Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Zhou, Baifan"'
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 production events, can cause severe environmental and economic damage. Previ
Externí odkaz:
http://arxiv.org/abs/2310.08737
Autor:
Rincon-Yanez, Diego, Gad-Elrab, Mohamed H., Stepanova, Daria, Tran, Kien Trung, Xuan, Cuong Chu, Zhou, Baifan, Karlamov, Evgeny
Publikováno v:
Industry Track - Extended Semantic Web Conference (ESWC2023)
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, improving the quality of discrete manufacturing operations, and optimizing
Externí odkaz:
http://arxiv.org/abs/2309.10550
Autor:
Tan, Zhipeng, Zhou, Baifan, Zheng, Zhuoxun, Savkovic, Ognjen, Huang, Ziqi, Gonzalez, Irlan-Grangel, Soylu, Ahmet, Kharlamov, Evgeny
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 mathematical mappings via machine learning (ML). However, there has been lim
Externí odkaz:
http://arxiv.org/abs/2308.01105
Autor:
Zhou, Baifan, Nikolov, Nikolay, Zheng, Zhuoxun, Luo, Xianghui, Savkovic, Ognjen, Roman, Dumitru, Soylu, Ahmet, Kharlamov, Evgeny
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 context, distributed computing solutions such as cloud systems are leveraged
Externí odkaz:
http://arxiv.org/abs/2308.01094
Autor:
Klironomos, Antonis, Zhou, Baifan, Tan, Zhipeng, Zheng, Zhuoxun, Mohamed, Gad-Elrab, Paulheim, Heiko, Kharlamov, Evgeny
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 libraries. In this demo paper, we present ExeKGLib, a Python library
Externí odkaz:
http://arxiv.org/abs/2305.02966
Autor:
Zheng, Zhuoxun, Zhou, Baifan, Zhou, Dongzhuoran, Cheng, Gong, Jiménez-Ruiz, Ernesto, Soylu, Ahmet, Kharlamo, Evgeny
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prom
Externí odkaz:
http://arxiv.org/abs/2209.11089
Autor:
Zhou, Dongzhuoran, Zhou, Baifan, Chen, Jieying, Cheng, Gong, Kostylev, Egor V., Kharlamov, Evgeny
Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, nam
Externí odkaz:
http://arxiv.org/abs/2209.11067
Publikováno v:
In Procedia Computer Science 2024 232:1299-1308
Akademický článek
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