should lead to increased amounts of machine-processable metadata. Secondly, the KB (equipped with a CNL-based interface and a SPARQL endpoint) can be integrated with the ontology that provides the vocabulary. This is important for ensuring a consistent use of the terminology across all information **systems using the KB. The stored knowledge can be further integrated with other available**
datasets, e.g. those provided by the EarthChem consortium. 6 Centralized vocabulary**. Providing a controlled vocabulary is essential for managing the knowledge. In our case, it was most important to collect the terms used**
in the database in a single OWL ontology, and give them unambiguous definitions along with human-readable annotations. This is a substantial improvement compared to the previous situation where terms were defined informally and in multiple, often contradictory sources. The resulting system can be used both as a dictionary (for people and applications i.e., via SPARQL) and as a tool for collaborative work on terminology.

Rock classification**. The formal definitions of the terms captured in standard**
OWL are not detailed enough to support automated rock sample classification, which is one of the most important use cases in petrology. To this end, we have investigated the possibility of complementing the definitions with quantitative restrictions on their mineral composition. Such restrictions can be **defined using** linear equations, a possible extension to the current data ranges in **OWL 2.**
Similarly to databases and glossaries, the classification recommendations, namely [10], are sometimes ambiguous and incomplete as well, so their formalization requires collaboration with petrologists from the Subcommission on the Systematics of Igneous Rocks of the International Union of Geological Sciences.

However, we managed to identify some predicates and functions requiring definitions, which can be used as building blocks of a formal theory. Following the methodology described in the 4 section, we have obtained detailed definitions for two types of rock as well as for some auxiliary terms. We plan to extend this work to cover all rock types in the classification.

Our work enables answering questions like Is a current object a sample of a certain rock? by performing instance checking, a standard reasoning task in OWL. However, this can be extended to query answering to find all possible rock types for a specific sample or to find all samples of a specific type in the KB. This, however, requires reasoning with linear inequalities, which is not **supported** at large scale at the moment (some reasoners are available, e.g. RACER).

Finally, we would like to stress that our approach to formalization differs from what can be seen in many biological and chemical ontologies. They are often deep class hierarchies with numerous asserted subsumptions between class names and with relatively few definitions. We focus on providing detailed definitions (using standard OWL and linear equations) instead, and plan to rely on automated reasoners to build and maintain the hierarchy. This may enable use