used terms i.e., terms used in industrial standards, and namely, rock types such as rhyolite or harzburgite, and formalizing the basic set of axioms. We use OWL as a main formalization tool enabling us, in particular, to automatically **check**
our representation for consistency.

It is only natural to start developing a theory by identifying the important terms to be later used for representing facts, e.g., knowledge about specific rock samples. Such facts are typically stored in relational databases in modern petrology, so relational databases can be used as a source of terms. We **describe the**
conversion of one such database, namely Proba [5] (*Sample* **in Russian), to a**
collection of OWL ontologies containing facts expressed using an initial set of currently undefined terms in the 2 section.

Once the terms have been identified, we proceed to their formalization, i.e.,
writing their definitions in OWL. First, it is essential to define the basic **terms,** which can be used to define all other terms. Currently available definitions are usually stored in a semi-structured form in natural language thesauri. Besides other issues, this often leads to contradictions, especially given differences between schools in petrology. We use one such thesaurus, namely the **Glossary**
of Igneous Rocks [7], to define petrological terms and relationships **in an OWL**
ontology. In addition, we develop a webProtege-based tool to enable domain experts to work collaboratively on term definitions, in particular, to agree upon them. See the 3 section for details.

Finally, we complement the ontology by using another rich source of term definitions - internationally adopted scientific recommendations describing rock sample classification methodologies, e.g. Igneous Rocks: A Classification and Glossary of Terms **[10]. The 4 section describes an approach to extracting definitions from the standard and expressing them as OWL axioms. As it stands,**
OWL 2 is insufficient for a complete capture of terms semantics (as specified in the standard), but this would be possible if *path free linear equations* **were**
adopted.1 **We conclude the paper by summarizing our experience from the described work and outlining plans for the future.**

## 2 Formalizing Facts: From Database To Owl

A considerable amount of important information is saved in databases, but in the form of data, which, unfortunately, is not a knowledge and requires an essential and laborious processing to obtain knowledge. This section describes a direct way of getting knowledge from the data: database conversion to the traditional form of knowledge, i.e. knowledge in a natural language. The natural language is limited to CNL to make this knowledge machine processable. We follow T.

Kuhn: CNLs are subsets of natural languages that are restricted in a way that allows their automatic translation into formal logic. **p.5 [9]. We consider CNL as**
a universal tool for representing a formal ontological knowledge.

1 **The details of the proposed extensions are available at**
http://www.w3.org/TR/owl2-dr-linear/**. Our work on petrology may be**
viewed as a use case for supporting linear equations in future OWL versions.