Event sequences appear widely in domains such as medicine, finance, and remote sensing, yet modeling them is challenging due to their heterogeneity: sequences often contain multiple event types with diverse structures—for example, electronic health records that mix discrete events like medical procedures with continuous lab measurements. Existing approaches either tokenize all entries, violating natural inductive biases, or ignore parts of the data to enforce a consistent structure. In this work, we propose a simple yet powerful Marked Temporal Point Process (MTPP) framework for modeling event sequences with flexible structure, using a single unified model. Our approach employs a single autoregressive transformer with discrete and continuous prediction heads, capable of modeling variable-length, mixed-type event sequences. The continuous head leverages an expressive normalizing flow to model continuous event attributes, avoiding the numerical integration required for inter-event times in most competing methods. Empirically, our model excels on both discrete-only and mixed-type sequences, improving prediction quality and enabling interpretable uncertainty quantification. We make our code public at https://github.com/czi-ai/FlexTPP.

Knowledge graphs and ontologies play an essential role in integrating, standardizing, and reasoning about complex data across domains. In recent studies, leveraging knowledge graphs in AI use cases, instead of traditional relational databases, led to quality improvements by up to 38 percentage points. However, learning ontologies from relational databases remains a challenging task due to the impedance mismatch between both modeling concepts. An understanding of which ontology learning system performs best, and why, is missing, as no established benchmark exists. We present BURR, a benchmark for evaluating ontology learning systems from relational databases. To evaluate the ontology learning space, we introduce a novel mapping-based metric and provide a comprehensive benchmark data collection. This collection of 54 scenarios consists of real-world database-ontology mappings, including industry data, and of a micro-benchmark evaluating the behavior of systems in encapsulated scenarios. We demonstrate the applicability of BURR by evaluating widely used ontology learning systems, including traditional rule-based as well as LLM-based approaches, on the benchmark. The results emphasize the current strengths of simple rule-based approaches compared to LLM-based systems, while also highlighting the significant research potential of LLMs in ontology learning.

"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it. Duplicates - multiple but different representations of the same real-world entities - are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential. Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results. To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform. It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results. Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.