Demonstrating Online Schema Alignment in Decentralized Knowledge Graphs Querying

TL;DR

This paper presents an online schema alignment method for decentralized knowledge graph querying that dynamically discovers and applies alignment rules during query execution, improving result completeness with low overhead.

Contribution

It introduces a novel online schema alignment approach for LTQP that operates dynamically at runtime, enhancing decentralized query accuracy.

Findings

Online schema alignment recovers complete query results.

The approach maintains low overhead during query processing.

Demonstration on a social-media scenario validates effectiveness.

Abstract

Decentralized Knowledge Graphs querying enables integrating distributed data without centralization, but is highly sensitive to vocabulary heterogeneity. Query issuers cannot realistically anticipate all vocabulary mismatches, especially when alignment rules are local, scoped, or discovered at runtime. We present an online schema alignment approach for Link Traversal Query Processing (LTQP) that discovers, scopes, and applies alignment rules dynamically during query execution while preserving traversal behavior. This demo paper demonstrates the approach on a decentralized social-media scenario through a web interface built on a Comunica-based LTQP engine. Source code, a CLI, and a reusable library are publicly available. The demonstration shows that online schema alignment recovers complete query results with low overhead, providing a practical foundation for web-scale reasoning in LTQP systems.