INK: knowledge graph embeddings for node classification

Modeling Relational Data with Graph Convolutional Networks

Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works

DBpedia: A Nucleus for a Web of Open Data

Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Richard Cyganiak, A., & Ives, Z. G. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76298-0_52 · Cited by 4,847 · see citing works

Knowledge Graph Embedding by Translating on Hyperplanes

Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works

Learning Entity and Relation Embeddings for Knowledge Graph Completion

Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works

Wikidata

Vrandečić, D., & Krötzsch, M. (2014) · Communications of the ACM · doi:10.1145/2629489 · Cited by 3,367 · see citing works

A Review of Relational Machine Learning for Knowledge Graphs

Nickel, M., Murphy, K. P., Tresp, V., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,684 · see citing works

A survey of feature selection and feature extraction techniques in machine learning

Khalid, S., Khalil, T., & Nasreen, S. (2014) · Science and Information Conference · doi:10.1109/sai.2014.6918213 · Cited by 1,246 · see citing works

Deep Graph Kernels

Yanardag, P., & Vishwanathan, S. V. N. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783417 · Cited by 1,108 · see citing works

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2053 · Cited by 923 · see citing works

Modeling Relation Paths for Representation Learning of Knowledge Bases

Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1082 · Cited by 657 · see citing works

TransG : A Generative Model for Knowledge Graph Embedding

Xiao, H., Huang, M., & Zhu, X. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1219 · Cited by 370 · see citing works

A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

Nguyen, D. Q., Vu, T., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1226 · Cited by 282 · see citing works

An Introduction to the Resource Description Framework

Miller, E. F. (1998) · Bulletin of the American Society for Information Science and Technology · doi:10.1002/bult.105 · Cited by 271 · see citing works

RDF2Vec: RDF graph embeddings and their applications

Ristoski, P., Rosati, J., Noia, T. D., Leone, R. D., & Paulheim, H. (2018) · Semantic Web · doi:10.3233/sw-180317 · Cited by 178 · see citing works

On The Role of Knowledge Graphs in Explainable AI

Lécué, F. (2020) · PROFILES/SEMEX@ISWC · doi:10.3233/sw-190374 · Cited by 165 · see citing works

Supporting Linked Data Production for Cultural Heritage Institutes: The Amsterdam Museum Case Study

de Boer, V., Wielemaker, J., van Gent, J., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30284-8_56 · Cited by 92 · see citing works

Graph Kernels for RDF Data

Lösch, U., Bloehdorn, S., & Rettinger, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30284-8_16 · Cited by 87 · see citing works

A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web

Ristoski, P., de Vries, G. K. D., & Paulheim, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46547-0_20 · Cited by 86 · see citing works

Web Semantics & Ontology

(2006) · IGI Global eBooks · doi:10.4018/978-1-59140-905-2 · Cited by 68 · see citing works

The knowledge graph as the default data model for learning on heterogeneous knowledge

Wilcke, X., Bloem, P., & de Boer, V. (2017) · Data Science · doi:10.3233/ds-170007 · Cited by 68 · see citing works

Generating Possible Interpretations for Statistics from Linked Open Data

Paulheim, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30284-8_44 · Cited by 61 · see citing works

How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

Anelli, V. W., Noia, T. D., Sciascio, E. D., Ragone, A., & Trotta, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30793-6_3 · Cited by 40 · see citing works

Large-scale relation extraction from web documents and knowledge graphs with human-in-the-loop

Ristoski, P., Gentile, A. L., Alba, A., Gruhl, D., & Welch, S. (2019) · Journal of Web Semantics · doi:10.1016/j.websem.2019.100546 · Cited by 34 · see citing works

The DEBS 2017 Grand Challenge

Gulisano, V. M., Jerzak, Z., Katerinenko, R., et al. (2017) · ACM International Conference on Distributed and Event-Based Systems (DEBS) · doi:10.1145/3093742.3096342 · Cited by 26 · see citing works

Representation Learning of Knowledge Graphs With Entity Attributes

Zhang, Z., Cao, L., Chen, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2963990 · Cited by 22 · see citing works

Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

Steenwinckel, B., Vandewiele, G., Bonte, P., Weyns, M., Paulheim, H., Ristoski, P., Turck, F. D., & Ongenae, F. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-87101-7_8 · Cited by 20 · see citing works

GTrans: Generic Knowledge Graph Embedding via Multi-State Entities and Dynamic Relation Spaces

Tan, Z., Zhao, X. H., Fang, Y., & Xiao, W. (2018) · IEEE Access · doi:10.1109/access.2018.2797876 · Cited by 18 · see citing works

What is the schema of your knowledge graph?

Zouaq, A., & Martel, F. (2020) · doi:10.1145/3391274.3393637 · Cited by 15 · see citing works

FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs

Portisch, J., Hladik, M., & Paulheim, H. (2021) · The Web Conference (WWW) · doi:10.1145/3442442.3451382 · Cited by 6 · see citing works

Explainable knowledge graph embeddings for industrial process monitoring & control

Weyns, M., Blyau, T., Steenwinckel, B., De Turck, F., Van Hoecke, S., & Ongenae, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103242 · Cited by 7 · see citing works

Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs

Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3652304 · Cited by 6 · see citing works

An Adaptable Real-Time Object Detection for Traffic Surveillance using R-CNN over CNN with Improved Accuracy

Vinod, G., & Padmapriya, G. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759030 · Cited by 11 · see citing works

SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS

Lutsenko, V. S., & Shukhman, A. E. (2022) · Vestnik komp iuternykh i informatsionnykh tekhnologii · doi:10.14489/vkit.2022.06.pp.040-050 · Cited by 1 · see citing works

Convolutional Neural Network (CNN) Applied to the Risk Analysis of Accidents in Vessels Navigating the Amazon Rivers

Nascimento, A. V. D., Ramos, C. C., Pantoja, J. A. B., et al. (2023) · International Journal of Advanced Engineering Research and Science · doi:10.22161/ijaers.102.6

Age Estimation Method based on Comparative Convolutional Neural Network using Inception Module

Kim, Y. H., soo, J. Y., Chung, Y.-S., et al. (2018) · Journal of Korean institute of intelligent systems · doi:10.5391/jkiis.2018.28.3.193 · Cited by 2 · see citing works

Convolutional Neural Network for Automated Analyzing of Medical Images

Cibi, M. A., & Rose, R. J. (2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.g5629.059720 · Cited by 3 · see citing works