Beyond Observed Topology: Semantic Graph Inference and Alignment for Text-Attributed Graph Learning

Text-attributed graph (TAG) learning jointly models textual semantics and graph topology, typically assuming that observed edges reflect the underlying semantic structure. However, this assumption may not always hold, as connected nodes can be semantically unrelated, while semantically related nodes may lack observed connections, resulting in structural–semantic mismatch. Recent studies have explored incorporating textual semantics into graph structure, either by building upon the observed topology or by using LLMs to infer semantic relations at the edge level. While the latter offers greater flexibility, it incurs additional inference costs and limits interpretability.To address these limitations, we propose SEGIA, a SEmantic Graph Inference and Alignment approach for text-attributed graph learning. Specifically, we first introduce an uncertainty-aware semantic encoding mechanism inspired by the stochastic block model to uncover latent connectivity patterns among nodes from their textual information, yielding a semantic graph.Then, we separately encode the observed and semantic graphs to characterize their respective structural information and derive view-specific predictions. Finally, we employ optimal transport to align these predictive distributions, enabling effective information integration across the two views while enforcing prediction consistency. Extensive experiments on five public TAG benchmarks demonstrate that our proposed SEGIA outperforms state of-the-art baselines.