Social network data analysis becomes increasingly important today. In order to improve the integration and reuse of their data, many social networks start to apply RDF to present the data. Accordingly, one common approach for social network data analysis is to employ SPARQL to query RDF data. As the sizes of social networks expand rapidly, queries need to be executed in parallel such as using the MapReduce framework. However, the state-of-the-art translation from SPARQL queries to MapReduce jobs mainly follows a two layer rule, in which SPARQL is first translated to SQL join, is not efficient. In this thesis, we introduce two primitives to enable automatic translation from SPARQL to MapReduce, and to enable efficient execution of the SPARQL queries. We use multiple-join-with-filter to substitute traditional SQL multiple join when feasible, and merge different stages in the MapReduce query workflow. The evaluation on social network benchmarks shows that these two primitives can achieve up to 2x speedup in query running time compared with the original two layer scheme.

The simplicity and proliferation of the World Wide Web (WWW) has taken the availability of information to an unprecedented level. The next generation of the Web, the Semantic Web, seeks to make information more usable by machines by introducing a more rigorous structure based on ontologies. One hinderance to the Semantic Web is the lack of existing semantically marked-up data. Until there is a critical mass of Semantic Web data, few people will develop and use Semantic Web applications. This project helps promote the Semantic Web by providing content. We apply existing information-extraction techniques, in particular, the BYU ontologybased data-extraction system, to extract information from the WWW based on a Semantic Web ontology to produce Semantic Web data with respect to that ontology. As an example of how the generated Semantic Web data can be used, we provide an application to browse the extracted data and the source documents together. In this sense, the extracted data is superimposed over or is an index over the source documents. Our experiments with ontologies in four application domains show that our approach can indeed extract Semantic Web data from the WWW with precision and recall similar to that achieved by the underlying information extraction system and make that data accessible to Semantic Web applications.

The present study attempts to find an alternate computational tool to model the complex physical interactions within the molten salt FLiNaK in a way that is both efficient and accurate. Additionally, this study seeks to describe the effects of several different types of impurities on the FLiNaK salt system. This study selects two different polarizable force fields, the AMOEBA polarizable approach and the polarizable ion model, to determine the density and the structure of the impure FLiNaK salt mixtures at typical operating temperatures in molten salt reactors (between 500-900 °C). This study conducts ab initio molecular dynamics (AIMD) simulations and classical molecular dynamics (CMD) for these salt mixtures to determine the correct parameter set for these two force fields. This study also uses an optimizer to minimize the difference between the forces calculated with AIMD and CMD simulation data. The AMOEBA polarizable approach is able to predict density for FLiNaK; however, it is unable to reliably predict other thermophysical properties due to the instability of its CMD simulations. The polarizable ion model is able to reliably determine density and salt structure for pure and impure FLiNaK mixtures. This model can be further used to determine other thermophysical properties. The polarizable ion model predicted densities for four impure salt mixtures: FLiNaK-MoF3, FLiNaK-UF3, FLiNaK-CsF, and FLiNaK-ZrF4. The predicted densities at 700 °C given in kg/m3 are 1929.94, 2454.15, 1650.67, and 1961.87, respectively with an error compared to the additive density model of -2.51%, -5.79%, -17.15%, and -1.67%, respectively. This study presents the radial distribution function and density correlation functions for each salt mixture. This study also presents a discussion of the shortcomings of the AMOEBA polarizable approach, as well as further work that may be done with these tools.

The Semantic Web has ushered in a vast repository of openly available data across various domains, resulting in over ten thousand Knowledge Graphs (KGs) published under the Linked Open Data (LOD) cloud. However, the exploration of these KGs can be time-consuming and resource-intensive, compounded by issues of availability and duplication across distributed and decentralized databases. Addressing these challenges, this thesis investigates methods for improving information retrieval over Linked Data (LD) through conceptual approaches facilitating access via formal and natural language queries. First, RDFSlice is introduced to efficiently select relevant fragments of RDF data from distributed KGs, demonstrating superior performance compared to conventional methods. Second, a novel distributed and decentralized publishing architecture is proposed to simplify data sharing and querying, enhancing reliability and efficiency. Third, a benchmark for evaluating ranking functions for RDF data is created, leading to the development of new ranking functions such as DBtrends and MIXED-RANK. Fourth, a scoring function based on Term Networks is proposed for interpreting factual queries, outperforming traditional information retrieval methods. Lastly, user interface patterns are discussed, and an extension for semantic search is proposed to improve information access in the face of the vast amounts of data available on the LOD cloud. These contributions collectively address key challenges in accessing and utilizing RDF data, offering insights and solutions to facilitate efficient information retrieval and exploration in the Semantic Web era.

Background: With the burgeoning volumes of data, efficient data transformation techniques are crucial. RDF mapping language has been recognized as a conventional method, whileIKEA the Knowledge graph’s approach brings a new perspective with tailored functions and schema definitions. Objectives: This study aims to compare the efficiency and effectiveness of the RDF mapping language (RML) and IKEA Knowledge graph(IKG) approaches in transforming JSON data into RDF format. It explores their performance across different complexity levels to provide insights into their strengths and limitations. Methods: We began our research by studying how professionals in the industry currently transform JSON data into Resource description framework(RDF) formats through a literature review. After gaining this understanding, we conducted practical experiments to compare the RDF mapping language (RML) and IKEA Knowledge graph(IKG)approaches at various complexity levels. We assessed user-friendliness, adaptability, execution time, and overall performance. This combined approach aimed to connect theoretical knowledge with experimental data transformation practices. Results: The results demonstrate the superiority of the IKEA Knowledge graph approach(IKG), particularly in intricate scenarios involving conditional mapping and external graph data lookup. It showcases the IKEA Knowledge Graph (IKG) method’s versatility and efficiency in managing diverse data transformation tasks. Conclusions: Through practical experimentation and thorough analysis, this study concludes that the IKEA Knowledge graph approach demonstrates superior performance in handling complex data transformations compared to the RDF mapping language (RML) approach. This research provides valuable insights for choosing an optimal data trans-formation approach based on the specific task complexities and requirements

This thesis explores the debate surrounding BIBFRAME (Bibliographic Framework) in a listserv provided by the Library of Congress (2011-2022). A combined qualitative and quantitative content analysis revealed tensions in the forum between those who prefer MARC (Machine-Readable Cataloging) and those who prefer BIBFRAME. From a theoretical perspective, these tensions are explained as originating in a conflict between two incommensurable bibliographic paradigms. It is argued that standards like MARC, AACR (Anglo-American Cataloging Rules) and ISBD (International Standard Bibliographic Description) belong to a ”record-based” paradigm whereas BIBFRAME, FRBR (Functional Requirements for Bibliographic Records) and RDA (Resource Description and Access) belong to an ”E-R” paradigm, based around the concepts of entities and relations. The results revealed that the debate is made up of two distinct dimensions: one theoretical technical and one social. The theoretical-technical tensions are, in turn, shown to revolve around two questions: whether MARC is obsolete and whether BIBFRAME is the right way forward. The social tensions stem from a divide between the library community and the IT people. The results provide evidence on the need for clearer communication on BIBFRAME, regarding its goals and design choices. Likewise, there is need for a closer integration between the library community and the IT world.

No description available.

No description available.

No description available.

No description available.