We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of event logs, but can learn to use a knowledge graph to incorporate information about the attribute values of the events and their mutual relationships. The idea is realized by mapping event attributes to nodes of a knowledge graph and training a sequence model alongside a graph neural network in an end-to-end fashion. This hybrid approach substantially enhances the flexibility and applicability of predictive process monitoring, as both the static and dynamic information residing in the databases of organizations can be directly taken as input data. We demonstrate the potential of ProcK by applying it to a number of predictive process monitoring tasks, including tasks with knowledge graphs available as well as an existing process monitoring benchmark where no such graph is given. The experiments provide evidence that our methodology achieves state-of-the-art performance and improves predictive power when a knowledge graph is available.
University of Oregon Eugene OR USA；Pacific Northwest National Laboratory Richland WA USA；Adobe Research San Jose CA USA；Texas A\&M University College Station TX USA；Rensselaer Polytechnic Institute Troy NY USA；University of Oregon；Pacific Northwest National Laboratory；Adobe Research；Texas A\&M University；Rensselaer Polytechnic Institute
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus
Facebook AI Research（脸书人工智能研究院）；University College London（伦敦大学学院）；University of Mannheim（曼海姆大学）；ENS, PSL University（巴黎高等师范学院，巴黎文理研究大学）；Inria（法国国家信息与自动化研究所）；University of Washington（华盛顿大学）
KILT: a Benchmark for Knowledge Intensive Language Tasks
Facebook AI Research（脸书人工智能研究院）；University College London（伦敦大学学院）；LORIA（洛林计算机科学及其应用实验室）；University of Cambridge（剑桥大学）；HuggingFace（HuggingFace公司）；University of Amsterdam（阿姆斯特丹大学）
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Department of ECE University of Minnesota（电子工程系明尼苏达大学）；Data Science Institute University of Chicago（数据科学研究所芝加哥大学）；Carlson School of Management University of Minnesota（卡尔森管理学院明尼苏达大学）；Department of Surgery University of Minnesota（外科系明尼苏达大学）；Cisco Research（思科研究）；School of Statistics University of Minnesota（统计学学院明尼苏达大学）
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
Prince Al-Hussein Bin Abdullah II Faculty of Information Technology, Hashemite University（哈希姆大学侯赛因·本·阿卜杜拉二世亲王信息技术学院）；Princess Sumaya University for Technology（苏玛雅公主科技大学）；University of Miskolc（米什科尔茨大学）
