Knowledge Graphs, Metadata Practices, and Badiou's Mathematical Ontology
Bibliographic Data
| ID | 19574064 |
|---|---|
| Authors | John Huck (0000-0003-1150-5402, University of Alberta, corresponding author) |
| Year | 2022 |
| Volume | 6 |
| Issue | 3 |
| Pages | 1-17 |
| Publication date | 2022-07-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | KULA knowledge creation dissemination and preservation studies (JOURNAL) |
| Journal identifiers | ISSN: 2398-4112 • E-ISSN: 2398-4112 |
| Publisher | University of Victoria Libraries (PUBLISHER) |
| DOI | 10.18357/kula.192 |
| OpenAlex | W4288060741 |
| Language | EN |
| Citations received | 1 |
| References cited | 6 |
Metadata practices in libraries have been shifting towards a graph-centric data model for a number of years due to the influence of the Semantic Web on metadata standards as well as the ongoing engagement of libraries with linked data. This trend is likely to be sustained by the growth of the knowledge graph domain, which is animated by the interests of large technology companies and which represents a continuation of earlier programmes such as expert systems and the Semantic Web. Given the role of Semantic Web ontologies in knowledge graph development and the relevance of philosophical questions of ontology to cataloguing theory, metadata practitioners require theoretical frameworks suitable for conceptualizing the knowledge graph data model’s mixture of data and ontology. To that end, this paper considers the mathematical ontology of philosopher Alain Badiou, which employs set theory to schematize a theory of the multiple. It outlines how Badiou’s ontology is compatible with the graph data model and what it offers to metadata practitioners seeking to critically engage the knowledge graph paradigm
Epistemology · Graph · Information retrieval · Knowledge graph · Linked Data · Metadata · Ontology · Semantic Web · Upper ontology · World Wide Web · Cognitive Computing and Networks · Computer Science · Semantic Web and Ontologies · Theoretical Computer Science
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |