Streaming classification systems must maintain robustness under the "holy trinity" of non-stationarity: concept drift, extreme class imbalance, and evolving label spaces (class-incremental learning). In these regimes, standard online learners and ensemble methods frequently suffer from catastrophic forgetting of minority concepts or lack mechanisms to accommodate newly emerging classes without retraining. In this paper, we introduce Wave-GA, an evolutionary framework that evolves a transparent linear model through cyclical, class-focused training waves. By rotating optimization pressure across classes and utilizing dynamic matrix expansion, Wave-GA integrates distinct mechanisms for drift adaptation (per-class replay buffers), imbalance handling (minority-aware blended fitness), and emerging labels (architectural growth). Extensive prequential evaluation on six synthetic benchmarks and three real-world streams demonstrates that Wave-GA prevents the minority-class collapse observed in state-of-the-art ensembles like Adaptive Random Forest. The approach improves average balanced accuracy by 6–12 percentage points over standard evolutionary baselines and delivers robust long-horizon stability with constant-time inference.

The paper introduces White Rabbit, a novel method for discovering high-quality, meaningful paths between entities in online Knowledge Graphs (KGs). Traditional exploration methods, such as SPARQL endpoints, struggle due to the large size and complexity of KGs. The proposed approach addresses this by introducing the problem of context-aware path finding, ensuring that retrieved paths are coherent and involve highly relevant entities. White Rabbit uses embeddings to score entity neighbors, a queue-based prioritization mechanism, and an iterative refinement process to improve efficiency and relevance. The system is demonstrated live, allowing participants to test it and compare against baseline methods (structural approaches, pretrained embeddings, and large language models). Results show that White Rabbit enhances both the efficiency of exploration and the quality of discovered paths.

International audience

Semantic summaries try to extract compact information from the original RDF graph, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for a specific purpose, such as indexing, query answering and source selection. In this paper, we present an approach that is able to construct high quality summaries, exploiting a small part of the query workload, maximizing their utility for query answering, i.e. the query coverage. We demonstrate our approach using two real world datasets and the corresponding query workloads and we show that we strictly dominates current state of the art in terms of query coverage.