Author: Mason King

Authors: Xuanqing Yu、Wangtao Sun、Jingwei Li、Kang Liu、Chengbao Liu、Jie Tan Paper: https://arxiv.org/abs/2408.07840 Introduction Event prediction is a critical area of research due to its potential to minimize losses associated with future events. Temporal Knowledge Graph Forecasting (TKGF) is a technique used to model real-world event data, representing complex interactions between entities over time. Traditional approaches to TKGF involve converting event data into Temporal Knowledge Graphs (TKGs) and using Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) to capture evolving entity relationships. However, these methods are resource-intensive and often fail to capture long-term trends and causal relationships effectively. To address these limitations,…

Authors: Alessandro Berti、Mayssa Maatallah、Urszula Jessen、Michal Sroka、Sonia Ayachi Ghannouchi Paper: https://arxiv.org/abs/2408.07720 Introduction Process Mining (PM) is a data science discipline that extracts process-related insights from event data recorded by information systems. Techniques in PM include process discovery, conformance checking, and predictive analytics. Large Language Models (LLMs) have shown promise as PM assistants, capable of responding to inquiries and generating executable code. However, LLMs struggle with complex tasks requiring advanced reasoning. This paper proposes the AI-Based Agents Workflow (AgWf) paradigm to enhance PM on LLMs by decomposing complex tasks into simpler workflows and integrating deterministic tools with LLMs’ domain knowledge. Related Work…