Predicting Relations Between RDF Entities by Deep Neural Network

Murata, Tsuyoshi; Onuki, Yohei; Nukui, Shun; Inagi, Seiya; Qiu, Xule; Watanabe, Minoru; Okamoto, Hiroshi

doi:10.1007/978-3-319-70407-4_43

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…En nuestro caso, la red neuronal se generó empleando las herramientas de código abierto Keras [18] y Google TensorFlow [19], que facilitan la construcción y entrenamiento de los modelos. Estas herramientas crean capas de abstracción e interfaces de diseño y evaluación rápida de las arquitecturas Deep Learning [20].…”

Section: Desarrollo De La Arquitectura De La Red Neuronalunclassified

Estimación de producción en cítricos usando técnicas de aprendizaje automático

Apolo¹,

Guanter²,

Egea³

et al. 2019

Congreso Ibérico De Agroingeniería

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Section: Desarrollo De La Arquitectura De La Red Neuronalunclassified

Estimación de producción en cítricos usando técnicas de aprendizaje automático

Apolo¹,

Guanter²,

Egea³

et al. 2019

Congreso Ibérico De Agroingeniería

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“…Specifically, SemDeep has seen contributions on the explicit modeling of lexical and semantic relations stemming from joint neural-symbolic methods [23,44,55]. Additionally, well-defined NLP tasks have also been the focus of several SemDeep papers over the years, covering event detection [11], part-of-speech tagging [67], co-reference resolution [63], sentiment analysis [47], named entity recognition [41] or question answering [32].…”

Section: Recent Semantic Deep Learning Approachesmentioning

confidence: 99%

Special issue on Semantic Deep Learning

Gromann

Espinosa-Anke

Declerck

2019

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Numerous success use cases involving deep learning have recently started to be propagated to the Semantic Web. Approaches range from utilizing structured knowledge in the training process of neural networks to enriching such architectures with ontological reasoning mechanisms. Bridging the neural-symbolic gap by joining deep learning and Semantic Web not only holds the potential of improving performance but also of opening up new avenues of research. This editorial introduces the Semantic Web Journal special issue on Semantic Deep Learning, which brings together Semantic Web and deep learning research. After a general introduction to the topic and a brief overview of recent contributions, we continue to introduce the submissions published in this special issue.

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Understanding Ecological Systems Using Knowledge Graphs: An Application to Highly Pathogenic Avian Influenza

Robertson,

Han,

Castellanos

et al. 2024

Preprint

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Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of important interactions underpinning system structure, resilience, and dynamics. Knowledge graphs have been successfully applied to organize heterogeneous data systematically and to predict new linkages representing unobserved relationships in complex systems. Though not previously applied broadly in ecology, knowledge graphs have much to offer in an era of global change when system dynamics are responding to rapid changes across multiple scales simultaneously. We developed a knowledge graph to demonstrate the method’s utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad animal host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include a wide range of data related to HPAI including pathogen-host associations, animal species distributions, and human population demographics, using a semantic ontology that defines relationships within the data and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying new relationships and features of HPAI ecology, underscoring the generalizable value of knowledge graphs to ecology including their utility in revealing previously known relationships between entities and generating testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

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Predicting Relations Between RDF Entities by Deep Neural Network

Cited by 3 publications

References 8 publications

Estimación de producción en cítricos usando técnicas de aprendizaje automático

Estimación de producción en cítricos usando técnicas de aprendizaje automático

Special issue on Semantic Deep Learning

Understanding Ecological Systems Using Knowledge Graphs: An Application to Highly Pathogenic Avian Influenza

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