Knowledge graphs are powerful models to represent networks of real-world entities, such as objects, events, situations, concepts, by illustrating the relationships between them. Information encoded by knowledge graphs is usually stored in graph databases, and visualized as graph structures. Although these models have been introduced in the Semantic Web context, they have recently found successful applications also in other contexts, e.g., the analysis of financial, social, geospatial and biomedical data.
Knowledge graphs often integrate datasets from various sources, which frequently differ in their structure. This, together with the increasing volumes of structured and unstructured data stored in a distributed manner, bring to light new problems related to data/knowledge representation and integration, data querying, business analysis and knowledge discovery.
The ultimate goal of this workshop is to provide to participants the opportunity of introducing and discussing new methods, theoretical approaches, algorithms, and software tools that are relevant to the Knowledge Graphs based research, especially when it is focused on a large scale. To this regard, interesting open issues include how Knowledge Graphs may be used to represent knowledge, how systems managing Knowledge Graphs work, and which applications may be provided on top of a Knowledge Graph, in the distributed.
The list of topics for the workshop, that has not to be intended as exhaustive, is reported below.
Topic
- Knowledge Graphs applications in real world domains
- Knowledge Graphs databases in the distributed
- Explainable knowledge recommendation
- Link prediction
- Knowledge Graphs alignment and querying
- Knowledge extraction and integration of heterogeneous data
- Risk detection and prediction
- Knowledge Graphs Embeddings
- Large Language Models (LLMs) for Knowledge Graphs
Paper Publishing
The workshop papers will be published by Springer in Communications in Computer and Information Science (CCIS). The authors of selected workshop papers will be invited to submit an extended version of their contributions to a special issue of an international journal.
Diversity and Inclusion Statement
D&I INITIATIVE. The ADBIS community believes that diversity and culture of support encourage retention and attraction of talent, promote diversity of thought and perspective, and help make the scientific community more flexible and responsive in times of change. For these reasons, ADBIS 2024 continues to participate in the Diversity and Inclusion (D&I) initiative of the Database community aiming to guide researchers in our community to adopt a more inclusive mindset in general toward different individuals regardless of their age, gender, gender identity, race, cultural background, religion, physical and mental ability, sexual orientation, parental and marital status, etc. If you have any comments, questions, suggestions, or complaints, please email the ADBIS 2024 Diversity and Inclusion chairs.