Graph Structures for Knowledge Representation and Reasoning

Suggested summer read
  Graph Structures for Knowledge Representation and Reasoning

From https://link.springer.com/content/pdf/10.1007%2F978-3-030-72308-8.pdf

 The development of effective techniques for knowledge representation and
reasoning (KRR) is a crucial aspect of successful intelligent systems.
Different representation paradigms, as well as their use in dedicated
reasoning systems, have been extensively studied in the past. Nevertheless,
new challenges, problems, and issues have emerged in the context of
knowledge representation in Artificial Intelligence (AI), involving the
logical manipulation of increasingly large information sets (see for
example Semantic Web, BioInformatics, and so on). Improvements in storage
capacity and performance of computing infrastructure have also affected the
nature of KRR systems, shifting their focus towards representational power
and execution performance. Therefore, KRR research is faced with the
challenge of developing knowledge representation structures optimized for
large-scale reasoning. This new generation of KRR systems includes
graph-based knowledge representation formalisms such as Constraint Networks
(CNs), Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs
(CGs), Formal Concept Analysis (FCA), CP-nets, GAI-nets, and Argumentation
Frameworks, all of which have been successfully used in a number of
applications. The goal of the workshop series on Graph Structures for
Knowledge Representation and Reasoning (GKR) is to bring together
researchers involved in the development and application of graph-based
knowledge representation formalisms and reasoning techniques.