Ruben Taelman

Bryan-Elliott Tam, Pieter Colpaert, Ruben Taelman

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.

Bart Bogaerts, Bas Ketsman, Younes Zeboudj et al.

Link Traversal-based Query Processing (ltqp), in which a sparql query is evaluated over a web of documents rather than a single dataset, is often seen as a theoretically interesting yet impractical technique. However, in a time where the hypercentralization of data has increasingly come under scrutiny, a decentralized Web of Data with a simple document-based interface is appealing, as it enables data publishers to control their data and access rights. While ltqp allows evaluating complex queries over such webs, it suffers from performance issues (due to the high number of documents containing data) as well as information quality concerns (due to the many sources providing such documents). In existing ltqp approaches, the burden of finding sources to query is entirely in the hands of the data consumer. In this paper, we argue that to solve these issues, data publishers should also be able to suggest sources of interest and guide the data consumer towards relevant and trustworthy data. We introduce a theoretical framework that enables such guided link traversal and study its properties. We illustrate with a theoretic example that this can improve query results and reduce the number of network requests. We evaluate our proposal experimentally on a virtual linked web with specifications and indeed observe that not just the data quality but also the efficiency of querying improves. Under consideration in Theory and Practice of Logic Programming (TPLP).

Ruben Verborgh, Ruben Taelman

Link-Traversal-Based Query Processing (LTBQP) is a technique for evaluating queries over a web of data by starting with a set of seed documents that is dynamically expanded through following hyperlinks. Compared to query evaluation over a static set of sources, LTBQP is significantly slower because of the number of needed network requests. Furthermore, there are concerns regarding relevance and trustworthiness of results, given that sources are selected dynamically. To address both issues, we propose guided LTBQP, a technique in which information about document linking structure and content policies is passed to a query processor. Thereby, the processor can prune the search tree of documents by only following relevant links, and restrict the result set to desired results by limiting which documents are considered for what kinds of content. In this exploratory paper, we describe the technique at a high level and sketch some of its applications. We argue that such guidance can make LTBQP a valuable query strategy in decentralized environments, where data is spread across documents with varying levels of user trust.