A5035030812- Semantic Web and Ontologies
- Biomedical Text Mining and Ontologies
- Advanced Graph Neural Networks
- Data Quality and Management
- Bioinformatics and Genomic Networks
- Topic Modeling
- Computational Drug Discovery Methods
- Scientific Computing and Data Management
- Radiomics and Machine Learning in Medical Imaging
- Web Data Mining and Analysis
- Natural Language Processing Techniques
- Data Mining Algorithms and Applications
- Ferroptosis and cancer prognosis
- Cancer Genomics and Diagnostics
Technische Informationsbibliothek (TIB)
2022-2024
L3S Research Center
2022-2024
Leibniz University Hannover
2022-2024
The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports collaborative effort performed by ten teams of students, each guided senior researcher as their mentor, attending ISWS 2023. Each team provided different perspective topic creative AI, substantiated set research questions main subject investigation. 2023 edition focuses on intersection technologies and Creative AI. explored various...
Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over wide variety sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, services visited most by patient before new diagnosis, as well type requested tests, uncover patterns that contribute to earlier disease detection effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, health data...
Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) analyze differences between patients with LC based on family history cancer (FHC).
In many processes, ranging from medical treatments to supply chains and employee management, there is a growing need gather information with the objective of enhancing efficiency process in question. Often, gathered different stages resides disparate storage systems, necessitating an fusion process. Post-fusion, it common encounter data inconsistencies that hinder accurate analysis. Unfortunately, existing validation languages lack capability model constraints across stages, making...
Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources knowledge. In such a context, reducing size Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in distributed setting. This paper presents two algorithms RDF summarization: Grouping Based Summarization (GBS) and Query (QBS). The...
Community-based knowledge graphs are generated following hybrid approaches, where human intelligence empowers computational methods to effectively integrate encyclopedic or provide a common understanding of domain. Existing community-based represent essential sources for enhancing the accuracy data mining, information retrieval, question answering, and multimodal processing. However, despite enormous effort conducted by contributing communities, may be incomplete duplicated metadata. We...
In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources knowledge discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, focus on constructing Knowledge4COVID-19 graph (KG) from declarative definition mapping rules using RDF Mapping Language. Since valuable information about treatments, interactions, side is in textual descriptions scientific...
Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources knowledge. In such a context, reducing size Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in distributed setting. This paper presents two algorithms RDF summarization: Grouping Based Summarization (GBS) and Query (QBS). The...