Knowledge Graphs at Scale. EU H2020, ITN, 2019-2023
The objective of KnowGraphs is to scale KGs to be accessible to a wide audience of users across multiple domains including companies (in domains including Industry 4.0, biomedicine, finance, law) of all sizes and even end-users (e.g., through personal assistants and web search). Addressing this goal demands a mix of works from the theoretical foundations to the exploitation and economic repercussions of knowledge graphs. The project focuses on addressing four of the facets of knowledge graph management: representation, construction, and maintenance, operation, and exploitation. The KnowGraphs team will address these pillars by researching and developing novel methods, models, and frameworks using a cross-disciplinary mix of methods from Web Science, Data Science, Knowledge Representation, Knowledge Engineering, Big Data, data law, and business innovation. The consortium will apply its results across sectors, i.e., in domains including personalized medicine, Industry 4.0, question answering, personal assistants, and Web search.
The Network was launched in May 2020 and is a partnership of public university hospitals and research institutions. The objectives of the Network are (a) the promotion of Precision Medicine in neurodegenerative diseases, (b) the development of models and biomarkers for those diseases that will support the use of Precision Medicine approaches in addressing those diseases. The ICT objectives of the Network are the creation of (a) a registry for neurodegenerative diseases that stores clinical patient data, (b) a Genomic Data Repository, (c) a database of images (MRIs, Ultrasounds, etc.), and (d) of an Epidemiological Map that will lead to the identification of populations with pathogenic variants.
The Network was launched in July 2019 and is a partnership of public university hospitals and research institutions. The objectives of the Network are (a) the promotion of Precision Medicine in cardiology, (b) the prevention of Juvenile Sudden Death, (c) the study of hereditary cardiovascular diseases in Greece, (d) the treatment of hereditary cardiovascular diseases on the basis of genetic tests in patients and their families. The ICT objectives of the Network are the creation of (a) a registry for the Inherited Diseases of the Heart (Cardiomyopathies, Aortopathies, and Channelopathies) that stores clinical patient data, (b) a Genomic Data Repository, and (c) an Epidemiological Map that will lead to the identification of populations with pathogenic variants. The ultimate goal of the network is to use the data stored in the systems to find populations who carry a specific genetic mutation and break the chain of the inherited disease.
Hellenic Precision Medicine Network on Cancer, National Project, 2018–2021
The mission of the network is to establish a connection with the National Health System, provide high-quality healthcare to Greek citizens, enrich diagnosis knowledge and prediction outcome and improve the targeted therapeutic treatment of cancer patients. The oncologists of the public and private health sector in Greece have access to the Network’s infrastructures in addition to the members of research labs who are responsible for performing the analysis of the patients’ biological samples.
HOBBIT – Holistic Benchmarking for Big Linked Data, EU H2020, 2015–2018
HOBBIT was an ambitious project that aims to push the development of Big Linked Data (BLD) processing solutions by providing a family of industry-relevant benchmarks for the BLD value chain through a generic evaluation platform. The aim of the project was to make open deterministic benchmarks available to test the performance of existing systems and push the development of innovative industry-relevant solutions. The underlying data mimics real industrial data assembled during the course of the project. In HOBBIT we worked with roughly 1PB of real industry-relevant data from 4 different domains. The produced benchmarks were based on data that reflected reality and measure industry-relevant Key Performance Indicators (KPIs) with comparable results using standardized hardware. The results of HOBBIT include (a) the creation of the HOBBIT Platform and (b) four different families of benchmarks for the Linked Data Lifecycle: Generation & Acquisition, Analysis & Processing, Storage & Curation, and Visualization & Services.
Linked Data Benchmark Council, EU FP7, 2012 - 2015
The goal of the Linked Data Benchmark Council (LDBC) FP7 project was to create the first comprehensive suite of open, fair, and vendor-neutral benchmarks for RDF/graph databases together with the LDBC foundation which defined processes for obtaining, auditing, and publishing results. The core scientific innovation of LDBC was therefore to define meaningful benchmarks derived from a combination of actual usage scenarios combined with the technical insight of top database systems researchers and architects in the choke points of current technology. LDBC brought together a broad community of researchers and RDF and graph database vendors and established an independent authority, the LDBC foundation, responsible for specifying benchmarks, benchmarking procedures, and verifying/publishing results. The forum created is an industry-supported association similar to the TPC where members include but are not limited to AWS, Intel, Neo4j, Ontotext, Sparsity, and TigerGraphDB.