scale (sum of all 6 …Item A Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning(Springer, Cham, 2023-05-22)When clinicians perform tasks involving clinical reasoning, such as the diagnosis or treatment of diabetes, multiple forms of reasoning, including deduction and abduction, are often employed. Ontologies designed to provide a foundation for clinical decision support systems have been encoded based on Clinical Practice Guidelines. Nevertheless, existing approaches solely allow deductive rules for clinical reasoning, with ontologies too large or complex to support tractable abductive reasoning. We follow existing guidelines and standards to design the Diabetes Pharmacology Ontology, a concise ontology – an ontology engineered by adhering to the Minimum Information to Reference an External Ontology Term principle and following an agile design approach. We claim that use cases that incorporate multiple forms of reasoning, such as those aimed at supporting both deduction and abduction, are better supported by concise, rather than complete and comprehensive, ontologies. We demonstrate how Personal Health Knowledge Graphs have been implemented using our ontology and evaluate the abductive capability of modules included with our ontology. We openly publish the resources that have resulted from this work, as listed below. This work demonstrates how multimodal semantic reasoning – deduction and abduction – can be used to emulate tasks involving clinical reasoning and thus has the potential to support practitioners with clinical decision-making.Item Web 3.0 Meets Web3: Exploring the Convergence of Semantic Web and Blockchain Technologies(CEUR, 2023-05-23)We outline the synergistic convergence of semantic web technologies, which have driven the advent of Web 3.0, and blockchain technologies, which have catalyzed the flourishing Web3 ecosystem. The integration of these technologies holds immense potential for transforming data representation, interoperability, and trust within decentralized knowledge graphs. The utilization of semantic web technologies enables the creation of machine-readable data formats, facilitating seamless understanding and exchange across heterogeneous systems. Complementing this, blockchain technologies provide an immutable and tamper-proof ledger, offering the foundation for establishing trust in decentralized knowledge graphs. We discuss the adoption of standardized vocabularies and smart contract powered schema alignment to enhance data exchange and integration with a focus on semantic interoperability, trustworthiness in semantic reasoning processes, and ownable and traceable resources.Item SciKG: Tutorial on Building Scientific Knowledge Graphs from Data, Data Dictionaries, and Codebooks(CUER, 2023-05-28)Data from scientific studies are published in datasets, typically accompanied by data dictionaries and codebooks to support data understanding. To conduct rigorous analysis, data users need to leverage this documentation to correctly interpret the data. While this process can be burdensome for new data users, it is also prone to errors even for seasoned users. A computational formal model of the knowledge that was used to create the study can facilitate better understanding and thus improved usage of the study data. Knowledge graphs can be used effectively to capture this study knowledge. The SciKG tutorial aimed to introduce participants to the basics of knowledge graph construction using data, data dictionaries, and codebooks from scientific studies. It used the Center for Disease Control and Prevention’s (CDC) National Health and Nutrition Examination Surveys (NHANES) data as a testbed and introduce standardized terminology, novel and established techniques, and resources such as scientific/biomedical ontologies, semantic data dictionaries, and knowledge graph frameworks in both lecture and practical sessions.
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