Knowledge Graph-Enhanced Information Retrieval for Context-Aware E-Commerce Search
Keywords:
knowledge graphs; e-commerce search; context-aware retrieval; information retrieval; personalization; sustainability; fairness; governanceAbstract
E-commerce search systems increasingly operate at the intersection of information retrieval, knowledge representation, and situated user modeling. Traditional lexical retrieval models remain efficient but often fail to capture the semantic, relational, and contextual dimensions of product search. This paper presents a system-level analysis of knowledge graph-enhanced information retrieval for context-aware e-commerce search. We examine how product knowledge graphs can be constructed, maintained, and integrated into query understanding, retrieval, ranking, and personalization pipelines. Rather than focusing on isolated algorithmic contributions, the paper emphasizes architectural trade-offs, governance requirements, deployment complexity, robustness, fairness, and policy implications. We discuss how graph-based representations support semantic enrichment, relation-aware ranking, explainability, and cross-domain reasoning, while also introducing challenges in schema alignment, knowledge drift, latency, privacy, and auditability. Drawing on contemporary research in neural retrieval, graph representation learning, context-aware computing, and responsible artificial intelligence, the paper argues that sustainable e-commerce search requires a layered socio-technical architecture in which knowledge graphs function as shared semantic infrastructure rather than as a standalone modeling artifact. The analysis provides forward-looking guidance for researchers and system architects seeking to build retrieval platforms that are simultaneously effective, interpretable, and accountable.
References
1. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
2. Singhal, A. (2001). Modern information retrieval: A brief overview. IEEE Data Engineering Bulletin, 24(4), 35-43.
3. Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data: The story so far. International Journal on Semantic Web and Information Systems, 5(3), 1-22.
4. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. Synthesis Lectures on Data, Semantics, and Knowledge, 12(2), 1-257.
5. Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3), 489-508.
6. Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11-33.
7. Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S. (2019). Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 5329-5336.
8. Ai, Q., Zhang, Y., Bi, K., Chen, X., & Croft, W. B. (2019). Learning a hierarchical embedding model for personalized product search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 645-654).
9. Guo, J., Fan, Y., Ai, Q., & Croft, W. B. (2016). A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (pp. 55-64).
10. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171-4186).
11. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems 26 (pp. 2787-2795).
12. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30 (pp. 1024-1034).
13. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
14. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (pp. 3111-3119).
15. Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2015). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 1-55.
16. Yu, X. (2026, January). AI-Driven Personalization across Domains for Local Categorical Query Understanding and Context-Aware Retrieval. In Proceedings of the 2nd International Conference on Artificial Intelligence, Digital Media Technology and Social Computing (pp. 77-82).
17. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
18. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273-1282). PMLR.
19. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (pp. 2503-2511).
20. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org.
21. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.
22. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
23. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
24. Cadwalladr, C., & Graham-Harrison, E. (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The Guardian.
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