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.

翻译：去中心化知识图谱查询可在无中心化的前提下实现分布式数据集成，但极易受到词汇异构性的影响。查询发起者难以预测所有词汇不匹配情况，尤其是当对齐规则具有局部性、作用域限定或需在运行时发现时。我们提出一种面向链接遍历查询处理（LTQP）的在线模式对齐方法：该方法能在查询执行过程中动态发现、限定作用域并应用对齐规则，同时保持遍历行为不变。本演示论文通过基于Comunica LTQP引擎构建的Web界面，在去中心化社交媒体场景中展示了该方法的实现。相关源代码、命令行工具（CLI）及可复用库均已公开。演示表明，在线模式对齐能以低开销恢复完整的查询结果，为LTQP系统的网络规模推理提供了实用基础。