Publications

• Hussein, H., Farfar, K. E., Oelen, A., Karras, O., & Auer, S. (2023). Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph. In D. H. Goh, S.-J. Chen, & S. Tuarob (Eds.), Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration (Vol. 14458, pp. 220–229). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8088-8_19
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• Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., Revenko, A., Waltersdorfer, L., Farfar, K. E., & Auer, S. (2023). Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. In C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, & S. Hertling (Eds.), The Semantic Web (Vol. 13870, pp. 372–389). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-33455-9_22
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• Stocker, M., Oelen, A., Jaradeh, M. Y., Haris, M., Oghli, O. A., Heidari, G., Hussein, H., Lorenz, A.-L., Kabenamualu, S., Farfar, K. E., & others. (2023). FAIR scientific information with the open research knowledge graph. FAIR Connect, 1(1), 19–21. https://doi.org/10.3233/FC-221513
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• Auer, S., Oelen, A., Haris, M., Stocker, M., D’Souza, J., Farfar, K. E., Vogt, L., Prinz, M., Wiens, V., & Jaradeh, M. Y. (2020). Improving access to scientific literature with knowledge graphs. Bibliothek Forschung Und Praxis, 44(3), 516–529. https://doi.org/10.1515/bfp-2020-2042
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• Jaradeh, M. Y., Oelen, A., Farfar, K. E., Prinz, M., D’Souza, J., Kismihók, G., Stocker, M., & Auer, S. (2019). Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge. Proceedings of the 10th International Conference on Knowledge Capture, 243–246. https://doi.org/10.1145/3360901.3364435
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• Oelen, A., Jaradeh, M. Y., Farfar, K. E., Stocker, M., & Auer, S. (2019). Comparing research contributions in a scholarly knowledge graph. CEUR Workshop Proceedings; 2526, 2526, 21–26. https://doi.org/10.15488/9388
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• Khadir, M., Farah, N., Farfar, K., Bendaoud, N., Ameyoud, A., & Tenzer, N. (2019). PREVOS-DZ: A Short-Mid Term Algerian Electric Load Forecasting Software. 2019 Algerian Large Electrical Network Conference (CAGRE), 1–6. https://doi.org/10.1109/CAGRE.2019.8713319
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• Farfar, K. E., & Khadir, M. T. (2019). A two-stage short-term load forecasting approach using temperature daily profiles estimation. Neural Computing and Applications, 31, 3909–3919. https://doi.org/10.1007/s00521-017-3324-x
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• Farfar, K. E., & Khadir, M. T. (2016). Day Types Identification of Algerian Electricity Load Using an Image Based Two-Stage Approach. In A. E. P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2016 (Vol. 9887, pp. 415–422). Springer International Publishing. https://doi.org/10.1007/978-3-319-44781-0_49
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