Human-Guided Reinforcement Learning for Knowledge Graph Maintenance
Résumé
Knowledge Graphs (KGs) are widely used for integrating and reasoning over heterogeneous data in enterprises as well as in modern scientific domains. As KGs evolve, maintaining them to account for (i) the dynamic nature of the underlying data sources, and (ii) changing application requirements, becomes essential. While prior work has focused on updates driven by the evolution of data sources, less attention has been paid to adaptations triggered by evolving user needs. In this paper, we investigate the maintenance of RDF-based KGs built from tabular data sources in response to changes to applications requirements. Specifically, we present Hologram, a framework that assists users in specifying mappings for newly added ontology elements with minimal manual effort. The novelty of Hologram is twofold. First, it formalizes the problem of specifying mappings that populate new concepts and relationships (introduced through ontology evolution) as a task of learning Steiner Trees over a schema graph representing data source structures. Second, Hologram combines Reinforcement Learning with human feedback to guide the learning process effectively and efficiently. We conducted empirical experiments that demonstrate that Hologram produces high-quality mappings with minimal user input.
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