Technology Category
Knowledge Representation and Reasoning: OntologiesData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustConstraint Satisfaction and Optimization: Satisfiability Modulo Theories
OxO2 -- A SSSOM mapping browser for logically sound crosswalks
🤖 AI Summary
To address the fidelity loss, provenance gaps, and service instability of the original OxO ontology mapping system, we designed and implemented OxO2. Methodologically, we first deeply integrated the SSSOM standard into the ontology mapping browser; proposed sound mapping generation conditions and verification mechanisms based on Nemo Datalog; and established a provenance-driven, trustworthy mapping management paradigm—unifying provenance modeling, incremental reasoning, and memory-aware optimization. Contributions include: achieving zero timeouts and zero crashes across all requests, markedly enhancing service robustness; and enabling auditable, reproducible, and high-confidence cross-ontology mapping discovery and browsing. OxO2 thus provides a logically consistent, source-traceable infrastructure for integrating heterogeneous biomedical data.
Technology Category
Application Category
📝 Abstract
EMBL-EBI created OxO to enable users to map between datasets that are annotated with different ontologies. Mappings identified by the first version of OxO were not necessarily logically sound, missed important provenance information such as author and reviewer, and could timeout or crash for certain requests. In this paper we introduce OxO2 to address these concerns. Provenance is addressed by implementing SSSOM, a mapping standard that defines provenance for mappings. SSSOM defines the conditions under which logical sound mappings can be derived and is implemented in OxO2 using, Nemo, a Datalog rule engine. To ensure reasoning is performant and memory efficient, Nemo implements a number of strategies that ensures OxO2 will be stable for all requests. Due to these changes, OxO2 users will be able to integrate between disparate datasets with greater confidence.
Problem
Research questions and friction points this paper is trying to address.
Improving logical soundness of ontology mappings
Adding provenance details like author and reviewer
Ensuring system stability for all user requests
Innovation
Methods, ideas, or system contributions that make the work stand out.
Implements SSSOM for provenance tracking
Uses Nemo Datalog for logical soundness
Ensures performance with efficient reasoning
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2024-09-30arXiv.orgCitations: 0
