The increasing availability of heterogeneous cultural heritage (CH) data calls for semantic, scalable, and operational infrastructures capable of integrating symbolic knowledge with multimodal representations. Existing CH knowledge graphs (KGs) are typically built from individual institutions’ datasets and focus predominantly on semantic modelling. They seldom incorporate the results of AI methods, such as visual representations or neural retrieval methods, which limit their ability to support cross-collection interoperability and advanced multimodal retrieval and sustain continuous, automated data enrichment. In this paper, we introduce ArtKB: a modular end-to-end knowledge base (KB) that unifies semantic modelling, multimodal representation, and operational access to artefacts. Built from Wikidata and annotated using the CACAO ontology, the proposed system combines an RDF graph, a vector database for visual embeddings, and an object storage for digital assets, all orchestrated through a unified API gateway. This infrastructure enables hybrid and multimodal retrieval, metadata enrichment and automated text-to-graph generation, demonstrating how neuro-symbolic approaches can enhance CH data management. The paper details the architecture of the domain-specific knowledge base, demonstrating how the different components integrate to support different tasks. The resulting KB offers a scalable, interoperable, and domain-agnostic solution that can be adapted to broader CH contexts and other multimodal domains.