As a part of a research project aiming to connect library data to the unfamiliar data sets available in the Linked Data (LD) community's CKAN Data Hub (thedatahub.org), this project collected, analyzed, and mapped properties used in describing and accessing music recordings, scores, and music-related information used by selected music LD data sets, library catalogs, and various digital collections created by libraries and other cultural institutions. This article reviews current efforts to connect music data through the Semantic Web, with an emphasis on the Music Ontology (MO) and ontology alignment approaches; it also presents a framework for understanding the life cycle of a musical work, focusing on the central activities of composition, performance, and use. The project studied metadata structures and properties of 11 music-related LD data sets and mapped them to the descriptions commonly used in the library cataloging records for sound recordings and musical scores (including MARC records and their extended schema.org markup), and records from 20 collections of digitized music recordings and scores (featuring a variety of metadata structures). The analysis resulted in a set of crosswalks and a unified crosswalk that aligns these properties. The paper reports on detailed methodologies used and discusses research findings and issues. Topics of particular concern include (a) the challenges of mapping between the overgeneralized descriptions found in library data and the specialized, music-oriented properties present in the LD data sets; (b) the hidden information and access points in library data; and (c) the potential benefits of enriching library data through the mapping of properties found in library catalogs to similar properties used by LD data sets.
Journal of the American Society for Information Science and Technology
Gracy, Karen F.; Lei Zeng, Marcia; Skirvin, Laurence (2013). Exploring Methods to Improve Access to Music Resources by Aligning Library Data with Linked Data: A Report of Methodologies and Preliminary Findings. Journal of the American Society for Information Science and Technology 64(10) 2078-2099. doi: 10.1002/asi.22914. Retrieved from https://oaks.kent.edu/slispubs/26