融合知识图谱与语义查询，实现文化遗产资源的可解释推荐。
A Three-stage Neuro-symbolic Recommendation Pipeline for Cultural Heritage Knowledge Graphs
- 分三阶段：嵌入表示、近邻搜索、语义过滤，结合神经与符号方法。
- 在320万条数据上测试，复合嵌入模型配合HNSW取得最佳效果。
- 推荐结果具可解释性，专家评估证实其实用性与准确性。
数字文化遗产资源的快速增长凸显了理解异构数据实体间语义关系的先进推荐方法的需求。本文提出一种完整的混合推荐流程，整合知识图谱嵌入、近似最近邻搜索和基于SPARQL的语义过滤。研究在CHExRISH项目中构建的JUHMP（雅盖隆大学遗产元数据门户）知识图谱上进行评估，该图谱包含约320万条描述人物、事件、物品及历史关联的RDF三元组。我们评估了四种嵌入方法（TransE、ComplEx、ConvE、CompGCN），并对ComplEx和HNSW进行超参数调优。最终提出的三阶段神经符号推荐器在稀疏且异构的元数据条件下仍生成了有效且可解释的推荐结果，经专家评估验证其有效性。
原文摘要 · Abstract (English)
The growing volume of digital cultural heritage resources highlights the need for advanced recommendation methods capable of interpreting semantic relationships between heterogeneous data entities. This paper presents a complete methodology for implementing a hybrid recommendation pipeline integrating knowledge-graph embeddings, approximate nearest-neighbour search, and SPARQL-driven semantic filtering. The work is evaluated on the JUHMP (Jagiellonian University Heritage Metadata Portal) knowledge graph developed within the CHExRISH project, which at the time of experimentation contained ${\approx}3.2$M RDF triples describing people, events, objects, and historical relations affiliated with the Jagiellonian University (Kraków, PL). We evaluate four embedding families (TransE, ComplEx, ConvE, CompGCN) and perform hyperparameter selection for ComplEx and HNSW. Then, we present and evaluate the final three-stage neuro-symbolic recommender. Despite sparse and heterogeneous metadata, the approach produces useful and explainable recommendations, which were also proven with expert evaluation.
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