A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology

2024-08-02

Scientific Data

AI Summary

This work addresses the semantic fragmentation and poor interoperability between PROV-O (Provenance Ontology) and BFO (Basic Formal Ontology). We propose a semantics- and logic-driven ontology mapping methodology that formally specifies alignment criteria, supports consistency checking via OWL reasoning and SPARQL-based validation, and adheres strictly to FAIR principles in ontology engineering. Our approach yields the first verifiable, formal alignment set bridging PROV-O and BFO. Empirical evaluation on canonical PROV-O instances confirms both logical consistency and semantic fidelity of the alignment. The resulting mappings significantly enhance the interpretability and interoperability of provenance data within the BFO top-level framework, enabling principled cross-domain provenance knowledge integration. This contribution provides both a reusable methodological foundation and foundational infrastructure for ontology-based provenance interoperability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Problem

• Mapping Provenance Ontology to Basic Formal Ontology
• Enhancing interoperability between PROV-O and BFO
• Evaluating alignments using semantic and logical principles

Innovation

• Mapping PROV-O to BFO using semantic principles
• Evaluating alignments with PROV-O instances and SPARQL
• Leveraging semantic web tech for FAIR compliance