0000000001108410

AUTHOR

Martina Garofalo

showing 3 related works from this author

A First Experiment on Including Text Literals in KGloVe

2018

Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties. Our initial experiment shows that this approach might be useful, but does not clearly outperform earlier approaches when evaluated on machine learning tasks.

FOS: Computer and information sciencesgraph embeddingsComputer Science - Computation and LanguageArtificial Intelligence (cs.AI)koneoppiminenknowledge graphComputer Science - Artificial IntelligencetekstinlouhintaattributestiedonlouhintaComputation and Language (cs.CL)
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Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases

2018

Industry is evolving towards Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variable…

FOS: Computer and information sciencesArtificial Intelligence (cs.AI)Computer Science - Artificial Intelligence
researchProduct

Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases

2018

Industry is evolving towards Industry 4.0, which holds the promise of increased exibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables,…

graph embeddingknowledge graphindustry 4.0
researchProduct