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pro vyhledávání: '"Reutter, Juan"'
Autor:
Díaz, Camila, Dunstan, Jocelyn, Etcheverry, Lorena, Fonck, Antonia, Grez, Alejandro, Mery, Domingo, Reutter, Juan, Rojas, Hugo
We present our results regarding the automatic construction of a knowledge graph from historical documents related to the Chilean dictatorship period (1973-1990). Our approach consists on using LLMs to automatically recognize entities and relations b
Externí odkaz:
http://arxiv.org/abs/2408.11975
Autor:
Cucumides, Tamara, Daza, Daniel, Barceló, Pablo, Cochez, Michael, Geerts, Floris, Reutter, Juan L, Romero, Miguel
The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as a promising approach, combining good performance with high interpretab
Externí odkaz:
http://arxiv.org/abs/2310.04598
Autor:
Geerts, Floris, Reutter, Juan L.
Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNN architectures, and tools for
Externí odkaz:
http://arxiv.org/abs/2204.04661
Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between $k$-tuples of vertices. The distinguishing power of these "higher-order'' GNNs is known to be bounded by the $k$-dimensional Weis
Externí odkaz:
http://arxiv.org/abs/2106.06707
Publikováno v:
Communications of the ACM; Aug2024, Vol. 67 Issue 8, p40-44, 5p
Work on knowledge graphs and graph-based data management often focus either on declarative graph query languages or on frameworks for graph analytics, where there has been little work in trying to combine both approaches. However, many real-world tas
Externí odkaz:
http://arxiv.org/abs/2004.01816
Worst-case optimal join algorithms have gained a lot of attention in the database literature. We now count with several algorithms that are optimal in the worst case, and many of them have been implemented and validated in practice. However, the impl
Externí odkaz:
http://arxiv.org/abs/1908.01812
Akademický článek
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Assessing and improving the quality of data in data-intensive systems are fundamental challenges that have given rise to numerous applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data migrat
Externí odkaz:
http://arxiv.org/abs/1712.03438
Publikováno v:
In Information Systems March 2022 105