A Three-stage Neuro-symbolic Recommendation Pipeline for Cultural Heritage Knowledge Graphs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917288458846208 |
|---|---|
| author | Kutt, Krzysztof Sroka, Elżbieta Ishchuk, Oleksandra Miranda, Luiz do Valle |
| author_facet | Kutt, Krzysztof Sroka, Elżbieta Ishchuk, Oleksandra Miranda, Luiz do Valle |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19711 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Three-stage Neuro-symbolic Recommendation Pipeline for Cultural Heritage Knowledge Graphs Kutt, Krzysztof Sroka, Elżbieta Ishchuk, Oleksandra Miranda, Luiz do Valle Information Retrieval Digital Libraries Human-Computer Interaction 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. |
| title | A Three-stage Neuro-symbolic Recommendation Pipeline for Cultural Heritage Knowledge Graphs |
| topic | Information Retrieval Digital Libraries Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.19711 |