Year: 2024
Type: book
Abstract: Knowledge graphs (KGs) and ontologies have been widely adopted for modelling numerous domains. \nHowever, understanding the content of an ontology/KG is far from straightforward: existing methods partially address this issue. \nThis thesis is based on the assumption that identifying the Ontology Design Patterns (ODPs) in an ontology or a KG contributes to address this problem. \nMost times, the reused ODPs are not explicitly annotated, or their reuse is unintentional. \nTherefore, there is a challenge to automatically identify ODPs in existing ontologies and KGs, which is the main focus of this research work. \nThis thesis analyses the role of ODPs in ontology engineering, through experiences in actual ontology projects, placing this analysis in the context of existing ontology reuse approaches. \nMoreover, this thesis introduces a novel method for extracting empirical ODPs (EODPs) from ontologies, and a novel method for extracting EODPs from knowledge graphs, whose schemas are implicit. \nThe first method groups the extracted EODPs in clusters: conceptual components. Each conceptual component represents a modelling problem, e.g. representing collections. \nAs EODPs are fragments possibly extracted from different ontologies, some of them will fall in the same cluster, meaning that they are implemented solutions to the same modelling problem. \nEODPs and conceptual components enable the empirical observation and comparison of modelling solutions to common modelling problems in different ontologies. \nThe second method extracts EODPs from a KG as sets of probabilistic axioms/constraints involving the ontological entities instantiated. \nThese EODPs may support KG inspection and comparison, providing insights on how certain entities are described in a KG. \nAn additional contribution of this thesis is an ontology for annotating ODPs in ontologies and KGs.
Source: Studies on the semantic web
DOI: 10.3233/ssw59
Author: Valentina Anita Carriero
Institution: University of Bologna
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4390818136