Promocijas darbs

Elektroniskā versija nesatur pielikumusDarbā izstrādātas oriģinālas metodes, kas ļauj vizuālus uz paplašinātām UML veida grafu diagrammām balstītus rīkus izmantot praktisku ontoloģiju un semantisko datu vaicājumu veidošanai un attēlošanai. OWL ontoloģiju vizuālas modelēšanas jomā izveidoti līdzekļi konkrētam lietojumam specifiskas notācijas uzdošanai un izmantošanai, tādi ka: mehānisms lietotāja definētu notāciju uzdošanai, ontoloģiju vizualizācijas parametru ietvars, ontoloģiju eksporta modulis un uz gramatikām balstīta priekšāteikšanas metode. Darbā piedāvāts risinājums vizuālai bagātīgu datu vaicājumu veidošanai pār RDF datubāzēm, un to translēšanai uz tekstuālu SPARQL valodu, kurā pierakstītie vaicājumi var tikt tieši izpildīti pār RDF datu bāzēm. Atslēgvārdi: OWL, OWLGrEd, teksta priekšāteicējs, domēnspecifiska ontoloģiju attēlošana, SPARQL, vizuāli vaicājumi, ViziQuerThe doctoral thesis develops original methods that allow visual tools that are based on extended UML-style graph diagrams to be used for creating and visualising practical ontologies and semantic data queries. In the field of visual modeling of OWL ontologies, tools have been developed for creating modeling notations specific to particular applications, such as a mechanism for creating user-defined notations, a framework for ontology visualisation parameters, an ontology export module and a grammar-based auto-completion method. The doctoral thesis presents a solution for the visual formulation of rich data queries over RDF databases, and their translation into the standard textual SPARQL query language. Keywords: OWL, OWLGrEd, text auto-completion, Domain-Specific Ontology Representation, SPARQL, Visual Queries, ViziQuer

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Qualität in der Inhaltserschließung

This edited volume deals with issues relating to the quality of subject cataloging in the digital age, where heterogenous articles from different processes meet, and attempts to define important quality standards. Topics range from metadata and the cataloging policies of the German National Library, the GND, and the head offices of the German library association, to the presentation of a range of different projects, such as QURATOR and SoNAR