Ontology distances/Distances entre ontologies in areas (2024-02-10)

The Semantic Web is an extension of the web that enables people to express knowledge in a way that machines can reason with it. At the web scale, this knowledge may be described using different ontologies, and alignments have been defined to express these differences. Furthermore, the same individual may be represented by different instances in different datasets. Dealing with knowledge heterogeneity in the Semantic Web requires comparing these knowledge structures. Our objective is to understand heterogeneity and benefit from this understanding, not to reduce diversity. In this context, we have studied and contributed to techniques and measures for comparing knowledge structures on the Semantic Web along three dimensions: ontologies, alignments, and instances. At the ontology level, we propose measures for the ontology space and alignment space. The first family of measures relies solely on the content of ontologies, while the second one takes advantage of alignments between ontologies. At the alignment level, we investigate how to assess the quality of alignments. First, we study how to extend classical controlled evaluation measures by considering the semantics of aligned ontologies while relaxing the all-or-nothing nature of logical entailment. We also propose estimating the quality of alignments when no reference alignment is available. At the instance level, we tackle the challenge of identifying resources from different knowledge graphs that represent the same entity. We follow an approach based on keys and alignments. Specifically, we propose the notion of a link key, algorithms for extracting them, and measures to assess their quality. Finally, we recast this work in the perspective of the dynamics and evolution of knowledge.

Assessing knowledge diversity may be useful for many purposes. In particular, it is necessary to measure diversity in order to understand how it arises or is preserved; it is also necessary to control it in order to measure its effects. Here we consider measuring knowledge diversity using two components: (a) a diversity measure taking advantage of (b) a knowledge difference measure. We present the general principles and various candidates for such components. We discuss how these measures may be used to generate populations of agents with controlled levels of knowledge diversity.