D2.4: Methods for interlinking knowledge graphs (V1.0)

Albert Meroño Peñuela, Jacopo De Berardinis, Fiorela Ciroku, Valentina Carriero, Mari Wigham, Enrico Daga, Eleanora Marzi, Elena Musumeci, Peter Van Kranenburg, Andrea Scharnhorst, James McDermott, Philippe Rigaux, Christophe Guillotel-Nothmann, Simon Holland

Research output: Book/ReportReportScientific

Abstract

One of the key missions of the Polifonia project is to build and interlink knowledge graphs of European musical cultural heritage. These interconnected music knowledge graphs act as a data backbone for the project. This deliverable is centred around one of the ten Polifonia pilots: INTERLINK. The INTERLINK pilot is not bound to a specific institutional dataset, collection or use case; but rather summarises the common needs of all pilots of representing their data as music knowledge graphs, and finding meaningful connections among them. 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:
(a) existing knowledge engineering methodologies do not account for “in-between” knowledge graph construction processes that are not fully distributed nor local; (b) in an integrated vision of European musical cultural heritage through knowledge graphs, both musical content and its ’context’ or metadata need to co-exist; and (c) no current techniques address the issue of finding missing links between different music knowledge graphs at scale, accounting for this co-existence of musical content and context in the same knowledge graph.
This deliverable describes our efforts, particularly gathered in Polifonia’s WP2 and Task T2.3, in addressing these challenges, delivering: (a) a scalable methodology for knowledge graph construction that is well suited for the project and sits in-between the two classic extremes of ontology engineering; (b) a music knowledge graph construction framework that leverages modules of the Polifonia Ontology Network and the SPARQL Anything tool; and (c) a music knowledge graph interlinking framework, based on both heuristics and neural network-based link prediction through music knowledge graph embeddings. The latter two are evaluated in two different use-cases, the ChoCo-KG chord annotation knowledge graph, and the MIDI2vec music knowledge graph interlinking method, with encouraging results.
Original languageEnglish
PublisherZenodo
Number of pages72
DOIs
Publication statusPublished - 01 Sep 2022

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