Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten
years. Building on a storied tradition of graphs in the AI community, a KG may be simply …

Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …

As graph databases become widespread, the International Organization for Standardization
(ISO) and International Electrotechnical Commission (IEC) have approved a project to create …

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud
services, and serverless functions have immediately become a new middleware for building …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Data is a precious resource in today's society, and it is generated at an unprecedented and
constantly growing pace. The need to store, analyze, and make data promptly available to a …

Property graphs have reached a high level of maturity, witnessed by multiple robust graph
database systems as well as the ongoing ISO standardization effort aiming at creating a new …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Abstract Machine learning methods can detect complex relationships between variables, but
usually do not exploit domain knowledge. This is a limitation because in many scientific …

We present L#, a new and simple approach to active automata learning. Instead of focusing
on equivalence of observations, like the L∗ algorithm and its descendants, L# takes a …