A5051964032- Semantic Web and Ontologies
- Advanced Database Systems and Queries
- Data Quality and Management
- Service-Oriented Architecture and Web Services
- Scientific Computing and Data Management
- Data Management and Algorithms
- Biomedical Text Mining and Ontologies
- Data Visualization and Analytics
- Natural Language Processing Techniques
- Geographic Information Systems Studies
- Digital Humanities and Scholarship
- Anomaly Detection Techniques and Applications
- Advanced Graph Neural Networks
- Data Mining Algorithms and Applications
- Web Data Mining and Analysis
- Digital Rights Management and Security
- Context-Aware Activity Recognition Systems
- Bioinformatics and Genomic Networks
- Software System Performance and Reliability
- IoT and Edge/Fog Computing
- Fault Detection and Control Systems
- Healthcare Technology and Patient Monitoring
- Open Education and E-Learning
- Graph Theory and Algorithms
- Research Data Management Practices
Ghent University
2015-2022
Ghent University Hospital
2015-2020
Eurogentec (Belgium)
2019
iMinds
2015-2016
Anomalies and faults can be detected, their causes verified, using both data-driven knowledge-driven techniques. Data-driven techniques adapt internal functioning based on the raw input data but fail to explain manifestation of any detection. Knowledge-driven inherently deliver cause that were detected require too much human effort set up. In this paper, we introduce FLAGS, Fused-AI interpretabLe Anomaly Generation System, combine in one methodology overcome limitations optimize them limited...
The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the graph and application’s supported entailments graph. During validation, are assessed against sets constraints, found violations help refining graphs. However, existing validation approaches cannot always explain root causes (inhibiting refinement), fully match during with those by application. These accurately validate graphs, or combine multiple systems,...
To unlock the value of increasingly available data in high volumes, we need flexible ways to integrate across different sources. While semantic integration can be provided through RDF generation, current generators insufficiently scale terms volume. Generators are limited by memory constraints. Therefore, developed RMLStreamer, a generator that parallelizes ingestion and mapping tasks generation multiple instances. In this paper, analyze what aspects parallelizable introduce an approach for...
Knowledge graphs, which contain annotated descriptions of entities and their interrelations, are often generated using rules that apply semantic annotations to certain data sources. (Re)using ontology terms without adhering the axioms defined by ontologies results in inconsistencies se affecting quality. Methods tools were proposed detect resolve inconsistencies, root causes include ontologies. However, these either require access complete knowledge graph, is not always available a...
Data is scattered across service providers, heterogeneously structured in various formats. By lack of interoperability, data portability hindered, and thus user control inhibited. An interoperable solution for transferring personal needed. We demo PROV4ITDaTa: a Web application, that allows users to transfer into an format their store. PROV4ITDaTa leverages the open-source solutions RML.io, Comunica, Solid: (i) RML.io toolset describe how access from providers generate datasets; (ii)...
In healthcare, the aging of population is resulting in a gradual shift from residential care to home care, requiring reliable follow-up elderly people by whole network providers. The environment these patients increasingly being equipped with different monitoring devices, which allow obtain insight into current condition patient & environment. However, platforms that support providers are centralized and not personalized, reducing performance, scalability, autonomy privacy. Because available...