The MUHAI consortium studies how it is possible to build AI systems that rest on meaning
and understanding. We call this kind of AI meaningful AI in contrast to AI that rests …

Biomedical knowledge graphs encode domain knowledge as biomedical entities and
relationships between them. Graph traversal algorithms can make use of these rich sources …

Causal extraction from text plays a crucial role in various downstream analytical and
predictive tasks, such as constructing repositories of causal insights for reasoning. However …

Abstract Large Language Models have excelled at encoding and leveraging language
patterns in large text-based corpora for various tasks, including spatiotemporal event-based …

摘要当代系统认知, 管理与控制的核心理论, 方法与技术已经转移到大数据和人工智能技术上,
这导致当前人工智能技术条件局限与复杂系统认知, 管理, 控制的需求之间形成了一道鸿沟 …

Text documents are rich repositories of causal knowledge. While journal publications
typically contain analytical explanations of observations on the basis of scientific …

The MUHAI consortium studies how it is possible to build AI systems that rest on meaning
and understanding. We call this kind of AI meaningful AI in contrast to AI that rests …

This paper explores how research conducted into pragmatics and cognitionin the past fifty
years can deepen our knowledge on natural languages, narra-tives, and ultimately …

Data labeling is a well-studied issue within artificial intelligence. One overlooked area in this
space is labeling time series data. Labeling time series data can help quickly explain trends …