most citedExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023

Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry

Yuanwei Qu, Baifan Zhou, Arild Waaler +1

The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired produc…

cs.DC2023

Addressing the Scalability Bottleneck of Semantic Technologies at Bosch

Diego Rincon-Yanez, Mohamed H. Gad-Elrab, Daria Stepanova +4

At the heart of smart manufacturing is real-time semi-automatic decision-making. Such decisions are vital for optimizing production lines, e.g., reducing resource consumption, impr…

cs.AI2023

Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case

Zhipeng Tan, Baifan Zhou, Zhuoxun Zheng +5

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathem…

cs.AI2023

Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Baifan Zhou, Nikolay Nikolov, Zhuoxun Zheng +5

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that contex…

cs.LG20231 cited

ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

Antonis Klironomos, Baifan Zhou, Zhipeng Tan +4

Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML…