The first High-Level Information Fusion (HLIF) competition took place in 2024, proposing a challenge for supporting aircraft pilots handling “Notices to Air Missions”. Technical difficulties on both the input datasets and the competition architecture raised the bar to submissions. Only two solutions taking the form of self-contained software were submitted, and were compared with two references produced by the organizers. A detailed analysis shows that some misunderstanding of the underlying use-case objectives had a big impact on the choices made by the participants. Those misunderstandings were due to a lack of precisions in the problem descriptions, along with differences in contestants' backgrounds. Consequently, the competition cannot rank the available solutions. However, our analysis highlights some interesting epistemological aspects of such a setting, related to the design and use of ontologies, knowledge graphs and queries. This allows us to propose a set of advices for improving future occurrences of an HLIF competition, on the same use-case or on another one.

Large-scale high-level information fusion and data integration is a pressing need in several scientific domains. Recently, the biomedical community established BioCypher, a tool to help create large Semantic Knowledge Graphs (SKGs) in a simple and reproducible way. In this article, we introduce OntoWeaver, a companion tool to BioCypher that allows to easily extract tabular data into SKGs by using a simple declarative mapping. OntoWeaver allows implementing reproducible mappings from tabular data to SKGs with a simple declarative configuration. The use of OntoWeaver and BioCypher is demonstrated in two different use cases: cancer database integration and invasive species monitoring. We believe that OntoWeaver and BioCypher, both free and open-source software, can help several scientific communities working on SKGs and high-level information fusion problems.