2023/06/30 by Gerd Stumme, Stumme, Gerd, Dominik Dürrschnabel +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Cognitive Science and Mapping #Computing methodologies → Algebraic algorithms #Computing methodologies → Boolean algebra algorithms #Computing methodologies → Inductive logic learning #Computing methodologies → Nonmonotonic #Computing methodologies → Ontology engineering #Computing methodologies → Rule learning #Computing methodologies → Semantic networks #Computing methodologies → Unsupervised learning #Data Visualization and Analytics #Order relation #approximations and heuristics #browsing #data science #default reasoning and belief revision #explainability #factor analysis #factorization #general algebra #lattices #metric learning #relational theory of measurement #visualization
paper · pdf · doi:10.4230/tgdk.1.1.6
openalex publication_date 2023/06/30 · openalex created_date 2023/07/20 · openalex updated_date 2026/07/28
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.