Database and Knowledge Engineering

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  3. Database and Knowledge Engineering

Database and Knowledge Engineering Group

Professors: Sang-Wook Kim, Jaehyuk Cha

Database and knowledge engineering groups perform research on database technologies by managing various types of data efficiently and data mining technologies searching for meaningful patterns or knowledge from big data exploiting statistics, artificial intelligence or machine learning techniques. Ongoing research projects are (distributed) database designs for high-end computing platforms with cutting-edge multicores and flash-based non-volatile storage as well as data mining techniques for big data processing in social network analysis or recommendation systems.

Selected Publications

  • Jang, M.-H., J.-M. Park, Y. Ko, and Sang-Wook Kim. “Accelerating storage-based training for graph neural networks.” In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM. 2026.
  • Seo, D.-H., S. Kim, T. Kim, W.-Y. Shin, and Sang-Wook Kim. “SCOUT: Structure-aware aspect and anchor-count selection for positional attribute augmentation.” In Proceedings of the ACM Web Conference (WWW). ACM, 2026.
  • Kim, M.-J., J. Son, Y.-C. Lee, and Sang-Wook Kim. “Retracing and restoring: Chronological context preservation for effective dynamic recommendation.” In Proceedings of the ACM Web Conference (WWW). ACM. 2026.
  • Ryu, S., and Sang-Wook Kim. “PANther: Party-specific attention-based networks for political perspective detection.” In Proceedings of the ACM Symposium on Applied Computing (SAC). ACM. 2026.
  • Kim, T., J. Kim, W.-Y. Shin, and Sang-Wook Kim. “Leveraging retrieval-augmented language models for accurate item/feature selection in conversational recommender systems.” In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). ACM. 2026.
  • Ko, Y., D. E. Lee, S. K. Yu, and Sang-Wook Kim. “Learning short-term and long-term patterns of high-order dynamics in real-world networks.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Ko, Y., S. Ryu, and Sang-Wook Kim. “Is this news still interesting to you?: Lifetime-aware interest matching for news recommendation.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Jang, M.-H., M.-K. Shin, T. Park, Y. Park, and Sang-Wook Kim. “An efficient PIM-based graph engine on a single machine.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Park, J.-M., M.-H. Jang, and Sang-Wook Kim. “RealGraphGPUWeb: A convenient and efficient GPU-based graph analysis platform on the web.” Journal of the Korean Institute of Smart Media (KISM) (2025).
  • Kim, T., S. Ban, H. Kim, and Sang-Wook Kim. “STARLINE: Contrastive learning with modality-aware graph refinement for effective multimedia recommendation.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2025.
  • Reyhani Hamedani, M., J.-S. Ryu, and Sang-Wook Kim. “SIGEM: A simple yet effective similarity based graph embedding method.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Vol. 2. ACM, 2025.
  • Son, J., J. Kim, T. Kim, Y.-C. Lee, and Sang-Wook Kim. “CATER: A cluster-based alternative-term recommendation framework for large-scale web search at NAVER.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (pp. 2548-2559). ACM, 2025.
  • Lee, Y.-C., J. Lee, M. Yamashita, D. Lee, and Sang-Wook Kim. “CAPER: Enhancing career trajectory prediction using temporal knowledge graph and ternary relationship.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2025.
  • Bae, H.-K., J. Kim, J. Lee, and Sang-Wook Kim. “DUET: Dually guided knowledge distillation from explicit feedback.” Information Fusion 120 (2025): 103098.
  • Ko, Y., H. Tong, and Sang-Wook Kim. “Enhancing hyperedge prediction with context-aware self-supervised learning.” IEEE Transactions on Knowledge and Data Engineering 37, no. 4 (2025): 1772-1784.
  • Son, J., H. Kim, and Sang-Wook Kim. “Rating-aware homogeneous review graphs and user likes/dislikes differentiation for effective recommendations.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 2070-2080). ACM, 2025.
  • Jeong, D., T. Kim, D. Cho, and Sang-Wook Kim. “MELON: Learning multi-aspect modality preferences for accurate multimedia recommendation.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1850-1859). ACM, 2025.
  • Xu, J., Z. Chen, W. Wang, X. Hu, Sang-Wook Kim, and E. C. H. Ngai. “COHESION: Composite graph convolutional network with dual-stage fusion for multimodal recommendation.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1830-1839). ACM, 2025.
  • Jang, M.-H., I. Jo, D.-H. Bae, and Sang-Wook Kim. “RealGraphOF: A high-performance graph engine for very large graph analysis.” In Proceedings of the ACM Web Conference (WWW). ACM, 2025.
  • Bae, H.-K., H.-R. Jang, W.-Y. Shin, and Sang-Wook Kim. “Ranking items by the current-preferences and profits: A list-wise learning-to-rank approach to profit maximization.” In Proceedings of the ACM Web Conference (WWW) (pp. 5010-5021). ACM, 2025.
  • Yu, S. K., D. E. Lee, Y. Ko, and Sang-Wook Kim. “HyGEN: Regularizing negative hyperedge generation for accurate hyperedge prediction.” In Companion Proceedings of the ACM Web Conference (WWW) (pp. 1500-1504). ACM, 2025.
  • Seo, D.-H., H. Shin, and Sang-Wook Kim. “Exploring search volumes of terms in web portals for accurate event-aware traffic prediction.” In Companion Proceedings of the ACM Web Conference (WWW) (pp. 1293-1297). ACM, 2025.
  • Ko, Y., S. Ryu, and Sang-Wook Kim. “CROWN: A novel approach to comprehending users' preferences for accurate personalized news recommendation.” In Proceedings of the ACM Web Conference (WWW) (pp. 1911-1921). ACM, 2025.
  • Son, J., D.-H. Seo, J. Song, K. Han, N. Kim, and Sang-Wook Kim. “A novel traffic flow prediction model based on a direct spatio-temporal graph.” In Proceedings of the ACM Symposium on Applied Computing (SAC) (pp. 415-424). ACM, 2025.
  • Kim, T., J. Heo, H. Kim, and Sang-Wook Kim. “HI-DR: Exploiting health status-aware attention and an EHR Graph+ for effective medication recommendation.” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11950-11958.
  • Kim, T., H. Jang, J. Yu, T. Kim, H. Lee, J.-H. Im, and Sang-Wook Kim. “ESPRESSO: An effective approach to passage retrieval for high-quality conversational recommender systems.” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11942-11949.
  • Lee, Y.-C., H. Shin, and Sang-Wook Kim. “Disentangling, amplifying, and debiasing: Learning disentangled representations for fair graph neural networks.” Proceedings of the AAAI Conference on Artificial Intelligence 39 (2025): 12013-12021.
  • Song, S., Y. Jang, D. Jung, K. Park, D. Kim, G. Kim, Hunjun Lee, J. Yoo, and J. Kim. “TierX: A simulation framework for multi-tier BCI system design evaluation and exploration.” In Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Vol. 2 (pp. 1998-2014). ACM, 2026.
  • Kim, J., B. Jeong, C. Kim, N. Ryu, E. Pak, and Hunjun Lee. “Accurate and high-throughput analog-digital DNN acceleration using sub-network scheduling.” IEEE Computer Architecture Letters. (2026).
  • Lee, I., J. Park, S. Lee, T. H. Kim, J. Seo, Hunjun Lee, and Y. Park. “Efficient image super-resolution using dynamic quality control with recursive model structures.” IEEE Access 13 (2025).
  • Jang, Y., D. Jung, S. Song, Hunjun Lee, and J. Kim. “InfiniMind: A learning-optimized large-scale brain-computer interface.” In Proceedings of the ACM/IEEE International Symposium on Computer Architecture (ISCA) (pp. 1969-1985). ACM, 2025.
  • Siddiqui, S., J. A. Khan, T. Akram, M. Alharbi, Jaehyuk Cha, and D. A. AlHammadi. “SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders.” SLAS Technology 33 (2025): 100304.
  • Fatima, M., M. A. Khan, A. M. Mirza, J. Shin, A. Alasiry, M. Marzougui, Jaehyuk Cha, and B. Chang. “An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.” Scientific Reports 15, no. 1 (2025): 22027.
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