Indiana University-Purdue University Indianapolis (IUPUI) / With the rise of big data and the demands for leveraging artificial intelligence (AI), healthcare requires more knowledge sharing that offers machine-readable semantic formalization. Even though some applications allow shared data interoperability, they still lack formal machine-readable semantics in ICD9/10 and LOINC. With ontology, the further ability to represent the shared conceptualizations is possible, similar to SNOMED-CT. Nevertheless, SNOMED-CT mainly focuses on electronic health record (EHR) documenting and evidence-based practice. Moreover, due to its independence on data quality, the ontology enhances advanced AI technologies, such as machine learning (ML), by providing a reusable knowledge framework. Developing a machine-readable and sharable semantic knowledge model incorporating external evidence and individual practice’s values will create a new revolution for best practice medicine. The purpose of this research is to implement a sharable ontology for the best practice in healthcare, with anterior cruciate ligament reconstruction (ACLR) as a case study. The ontology represents knowledge derived from both evidence-based practice (EBP) and practice-based evidence (PBE). First, the study presents how the domain-specific knowledge model is built using a combination of Toronto Virtual Enterprise (TOVE) and a bottom-up approach. Then, I propose a top-down approach using Open Biological and Biomedical Ontology (OBO) Foundry ontologies that adheres to the Basic Formal Ontology (BFO)’s framework. In this step, the EBP, PBE, and statistic ontologies are developed independently. Next, the study integrates these individual ontologies into the final ACLR Ontology (ACLRO) as a more meaningful model that endorses the reusability and the ease of the model-expansion process since the classes can grow independently from one another. Finally, the study employs a use case and DL queries for model validation. The study's innovation is to present the ontology implementation for best-practice medicine and demonstrate how it can be applied to a real-world setup with semantic information. The ACLRO simultaneously emphasizes knowledge representation in health-intervention, statistics, research design, and external research evidence, while constructing the classes of data-driven and patient-focus processes that allow knowledge sharing explicit of technology. Additionally, the model synthesizes multiple related ontologies, which leads to the successful application of best-practice medicine.

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The Semantic Web was introduced to bring structure to the Web. The goal is to allow computer agents to be able to traverse the web and carry out tasks for human users. In the Semantic Web, data is stored using the RDF data model. The purpose of this study is to explore the possibility of exposing GraphQL data to the Semantic Web using a data-to-data translation inspired by Ontology Based Data Access (OBDA). This was done by introducing GraphQL2RDF, a proof-of-concept method to materialize GraphQL data as RDF triples. GraphQL2RDF uses two mapping schemas: a GraphQL-mapping schema annotated with directives to filter and select GraphQL data and an RDF-mapping schema to specify which RDF triples to create. GraphQL2RDF supports directives for filtering based on the SQL where-clause used for filtering in SQL. The approach is demonstrated in a library use-case, in which library data exposed in a GraphQL endpoint is mapped into RDF. GraphQL2RDF demonstrates a method for exposing GraphQL data as RDF, while imposing a minimal set of requirements on the GraphQL endpoint. Future work includes improvements of the model and exploring extensions of this translation method towards an OBDA approach that does not require full materialization of the RDF data.

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This dissertation addresses issues for the efficient access to Web databases and services. We propose a distributed ontology for a meaningful organization of and efficient access to Web databases. Next, we dedicate most of our work on presenting a comprehensive query infrastructure for the emerging concept of Web services. The core of this query infrastructure is to enable the efficient delivery of Web services based on the concept of Quality of Web Service. Treating Web services as first class objects is a fundamental step towards achieving the envisioned Semantic Web. Semantics-aware processing of information requires intensive use of Web services. In our research, we propose a new query model where queries are resolved by combining Web service invocations. To efficiently deploy such scheme, we propose an optimization strategy based on aggregating Quality of Web service (QoWS) of different Web services. QoWS is adjusted through a dynamic rating scheme and multilevel matching. Web service rating provides an assessment of their behavior. The multilevel matching allows a larger solution space by enabling similar and partial answers. / Ph. D.

In this thesis, we present a novel approach for the preservation of trade secrets in a Business-to-Business (B2B) environment that involves trade among competitors. The Web provides a low cost medium for B2B collaborations. Information exchange may take place during such a collaboration. The exchanged information may be of a sensitive nature, forming a business trade secret. The open nature of the Web calls for techniques to prevent the disclosure of trade secrets. The emerging Semantic Web is expected to make the challenge more acute in terms of trade secret protection due to the automation of B2B interactions. In this thesis, the different businesses are represented by Web services on the envisioned Semantic Web. We propose a Peer-to-Peer (P2P) approach for preserving trade secrets in B2B interactions. We introduce a set of techniques based on data perturbation for preserving data privacy. The techniques presented in our thesis are implemented in WebBIS, a prototype for accessing e-business Web services. Finally, we conduct an extensive performance study (analytical and experimental) of the proposed techniques. / Master of Science

Yes / Welcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies.