A seasoned Computer Science researcher and engineer dedicated to advancing the field through a unique blend of academic expertise and hands-on deep technical industry experience. With over fifteen years of experience, I've worn multiple hats: University Professor, Researcher, IT Architect, CTO and Full-Stack Engineer for multiple organizations. I am currently stationed as a researcher at the Bosch Center for Artificial Intelligence in Germany, where I continue to explore the intersections of academia and industry.
My current research interests are the Knowledge Graph Construction Methods within Neuro-Symbolic AI, Semantic-powered Smart Manufacturing, Synthetic triple Generation and knowledge graph Embeddings. Additionally, I have a small room in my heart for other computer-related domains such as distributed computing and high-performance computing.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Information Systems and Management, Artificial Intelligence, Computational Theory and Mathematics
LLMs on the Rise: Neuro-Symbolic AI for Knowledge Graph Construction in Manufacturing: Systematic Literature Review Wilma Johanna Schmidt, Diego Rincon-Yanez, Evgeny Kharlamov, Adrian Paschke IEEE Access, 2026 Numerous digitization 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 potentials 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 on manufacturing downstream tasks, e.g., on tasks of reliability- or safety-relevance. This makes it interesting to evaluate the utilization of neuro-symbolic AI and LLMs in KG construction in manufacturing. To the best of our knowledge, there is no systematic literature research on neuro-symbolic AI and LLMs in KGs in manufacturing, yet. 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BERT</i> embeddings, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNN</i> encodings, especially <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BiLSTM, CRF</i> 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 as well as industry application in this field. The main question to guide this review is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Which role play neuro-symbolic AI, especially LLM approaches in knowledge graph construction for manufacturing?”</i>.
Telemedicine-supported structured Orofacial Myofunctional Therapy model for Obstructive Sleep Apnea: Patients’ report outcomes measurements Eliana Elizabeth Rivera Capacho, Claudia Patricia Diaz Bossa, María del Carmen Campos, Diego Rincon-Yanez, Heriberto Rangel-Navia, Esther Mandelbaum Gonçalves Bianchini Respiratory Medicine, 2025 OBJECTIVE: To evaluate the clinical effectiveness and feasibility of the structured ST-OSA-PS-OMT model for adults with Obstructive Sleep Apnea and Primary Snoring, focusing on changes in excessive daytime sleepiness and patient-reported effectiveness within an AI-enhanced telemedicine framework. MATERIALS AND METHODS: Retrospective observational cohort study from November 2021 to November 2022 with 87 adults with OSA and PS. They received OMT through the Smart Therapy for Obstructive Sleep Apnea and Primary Snoring with Orofacial Myofunctional Therapy (ST-OSA-PS-OMT) program. Demographic variables, clinical data, orofacial myofunctional examination, and satisfaction were evaluated using the Perception Scale of Effectiveness of Orofacial Myofunctional Therapy in Sleep-Disordered Breathing -Patient Reported Outcome Measures (EPE-TMO-TRS-PROMs), type 1 (83%)/type 3 (17%) polysomnography, and the Epworth Sleepiness Scale (ESS). RESULTS: ). The majority rated OMT as effective. Descriptive analysis revealed that patients with psychiatric comorbidities tended to perceive treatment as more effective, while those with Class III malocclusion showed a tendency toward lower treatment favorability. CONCLUSION: The ST-OSA-PS-OMT model represents a viable and effective therapeutic approach for OSA/PS, as evidenced by significant ESS improvement and positive patient-reported outcomes. The AI-enhanced telemedicine framework establishes a foundation for therapeutic personalisation. Key anatomical findings, particularly the association between a descended velum and erythematous pillars, justify refined pre-treatment assessment protocols. Future research should focus on longitudinal studies and developing targeted intervention strategies for subgroups with less favourable responses, including those with Angle Class III malocclusion.
Injecting Knowledge Graph Embeddings into RAG Architectures: Scalable Fact-Checking for Combating Disinformation with LLMs Ceur Workshop Proceedings, 2025
Second International Workshop on Scaling Knowledge Graphs for Industry (SKGi) - LLMs meet KGs: Preface Ceur Workshop Proceedings, 2025
Harnessing Graph Neural Networks to Predict International Trade Flows Bassem Sellami, Chahinez Ounoughi, Tarmo Kalvet, Marek Tiits, Diego Rincon-Yanez Big Data and Cognitive Computing, 2024 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.
Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models Ceur Workshop Proceedings, 2024
Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings Diego Rincon-Yanez, Chahinez Ounoughi, Bassem Sellami, Tarmo Kalvet, Marek Tiits, Sabrina Senatore, Sadok Ben Yahia Journal of King Saud University Computer and Information Sciences, 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.
A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems Giuseppina Di Paolo, Diego Rincon-Yanez, Sabrina Senatore Information Switzerland, 2023 Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form <subject, predicate, object>, while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application.
Addressing the Scalability Bottleneck of Semantic Technologies at Bosch Diego Rincon-Yanez, Mohamed H. Gad-Elrab, Daria Stepanova, Kien Trung Tran, Cuong Chu Xuan, Baifan Zhou, Evgeny Karlamov Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch Ceur Workshop Proceedings, 2023
Federated cloud architecture model using opportunistic university resources Risti Revista Iberica De Sistemas E Tecnologias De Informacao, 2019
RECENT SCHOLAR PUBLICATIONS
Digitising Death: Benchmarking Genealogical Data and Recovering Women’s Histories in Early Modern Ireland BA McShane, D Rincon-Yanez, F Vanden Borre, J Ohlmeyer, ... Journal of Open Humanities Data 12 (1) , 2026 2026
LLMs on the Rise: Neuro-Symbolic AI for Knowledge Graph Construction in Manufacturing (Systematic Literature Review) WJ Schmidt, D Rincon-Yanez, E Kharlamov, A Paschke IEEE access , 2026 2026 Citations: 7
From Data Spaces to Agent Spaces A Polleres, D Rincon-Yanez, A Anjomshoaa, D Dobriy, E Sallinger 2026
Telemedicine-supported structured Orofacial Myofunctional Therapy model for Obstructive Sleep Apnea: Patients’ report outcomes measurements EER Capacho, CPD Bossa, M del Carmen Campos, D Rincon-Yanez, ... Respiratory Medicine, 108460 , 2025 2025 Citations: 2
Scaling NeuroSymbolic AI Integration for Seismic Event Detection D Rincon-Yanez, S Senatore, D O’Sullivan European Semantic Web Conference, 108-113 , 2025 2025 Citations: 1
Second International Workshop on Scaling Knowledge Graphs for Industry (SKGi)-LLMs meet KGs: Preface D Rincon-Yanez, WJ Schmidt, E Kharlamov, M Cochez, A Paschke, ... Joint of Posters, Demos, Workshops, and Tutorials of the 21st International … , 2025 2025
Injecting Knowledge Graph Embeddings into RAG Architectures: Scalable Fact-Checking for Combating Disinformation with LLMs OA Ubaque, D Rincon-Yanez, D O’Sullivan 2025
Document-Level Relation Extraction with Ontology-Guided RAG WJ Schmidt, D Rincon-Yanez, I Grangel-González, A Paschke, ... Linking Meaning: Semantic Technologies Shaping the Future of AI, 19-27 , 2025 2025 Citations: 2
A knowledge database discovery approach for improving quality in higher education institutions MOP Pulido, OCM Ardila, FJ Leon, LFB Chacón, H Bolivar, ... Data-Driven Insights and Analytics for Measurable Sustainable Development … , 2025 2025
Leveraging shallow learning techniques to support the methodological design of public policies in low density areas in Colombia P Cardenas-Malpica, DYE Pabón, M Perpinan-Arahuho, H Rangel-Navia, ... Data-Driven Insights and Analytics for Measurable Sustainable Development … , 2025 2025 Citations: 1
Expanding the Virtual Record Treasury of Ireland Knowledge Graph B Yaman, A Randles, L McKenna, L Kilgallon, D Rincon-Yanez, ... 2025 Citations: 2
Human-Oriented Fuzzy-Based Assessments of Knowledge Graph Embeddings for Fake News Detection K Gutiérrez-Batista, D Rincon-Yanez, S Senatore International Conference on Information Processing and Management of … , 2024 2024 Citations: 2
Harnessing graph neural networks to predict international trade flows B Sellami, C Ounoughi, T Kalvet, M Tiits, D Rincon-Yanez Big Data and Cognitive Computing 8 (6), 65 , 2024 2024 Citations: 30
RAING: Generación Aumentada por Recuperación Utilizando Grafos de Conocimiento: Un Enfoque para Descubrir Noticias Falsas O Abaunza Ubaque Pontificia Universidad Javeriana , 2024 2024
20th International Conference, IPMU 2024, Lisbon, Portugal, July 22–26, 2024, Proceedings, Volume MJ Lesot, S Vieira, MZ Reformat, JP Carvalho, F Batista, ... 2024 Citations: 25
Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models WJ Schmidt, D Rincon-Yanez, E Kharlamov, A Paschke 2024 Citations: 7
Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings D Rincon-Yanez, C Ounoughi, B Sellami, T Kalvet, M Tiits, S Senatore, ... Journal of King Saud University-Computer and Information Sciences 35 (10 … , 2023 2023 Citations: 28
Addressing the scalability bottleneck of semantic technologies at bosch D Rincon-Yanez, MH Gad-Elrab, D Stepanova, KT Tran, C Chu Xuan, ... European Semantic Web Conference, 177-181 , 2023 2023 Citations: 6
A quick prototype for assessing OpenIE knowledge graph-based question-answering systems G Di Paolo, D Rincon-Yanez, S Senatore Information 14 (3), 186 , 2023 2023 Citations: 11
Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch Z Zheng, B Zhou, Z Tan, O Savkovic, D Rincon-Yanez, NV Nikolov, ... 2023 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Semantically enhanced IoT-oriented seismic event detection: An application to Colima and Vesuvius volcanoes M Falanga, E De Lauro, S Petrosino, D Rincon-Yanez, S Senatore IEEE Internet of Things Journal 9 (12), 9789-9803 , 2022 2022 Citations: 34
Harnessing graph neural networks to predict international trade flows B Sellami, C Ounoughi, T Kalvet, M Tiits, D Rincon-Yanez Big Data and Cognitive Computing 8 (6), 65 , 2024 2024 Citations: 30
Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings D Rincon-Yanez, C Ounoughi, B Sellami, T Kalvet, M Tiits, S Senatore, ... Journal of King Saud University-Computer and Information Sciences 35 (10 … , 2023 2023 Citations: 28
20th International Conference, IPMU 2024, Lisbon, Portugal, July 22–26, 2024, Proceedings, Volume MJ Lesot, S Vieira, MZ Reformat, JP Carvalho, F Batista, ... 2024 Citations: 25
FAIR Knowledge Graph Construction from Text, an Approach Applied to Fictional Novels. D Rincon-Yanez, S Senatore TEXT2KG/MK@ ESWC, 94-108 , 2022 2022 Citations: 19
A quick prototype for assessing OpenIE knowledge graph-based question-answering systems G Di Paolo, D Rincon-Yanez, S Senatore Information 14 (3), 186 , 2023 2023 Citations: 11
Enhancing downstream tasks in knowledge graphs embeddings: A complement graph-based approach applied to bilateral trade D Rincon-Yanez, A Mouakher, S Senatore Procedia Computer Science 225, 3692-3700 , 2023 2023 Citations: 11
Towards a semantic model for IoT-based seismic event detection and classification D Rincon-Yanez, E De Lauro, M Falanga, S Senatore, S Petrosino 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 189-196 , 2020 2020 Citations: 9
Identifying the fingerprint of a volcano in the background seismic noise from machine learning-based approach D Rincon-Yanez, E De Lauro, S Petrosino, S Senatore, M Falanga Applied Sciences 12 (14), 6835 , 2022 2022 Citations: 8
LLMs on the Rise: Neuro-Symbolic AI for Knowledge Graph Construction in Manufacturing (Systematic Literature Review) WJ Schmidt, D Rincon-Yanez, E Kharlamov, A Paschke IEEE access , 2026 2026 Citations: 7
Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models WJ Schmidt, D Rincon-Yanez, E Kharlamov, A Paschke 2024 Citations: 7
Addressing the scalability bottleneck of semantic technologies at bosch D Rincon-Yanez, MH Gad-Elrab, D Stepanova, KT Tran, C Chu Xuan, ... European Semantic Web Conference, 177-181 , 2023 2023 Citations: 6
Enabling a Semantic Sensor Knowledge Approach for Quality Control Support in Cleanrooms D Rincon-Yanez, F Crispoldi, D Onorati, P Ulpiani, G Fenza, S Senatore The 20th International Web Semantic Conference 2980, 3 , 2021 2021 Citations: 3
Telemedicine-supported structured Orofacial Myofunctional Therapy model for Obstructive Sleep Apnea: Patients’ report outcomes measurements EER Capacho, CPD Bossa, M del Carmen Campos, D Rincon-Yanez, ... Respiratory Medicine, 108460 , 2025 2025 Citations: 2
Document-Level Relation Extraction with Ontology-Guided RAG WJ Schmidt, D Rincon-Yanez, I Grangel-González, A Paschke, ... Linking Meaning: Semantic Technologies Shaping the Future of AI, 19-27 , 2025 2025 Citations: 2
Expanding the Virtual Record Treasury of Ireland Knowledge Graph B Yaman, A Randles, L McKenna, L Kilgallon, D Rincon-Yanez, ... 2025 Citations: 2
Human-Oriented Fuzzy-Based Assessments of Knowledge Graph Embeddings for Fake News Detection K Gutiérrez-Batista, D Rincon-Yanez, S Senatore International Conference on Information Processing and Management of … , 2024 2024 Citations: 2
Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch Z Zheng, B Zhou, Z Tan, O Savkovic, D Rincon-Yanez, NV Nikolov, ... 2023 Citations: 2
Federated cloud architecture model using opportunistic university resources D Rincon-Yanez, C Diaz, O Garcia, H Bolivar Revista Ibérica de Sistemas e Tecnologias de Informação, 198-211 , 2019 2019 Citations: 2
Estudio de los resultados del Programa ADSI del Centro de Servicios Financieros y su compromiso y pertinencia ante una problemática de orden nacional DA Rincon Yanez, SI Galvis Motoa Simposio Nacional de Formación con Calidad y Pertinencia 2 , 2015 2015 Citations: 2
