Diego Rincon-Yanez, Sabrina Senatore, Declan O'Sullivan
Book / chapter· Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 108--113
Numerous digitisation activities in manufacturing led to an enormous increase in available, accessible data. Knowledge graphs (KGs) become increasingly popular in this domain as they show strengths in integrating different data sources and serve as a basis for downstream tasks. Yet, constructing a KG is still a challenging and time-consuming process. Neuro-symbolic AI approaches, especially with powerful LLMs, have shown promising potential in research and industry and can support KG construction. Nevertheless, KG construction with neural methods must be aware of, or ideally even handle, the inexplicability of results when applying the KG to downstream manufacturing tasks, e.g., tasks of reliability- or safety-relevance. This makes it interesting to evaluate the utilisation of neuro-symbolic AI and LLMs in KG construction in manufacturing. To the best of our knowledge, there is no systematic literature review on neuro-symbolic AI and LLMs in KGs in manufacturing to date. Hence, this paper conducts a systematic literature review on neuro-symbolic AI and LLMs in KG construction in manufacturing. We show a solid increase of relevant publications on manufacturing KG construction and further show that BERT embeddings, RNN encodings, especially BiLSTM, CRF decodings, and, recently, LLMs, are common components of knowledge extraction from text documents to build KGs in manufacturing. With this systematic review, we support both further research and industry application in this field. The main question to guide this review is “Which role play neuro-symbolic AI, especially LLM approaches in knowledge graph construction for manufacturing?”.
BibTeX
@article{Schmidt2026,author = {Wilma Johanna Schmidt and Diego Rincon-Yanez and Evgeny Kharlamov and Adrian Paschke},doi = {10.1109/ACCESS.2026.3665999},issn = {2169-3536},journal = {IEEE Access},pages = {28383-28410},title = {LLMs on the Rise: Neuro-Symbolic AI for Knowledge Graph Construction in Manufacturing: Systematic Literature Review},volume = {14},url = {https://ieeexplore.ieee.org/document/11398067/},year = {2026}}
This article examines how benchmarking digital methods can advance historical research by recovering women's lives from fragmented and underused archives. It focuses on the Funeral Entries held in the Genealogical Office of the National Library of Ireland, a rich but understudied manuscript collection compiled by the Ulster King of Arms between the late sixteenth and early eighteenth centuries. These records, which document death dates, kinship networks, and social affiliations, contain an unusually high proportion of women for the early modern period (about 38 percent), offering rare insight into gendered experiences of death, family, and memory. The article emerges from the ERC-funded VOICES project, which develops AI-powered approaches to recover women's voices and experiences from early modern sources (VOICES Project, 2023; https://voicesproject.ie). As part of this work, we present a benchmarking experiment using the Funeral Entries to assess the ability of Handwritten Text Recognition (HTR) and Named Entity Recognition (NER) models to cope with early modern orthography, multilingual naming practices, and manuscript variability. These experiments not only illuminate the constraints of existing tools but also demonstrate how benchmarking can generate reusable workflows, inform the creation of annotated gold standards, and support the production of FAIR-aligned humanities data. Rather than offering a completed dataset, the article argues for the historiographical value of iterative benchmarking. We show how evaluating and refining computational methods reframes questions of archival visibility, evidentiary status, and the interpretive potential of genealogical records, positioning the Funeral Entries not merely as instruments of male lineage but as essential sources for recovering early modern women's social worlds.
BibTeX
@article{Mcshane2026,author = {Bronagh Ann Mcshane and Diego Rincon-Yanez and Felix Vanden Borre and Jane Ohlmeyer and Declan O'sullivan},doi = {10.5334/johd.491},issn = {2059481X},issue = {45},journal = {Journal of Open Humanities Data},month = {3},pages = {1-13},title = {Digitising Death: Benchmarking Genealogical Data and Recovering Women’s Histories in Early Modern Ireland},volume = {12},url = {http://openhumanitiesdata.metajnl.com/articles/10.5334/johd.491/},year = {2026}}
From Data Spaces to Agent Spaces
Axel Polleres, Diego Rincon-Yanez, Amin Anjomshoaa, Daniel Dobriy, Emanuel Sallinger
Conference· Fourth International Workshop on Semantics in Dataspaces (SDS 2026)
Abstract
No abstract is available in the current publication record.
BibTeX
@inproceedings{Polleres2026,author = {Axel Polleres and Diego Rincon-Yanez and Amin Anjomshoaa and Daniel Dobriy and Emanuel Sallinger},issn = {1613-0073},booktitle = {Fourth International Workshop on Semantics in Dataspaces (SDS 2026)},month = {4},publisher = {CEUR},title = {From Data Spaces to Agent Spaces},year = {2026}}
In the era of information overload, distinguishing between real and fake news is a critical challenge, particularly in public social networking domains. For this purpose, an approach based on the synergy accomplished by a Neurosymbolic AI system, powered with Fuzzy Logic techniques, is introduced to achieve understandable fact-checking classification results. The work proposes a fact-checking approach based on Knowledge Graph Embedding (KGE) techniques. It extracts the involved entities from textual data in the form of triples that, projected in a vector space, form graphs that effectively highlight contextual information. The classification results are interpreted by exploiting fuzzy set modelling, which aims to improve the presentation of the final results. Specifically, we use the Hits@N metric to design fuzzy variables whose linguistic terms reflect news distribution. Then, by exploiting fuzzy rule design, human-like classification performance evaluation is provided. Through experimental evaluation of the benchmark dataset, our approach shows its effectiveness in discriminating between real and fake news, enhanced by straightforward explanations driven by the fuzzy rule design.
BibTeX
@incollection{Karel-Batista2024,address = {Lisbon, Portugal},author = {Guti{\'{e}}rrez-Batista, Karel and Rincon-Yanez, Diego and Senatore, Sabrina},booktitle = {Lecture Notes in Networks and Systems},doi = {10.1007/978-3-031-73997-2_10},isbn = {9783031739965},issn = {23673389},pages = {106--118},title = {{Human-Oriented Fuzzy-Based Assessments of Knowledge Graph Embeddings for Fake News Detection}},url = {https://link.springer.com/10.1007/978-3-031-73997-2_10},volume = {1176 LNNS},year = {2025}}
Eliana Elizabeth Rivera Capacho, Claudia Patricia Diaz Bossa, Mar\'ia Del Carmen Campos, Diego Rincon-Yanez, Heriberto Rangel-Navia, Esther Mandelbaum Gon\ccalves Bianchini
Miguel Oswaldo P\'erez Pulido, Omar Camilo Mej\'ia Ardila, Francisco Javier Leon, Lesley Fabiola Bohorquez Chac\'on, Holman Bolivar, Diego Rincon-Yanez
Book / chapter· Data-Driven Insights and Analytics for Measurable Sustainable Development Goals, 207--228
No abstract is available in the current publication record.
BibTeX
@incollection{PerezPulido2025,author = {{P{\'{e}}rez Pulido}, Miguel Oswaldo and {Mej{\'{i}}a Ardila}, Omar Camilo and Leon, Francisco Javier and {Bohorquez Chac{\'{o}}n}, Lesley Fabiola and Bolivar, Holman and Rincon-Yanez, Diego},booktitle = {Data-Driven Insights and Analytics for Measurable Sustainable Development Goals},doi = {10.1016/B978-0-443-33044-5.00017-6},isbn = {9780443330452},pages = {207--228},publisher = {Elsevier},title = {{A knowledge database discovery approach for improving quality in higher education institutions}},url = {https://linkinghub.elsevier.com/retrieve/pii/B9780443330445000176},year = {2025}}
No abstract is available in the current publication record.
BibTeX
@incollection{Cardenas-Malpica2025,author = {Cardenas-Malpica, Paola and {Espinel Pab{\'{o}}n}, Diana Yurley and Perpinan-Arahuho, Marieth and Rangel-Navia, Heriberto and Rincon-Yanez, Diego},booktitle = {Data-Driven Insights and Analytics for Measurable Sustainable Development Goals},doi = {10.1016/b978-0-443-33044-5.00019-x},isbn = {9780443330452},pages = {247--262},publisher = {Elsevier},title = {{Leveraging shallow learning techniques to support the methodological design of public policies in low density areas in Colombia}},url = {https://linkinghub.elsevier.com/retrieve/pii/B978044333044500019X},year = {2025}}
No abstract is available in the current publication record.
BibTeX
@incollection{schmidt2025ontologyrag,author = {Schmidt, Wilma Johanna and Rincon-Yanez, Diego and Grangel-Gonz{\'{a}}lez, Irlan and Paschke, Adrian and Kharlamov, Evgeny},booktitle = {Volume 62: Linking Meaning: Semantic Technologies Shaping the Future of AI},doi = {10.3233/ssw250006},isbn = {9789819500192},issn = {16113349},month = {aug},pages = {366--376},title = {{Document-Level Relation Extraction with Ontology-Guided RAG}},url = {https://ebooks.iospress.nl/doi/10.3233/SSW250006},year = {2025}}
The growing threat of disinformation and misinformation across digital platforms has intensified the demand for systems capable of producing verifiable and trustworthy outputs. With the widespread adoption of Large Language Models (LLMs) for a variety of tasks, the requirement to provide accurate and fact-verifiable answers is increasing daily. GraphRAGs have become a powerful approach for solving complex tasks that require factual context to deliver accurate and explainable answers. However, Knowledge Bases (KB) used to provide factual and contextual knowledge are composed of thousands or millions of statements, which limits the size of inputs that an LLM can handle, typically by the number of input tokens supported by the model. This work addresses the problem of Fact-Checking by injecting Knowledge Graph Embedding (KGE) vector representations into LLMs using a Retrieval Augmented Generation (RAG) approach to obtain more accurate results. The results show a notable difference in the quality of the results with two different vector representations and two KB construction methods.
BibTeX
@inproceedings{Ubaque2025,author = {Orlando Abuanza Ubaque and Diego Rincon-Yanez and Declan O’Sullivan},issn = {16130073},booktitle = {CEUR Workshop Proceedings},title = {Injecting Knowledge Graph Embeddings into RAG Architectures: Scalable Fact-Checking for Combating Disinformation with LLMs},volume = {4064},url = {https://ceur-ws.org/Vol-4064/SKGi-paper4.pdf},year = {2025}}
This version explores the intersection of Knowledge Graphs (KGs) and Large Language Models (LLMs) with a focus on enabling scalable, efficient, and trustworthy AI applications in industrial contexts. As generative AI rapidly evolves, integrating symbolic and neural methods becomes essential to address challenges such as explainability, data alignment, and system robustness by gathering academic researchers and industry practitioners to discuss practical solutions and future of Semantic Web technologies in the era of foundation models.
BibTeX
@article{Rincon-Yanez2025,author = {Diego Rincon-Yanez and Wilma Johanna Schmidt and Evgeny Kharlamov and Michael Cochez and Adrian Paschke and Declan O’Sullivan},issn = {16130073},journal = {CEUR Workshop Proceedings},title = {Second International Workshop on Scaling Knowledge Graphs for Industry (SKGi) - LLMs meet KGs: Preface},volume = {4064},url = {https://ceur-ws.org/Vol-4064/SKGI-preface.pdf},year = {2025}}
In the realm of international trade and economic development, the prediction of trade flows between countries is crucial for identifying export opportunities. Commonly used log-linear regression models are constrained due to difficulties when dealing with extensive, high-cardinality datasets, and the utilization of machine learning techniques in predictions offers new possibilities. We examine the predictive power of Graph Neural Networks (GNNs) in estimating the value of bilateral trade between countries. We work with detailed UN Comtrade data that represent annual bilateral trade in goods between any two countries in the world and more than 5000 product groups. We explore two different types of GNNs, namely Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), by applying them to trade flow data. This study evaluates the effectiveness of GNNs relative to traditional machine learning techniques such as random forest and examines the possible effects of data drift on their performance. Our findings reveal the superior predictive capability of GNNs, suggesting their effectiveness in modeling complex trade relationships. The research presented in this work offers a data-driven foundation for decision-making and is relevant for business strategies and policymaking as it helps in identifying markets, products, and sectors with significant development potential.
BibTeX
@article{Sellami2024,author = {Sellami, Bassem and Ounoughi, Chahinez and Kalvet, Tarmo and Tiits, Marek and Rincon-Yanez, Diego},doi = {10.3390/bdcc8060065},issn = {25042289},journal = {Big Data and Cognitive Computing},month = {jun},number = {6},pages = {65},title = {{Harnessing Graph Neural Networks to Predict International Trade Flows}},url = {https://www.mdpi.com/2504-2289/8/6/65},volume = {8},year = {2024}}
Wilma Schmidt, Diego Rincon-Yanez, Evgeny Kharlamov, Adrian Paschke
Conference· Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems co-located with 20th International Conference on Semantic Systems (SEMANTiCS 2024), 3759
No abstract is available in the current publication record.
BibTeX
@inproceedings{Schmidt2024b,author = {Schmidt, Wilma and Rincon-Yanez, Diego and Kharlamov, Evgeny and Paschke, Adrian},booktitle = {Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems co-located with 20th International Conference on Semantic Systems (SEMANTiCS 2024)},publisher = {CEUR-WS},title = {{Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models}},volume = {3759},issn = {16130073},url = {https://ceur-ws.org/Vol-3759/workshop4.pdf},year = {2024}}
Diego Rincon-Yanez, Mohamed H. Gad-Elrab, Daria Stepanova, Kien Trung Tran, Cuong Chu Xuan, Baifan Zhou, Evgeny Karlamov
Book / chapter· Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13998 LNCS, 177--181
No abstract is available in the current publication record.
BibTeX
@incollection{Rincon-Yanez2023,author = {Rincon-Yanez, Diego and Gad-Elrab, Mohamed H. and Stepanova, Daria and Tran, Kien Trung and {Chu Xuan}, Cuong and Zhou, Baifan and Karlamov, Evgeny},booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},doi = {10.1007/978-3-031-43458-7_33},month = {sep},issn = {16113349},pages = {177--181},url = {https://link.springer.com/10.1007/978-3-031-43458-7_33},volume = {13998 LNCS},title = {{Addressing the Scalability Bottleneck of Semantic Technologies at Bosch}},year = {2023}}
Zhuoxun Zheng, Baifan Zhou, Zhipeng Tan, Ognjen Savkovic, Diego Rincon-Yanez, Nikolay Nikolov, Dumitru Roman, Ahmet Soylu, Evgeny Kharlamov
Conference· Proceedings of the ISWC 2023 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 22nd International Semantic Web Conference (ISWC 2023), 3632
No abstract is available in the current publication record.
BibTeX
@inproceedings{Zheng2023,author = {Zheng, Zhuoxun and Zhou, Baifan and Tan, Zhipeng and Savkovic, Ognjen and Rincon-Yanez, Diego and Nikolov, Nikolay and Roman, Dumitru and Soylu, Ahmet and Kharlamov, Evgeny},booktitle = {Proceedings of the ISWC 2023 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 22nd International Semantic Web Conference (ISWC 2023)},issn = {16130073},title = {{Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch}},url = {https://ceur-ws.org/Vol-3632/ISWC2023_paper_510.pdf https://hdl.handle.net/11250/3116366},volume = {3632},year = {2023}}
Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms.
BibTeX
@article{Rincon-Yanez2023a,author = {Rincon-Yanez, Diego and Ounoughi, Chahinez and Sellami, Bassem and Kalvet, Tarmo and Tiits, Marek and Senatore, Sabrina and Yahia, Sadok Ben},doi = {10.1016/j.jksuci.2023.101789},issn = {13191578},journal = {Journal of King Saud University - Computer and Information Sciences},number = {10},title = {{Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings}},volume = {35},year = {2023}}
No abstract is available in the current publication record.
BibTeX
@article{Rincon-Yanez2022,author = {Rincon-Yanez, Diego and {De Lauro}, Enza and Petrosino, Simona and Senatore, Sabrina and Falanga, Mariarosaria},doi = {10.3390/app12146835},issn = {2076-3417},journal = {Applied Sciences},title = {{Identifying the Fingerprint of a Volcano in the Background Seismic Noise from Machine Learning-Based Approach}},volume = {12},year = {2022}}
Conference· Proceedings of the 1st International Workshop on Knowledge Graph Generation From Text and the 1st International Workshop on Modular Knowledge co-located with 19th Extended Semantic Conference (ESWC 2022), 3184, 94--108
No abstract is available in the current publication record.
BibTeX
@inproceedings{Rincon-Yanez2022Text2KG,address = {Hersonissos, Greece},author = {Rincon-Yanez, Diego and Senatore, Sabrina},booktitle = {Proceedings of the 1st International Workshop on Knowledge Graph Generation From Text and the 1st International Workshop on Modular Knowledge co-located with 19th Extended Semantic Conference (ESWC 2022)},pages = {94--108},issn = {16130073},title = {{FAIR Knowledge Graph construction from text, an approach applied to fictional novels}},volume = {3184},year = {2022}}
No abstract is available in the current publication record.
BibTeX
@inproceedings{Rodriguez2021,author = {Rodriguez, Norvey and Barrero, Juan and Rincon-Yanez, Diego},booktitle = {2021 7th Congreso Internacional de Innovación y Tendencias en Ingeniería, CONIITI 2021 - Conference Proceedings},doi = {10.1109/CONIITI53815.2021.9619602},isbn = {9781665402217},publisher = {IEEE},title = {{An alternative construction for the district institutional development index (IDID) of Bogotá using DEA-CCR}},year = {2021}}
Enabling a Semantic Sensor Knowledge Approach for Quality Control Support in Cleanrooms
Diego Rincon-Yanez, Flavia Crispoldi, Dalila Onorati, Pierfrancesco Ulpiani, Giuseppe Fenza, Sabrina Senatore
Conference· ISWC, 2980
Abstract
No abstract is available in the current publication record.
BibTeX
@inproceedings{Rincon-Yanez2021,author = {Rincon-Yanez, Diego and Crispoldi, Flavia and Onorati, Dalila and Ulpiani, Pierfrancesco and Fenza, Giuseppe and Senatore, Sabrina},booktitle = {ISWC},issn = {16130073},title = {{Enabling a Semantic Sensor Knowledge Approach for Quality Control Support in Cleanrooms}},volume = {2980},year = {2021}}
No abstract is available in the current publication record.
BibTeX
@inproceedings{Rincon-Yanez2020,author = {Rincon-Yanez, Diego and Lauro, Enza De and Falanga, Mariarosaria and Senatore, Sabrina and Petrosino, Simona},booktitle = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)},doi = {10.1109/SSCI47803.2020.9308329},isbn = {978-1-7281-2547-3},month = {dec},pages = {189--196},publisher = {IEEE},title = {{Towards a semantic model for IoT-based seismic event detection and classification}},year = {2020}}
No abstract is available in the current publication record.
BibTeX
@inproceedings{8960807,author = {Quiroga, Luisa and Guzman, Roger and Bolivar, Holman and Rincon, Diego},booktitle = {2019 Congreso Internacional de Innovación y Tendencias en Ingeniería, CONIITI 2019 - Conference Proceedings},doi = {10.1109/CONIITI48476.2019.8960807},isbn = {9781728147468},publisher = {IEEE},title = {{Automatic Classification Method for Academic Characterization of Computer Programming Students}},year = {2019}}
Federated cloud architecture model using opportunistic university resources
Diego Rincon, Cesar Diaz, Olmer Garcia, Holman Bolivar
Journal· RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação(E17), 198--211
Abstract
No abstract is available in the current publication record.
BibTeX
@article{Rincon2019,author = {Rincon, Diego and Diaz, Cesar and Garcia, Olmer and Bolivar, Holman},issn = {16469895},journal = {RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação},number = {E17},pages = {198--211},publisher = {Associação Ibérica de Sistemas e Tecnologias de Informação},title = {{Federated cloud architecture model using opportunistic university resources}},year = {2019}}
2015
1 publication
Sistema de Auto-Cuidado basado en un enfoque de Multi-agentes para el monitoreo y corrección de problemas en una infraestructura tecnológica distribuida
Diego Alberto Rincon, Alvaro Sebastian Miranda, Julian Mauricio Angel
Conference· Congreso Internacional de Innovación y Tendencias en Ingeniería, 1(1), 4
Abstract
No abstract is available in the current publication record.
BibTeX
@inproceedings{RinconYanez2015,author = {Rincon, Diego Alberto and Miranda, Alvaro Sebastian and Angel, Julian Mauricio},booktitle = {Congreso Internacional de Innovación y Tendencias en Ingeniería},number = {1},pages = {4},title = {{Sistema de Auto-Cuidado basado en un enfoque de Multi-agentes para el monitoreo y corrección de problemas en una infraestructura tecnológica distribuida}},volume = {1},year = {2015}}
2012
1 publication
Grid Colombia: Accomplishments and Challenges of an NGI in Latin America
Enrique Gonzalez Guerrero, Harold Enrique Castro, Diego Alberto Rincon Yañez
Conference· Proceedings of the Joint GISELA-CHAIN Conference, 1, 10
Abstract
No abstract is available in the current publication record.
BibTeX
@inproceedings{GonzalezGuerrero2012,author = {{Gonzalez Guerrero}, Enrique and Castro, Harold Enrique and {Rincon Yañez}, Diego Alberto},booktitle = {Proceedings of the Joint GISELA-CHAIN Conference},pages = {10},publisher = {COMETA - 978- 88-95892-05-4},title = {{Grid Colombia: Accomplishments and Challenges of an NGI in Latin America}},volume = {1},year = {2012}}
2011
1 publication
Conexión de un nodo a Grid Colombia
Álvaro Ospina Sanjuan, Enrique Gonzalez Guerrero, Diego Alberto Rincon Yanez, Julián Castaño Marín, Claudia Carmona Rodríguez
Book / chapter· Editorial Pontificia Universidad Javeriana, 1(1), 1-80
Abstract
La iniciativa Grid Colombia tiene por objetivo contribuir al desarrollo científico de Colombia por medio del montaje y la operación de un grid de cómputo de alcance nacional. La infraestructura desarrollada tiene características de escalabilidad y disponibilidad de recursos de cómputo distribuidos, los cuales son administrados para lograr una operación estable y segura. Los componentes de interfaz facilitan el uso de los recursos por parte de los usuarios. El grid nacional tiene la capacidad de expandirse mediante la interconexión a otras mallas computacionales internacionales. El aporte de Grid Colombia —incluso más importante que la infraestructura tecnológica— es la formación de una comunidad de investigadores que trabaja bajo un esquema cooperativo, en el que, aun cuando sus participantes tengan diferentes capacidades en los recursos y servicios ofrecidos, se propicia un ambiente de colaboración científica y técnica. El presente trabajo hace parte de los productos de la iniciativa Grid Colombia y, como tal, aporta al logro de sus objetivos generales, al poner a disposición de la comunidad académica un procedimiento sistémico y optimizado que guía la conexión de un clúster a la malla de cómputo nacional Grid Colombia. Los objetivos son, por un lado, facilitar la participación de las diferentes instituciones educativas en esta iniciativa y, por el otro, apoyar la evolución, crecimiento, actualización y consolidación de los recursos técnicos y tecnológicos que el Gobierno nacional y las diferentes instituciones participantes en el proyecto Grid Colombia y RENATA ofrecen a la comunidad académica y científica.
BibTeX
@book{AlvaroOspinaSanjuan2011,author = {Álvaro Ospina Sanjuan and Enrique Gonzalez Guerrero and Diego Alberto Rincon Yanez and Julián Castaño Marín and Claudia Carmona Rodríguez},edition = {1},isbn = {78-958-716-503-6},issue = {1},journal = {Editorial Pontificia Universidad Javeriana},pages = {1-80},publisher = {Pontificia Universidad Javeriana},title = {Conexión de un nodo a Grid Colombia},volume = {1},year = {2011}}