In this work, we study disagreements in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are essential in collaborative work as they can increase contributor performance and encourage the emergence of shared norms and practices. While disagreements can play a productive role in discussions, they can also lead to conflicts and controversies, which impact contributor’ well-being and their motivation to engage. We want to understand if and when such phenomena arise in Wikidata, using a mix of quantitative and qualitative analyses to identify the types of topics people disagree about, the most common patterns of interaction, and roles people play when arguing for or against an issue. We find that decisions to create Wikidata properties are much faster than those to delete properties and that more than half of controversial discussions do not lead to consensus. Our analysis suggests that Wikidata is an inclusive community, considering different opinions when making decisions, and that conflict and vandalism are rare in discussions. At the same time, while one-fourth of the editors participating in controversial discussions contribute legitimate and insightful opinions about Wikidata’s emerging issues, they respond with one or two posts and do not remain engaged in the discussions to reach consensus. Our work contributes to the analysis of collaborative KG construction with insights about communication and decision-making in projects, as well as with methodological directions and open datasets. We hope our findings will help managers and designers support community decision-making and improve discussion tools and practices.

Abstract In the music domain, several ontologies have been proposed to annotate musical data, in both symbolic and audio form, and generate semantically rich Music Knowledge Graphs. However, current models lack interoperability and are insufficient for representing music history and the cultural heritage context in which it was generated; risking the propagation of recency and cultural biases to downstream applications. In this article, we propose the Polifonia Ontology Network (PON) for music cultural heritage, centred around four modules: Music Meta (metadata), Representation (content), Source (provenance) and Instrument (cultural objects). We design PON with a strong accent on cultural stakeholder requirements and competency questions (CQs), contributing an NLP-based toolkit to support knowledge engineers in generating, validating, and analysing them; and a novel, high-quality CQ dataset produced as a result. We show current and future use of these resources by internal project pilots, early adopters in the music industry, and opportunities for the Semantic Web and Music Information Retrieval communities.

Knowledge graphs (KGs) provide structured representation of data in the form of relations between different entities. The semantics of relations between words and entities are often ambiguous, where it is common to find polysemous relations that represent multiple semantics based on the context. This ambiguity in relation semantics also proliferates KG triples. While the guidance from custom-designed ontologies addresses this issue to some extent, our analysis shows that the heterogeneity and complexity of real-world data still results in substantial relation polysemy within popular KGs. The correct semantic interpretation of KG relations is necessary for many downstream applications such as entity classification and question answering. We present the problem of fine-grained relation discovery and a data-driven method towards this task that leverages the vector representations of the knowledge graph entities and relations available from relational learning models. We show that by performing clustering over these vectors, our method is able to not only identify the polysemous relations in knowledge graphs, but also discover the different semantics associated with them. Extensive empirical evaluation shows that fine-grained relations discovered by the proposed approach lead to substantial improvement in the semantics in the Yago and NELL datasets, as compared to baselines. Additional insights from qualitative analyses convey that fine-grained relation discovery is an important yet complex task, especially in the presence of complex ontologies and noisy data.