Ontological Modelling and Social Networks: From Expert Validation to Consolidated Domains

Pileggi, Salvatore F.

doi:10.1007/978-3-031-36030-5_53

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2024

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Cited by 2 publications

References 44 publications

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A Cross-Domain Perspective to Clustering with Uncertainty

Pileggi

2024

Lecture Notes in Computer Science

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A Cross-Domain Perspective to Clustering with Uncertainty

Pileggi

2024

Lecture Notes in Computer Science

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Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation

Osman,

Pileggi,

Yahia

2024

Applied Sciences

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Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application.

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Ontological Modelling and Social Networks: From Expert Validation to Consolidated Domains

Cited by 2 publications

References 44 publications

A Cross-Domain Perspective to Clustering with Uncertainty

A Cross-Domain Perspective to Clustering with Uncertainty

Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation

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