OxO2 -- A SSSOM mapping browser for logically sound crosswalks

2025-06-04

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

• 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: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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

• Improving logical soundness of ontology mappings
• Adding provenance details like author and reviewer
• Ensuring system stability for all user requests

Innovation

• Implements SSSOM for provenance tracking
• Uses Nemo Datalog for logical soundness
• Ensures performance with efficient reasoning