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.

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.

Abstract: In order to confirm their theoretical assumptions, physicists employ Monte-Carlo generators to produce millions of simulated particle collision events and compare them with the results of the detector experiments. The traditional, static analysis workflow of physicists involves creating and compiling a C++ program for each study, and loading large data files for every run of their program. To make this process more interactive and agile, we created an application that loads the data into the relational in-memory column store DBMS SAP HANA, exposes raw particle data as database views and offers an interactive web interface to explore this data. We expressed common particle physics analysis algorithms using SQL queries to benefit from the inherent scalability and parallelization of the DBMS. In this paper we compare the two approaches, i.e. manual analysis with C++ programs and interactive analysis with SAP HANA. We demonstrate the tuning of the physical database schema and the SQL queries used for the application. Moreover, we show the web-based interface that allows for interactive analysis of the simulation data generated by the EPOS Monte-Carlo generator, which is developed in conjunction with the ALICE experiment at the Large Hadron Collider (LHC), CERN. 1