Zobrazeno 1 - 10
of 435
pro vyhledávání: '"GROTH, PAUL"'
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 3:1-3:19 (2023)
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence
Externí odkaz:
https://doaj.org/article/f9138e5c7b12442caea2f8d109d8840e
Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many uses of knowledge graphs such as web search and recommendation. Specifi
Externí odkaz:
http://arxiv.org/abs/2410.09128
Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in entity linking models, characterized as a decline in model performance
Externí odkaz:
http://arxiv.org/abs/2410.09127
Entity matching (EM) is the problem of determining whether two records refer to same real-world entity, which is crucial in data integration, e.g., for product catalogs or address databases. A major drawback of many EM approaches is their dependence
Externí odkaz:
http://arxiv.org/abs/2409.04073
Autor:
Daza, Daniel, Chu, Cuong Xuan, Tran, Trung-Kien, Stepanova, Daria, Cochez, Michael, Groth, Paul
Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks or knowledge graphs. While this task has been intensively approached from heuristics to graph embeddings and
Externí odkaz:
http://arxiv.org/abs/2407.07639
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to their findings. However, this manual process is tedious and error-prone.
Externí odkaz:
http://arxiv.org/abs/2404.19591
Autor:
Allen, Bradley P., Groth, Paul T.
A backbone of knowledge graphs are their class membership relations, which assign entities to a given class. As part of the knowledge engineering process, we propose a new method for evaluating the quality of these relations by processing description
Externí odkaz:
http://arxiv.org/abs/2404.17000
We describe the University of Amsterdam Intelligent Data Engineering Lab team's entry for the SemEval-2024 Task 6 competition. The SHROOM-INDElab system builds on previous work on using prompt programming and in-context learning with large language m
Externí odkaz:
http://arxiv.org/abs/2404.03732
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smar
Externí odkaz:
http://arxiv.org/abs/2403.18133
Autor:
Borgman, Christine L., Groth, Paul T.
Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stake
Externí odkaz:
http://arxiv.org/abs/2402.07926