title: D2.5: Methods for interlinking knowledge graphs (V1.0)
title: DRAFT
titleType: Subtitle
Creators: name: De Berardinis, Jacopo
name: Reklos, Ioannis
name: Meroño Peñuela, Albert
name: Simperl, Elena
name: Lazzari, Nicolas
name: Presutti, Valentina
Publisher: name: Zenodo
Year: 2024

description: The INTERLINK pilot aims at connecting knowledge graphs of European musical cultural heritage, which are notoriously large and use a diversified vocabulary of relations. The pilot is not bound to the specific institutional dataset, collections or use cases; but rather summarises the common needs of all pilots of representing their data as music knowledge graphs, and develop technologies that will enable finding new meaningful connections among them. Initial efforts were devoted at creating ChoCo – the largest knowledge graph of music harmony, and harmonising the stakeholders’ requirements into the Polifonia Ontology Network – to facilitate entity linking and ontology alignment upfront. This provided the infrastructure and the requirements to address the final challenge of the pilot: i.e. linking knowledge graphs at scale.As pointed in the previous versions of the deliverable, existing work in the relevant areas of knowledge engineering, knowledge graph construction, and knowledge graph interlinking is promising but presents a number of pitfalls in its direct application to Polifonia from the point of view of achieving this knowledge graph building and interlinking that necessitates scalability, inductiveness, and computational efficiency. Learning numerical representations from knowledge graphs is currently the most prominent approach to address link prediction, in addition to knowledge discovery and reasoning tasks. This is often done by allocating and learning embedding tables for all (or a subset of) the entities. As this scales linearly with the number of entities, learning embedding models in real-world KGs with millions of entities is computationally intractable.This deliverable describes our efforts delivering a novel method for knowledge graph embeddings that: (i) only allocates embedding tables for relations (which are typically orders of magnitude fewer than the entities) and requires less than 50% of the parameters of previous parameter efficient methods; (ii) rather than storing entity embeddings, it learns to compute them – by leveraging multiple entity-relation paths to contextualise individual entities within triples. Evaluated on various benchmarks, our method achieves significant results on path-rich KGs and can yield state-of-the-art performance for link prediction while training on consumer-grade hardware. We perform ablation experiments to analyse the sensitivity of our model to key hyper-parameters, and provide insights into its applicability based on specific KG properties. To contextualise our work, we discuss (a) how a scalable methodology for link prediction is well suited for the requirements and the long-term vision of Polifonia; and (b) our future plans for applying our method to the Polifonia Knowledge Graphs to interlink them with large resources (Wikidata).Finally, to complement our vision, we also report a novel computation method for modelling musical influence – which enables the detection of creative influence between artists using techniques based on graph theory and complex network science. As this method takes into account the individuality of a relationship type, this provides a powerful tool to infer further links within and across music knowledge graphs.