Graphs and knowledge bases have been around for many decades, and research outcomes have tremendously impacted areas like mathematics, artificial intelligence, and databases. However, despite being already coined by the scientific community, technological developments and astronomical data growth make knowledge graph management a fundamental topic nowadays in various computer science areas, supporting novel applications at the science (e.g., biomedicine) and industry (e.g., Google’s Knowledge Graph) level.
Scientific and industrial communities reacted to the emergent area of knowledge graph (KG) management. As a result, formal frameworks for KG definition and representation, and methods for the creation, exploration, and analysis have flourished to make KGs a reality. On the one hand, albeit expressive and capable of providing a domain shared understanding, current KGs are relatively simple semantic structures that mainly represent an assembly of factual statements arranged in entity descriptions, possibly enriched by class hierarchies and corresponding property definitions. On the other hand, despite the noticeable results, sustainability is still affected by the absence of transparent and traceable frameworks for intelligent KG governance. Therefore, the application of KGs in the real world demands 1) programming paradigms for KG management, 2) transparent data integration and quality assessment techniques, 3) scalable and sustainable approaches for knowledge creation, exploration, and analysis, and 4) access control and privacy preservation.
This Dagstuhl Seminar focuses on these relevant research topics and aspires to reflect on KGs from their more foundational computer science perspectives. The main aim of the seminar is to bring together interdisciplinary researchers from both academia and industry eager to discuss foundations, concepts, and implementations that will pave the way for the next generation of KGs ready to be used in the real world. The unique combination of these research topics should lead to breakthrough ideas to be further investigated. Specifically, the seminar aims to address the following research questions:
Q1) What are the key requirements for programming languages paradigms for modeling, representing, storing, and managing KGs in the real world?
Q2) How is sustainability achieved in the context of KG management, and what are its main benefits in terms of data integration, curation, and exploration toward traceable and sustainable pipelines?
Q3) What are the key requirements in terms of data management and query processing to ensure scalability over big KGs?
Q4) What are the trade-offs between fine-grained knowledge representation (e.g., personalized KGs) and the enforcement of data privacy and access control regulations?
The ambition of the organizers is to make this seminar an influential event in the field. In particular, the aim is to use the insights and the results of the seminar to design a roadmap that will shape the future of intelligent frameworks to make KGs applicable in the real world. The dissemination plan includes 1) a report summarizing the conclusions of the seminar, e.g., as a report in the ACM SIGMOD Record and 2) the publication of a position paper describing the framework roadmap in a top-ranked journal.
- Artificial Intelligence
- Programming Languages
- Semantic Data Integration
- Federated Query Processing
- Programming Paradigms for Knowledge Graphs
- Intelligent Knowledge Graph Management
- Access Control and Privacy