Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges

Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready

The Promise and Challenge of Large Language Models for Knowledge Engineering:Insights from a Hackathon

Knowledge engineering (KE) is the process of managing knowledge in a machine-readable way. This often takes the form of Knowledge Graphs (KGs). The advent of new technologies like Large Language Models (LLMs), besides enhancing automated processes in KG construction, has also changed KE work. We conducted a multiple-methods study exploring user opinions and needs regarding the use of LLMs in KE. We used ethnographic techniques to observe KE workers using LLMs to solve KE tasks during a hackathon, followed by interviews with some of the participants. This interim study found that despite LLMs' promising capabilities for efficient knowledge acquisition and multimodality, their effective deployment requires an extended set of capabilities and training, particularly in prompting and understanding data. LLMs can be useful for simple quality assessment tasks, but in complex scenarios, the output cannot be controlled and evaluation may require novel approaches. With this study, we aim to support with evidence the interaction of KE stakeholders with LLMs, identify areas of potential and understand the barriers to their effective use. Copilot approaches may be valuable in developing processes where the human or a team of humans is assisted by generative AI