## 1 Introduction
However, the presented ontologies in the domain of tribology do not overcome the lack of comparability and shared understanding of experimental results from distributed sources. Within other experimental-intensive domains, like bioinformatics, it is common and successful practice to use ontologies for annotating experimental results for reuse and sharing knowledge from different sources. For instance, the Gene Ontology (GO) [20] provides structured controlled vocabulary for enabling interoperability between gene databases as well as standardizing experimental results by supplying vocabulary for describing function, process, and molecular components. Moreover, the Experimental Factor Ontology (EFO) [21] is an application ontology for describing experimental variables and providing interoperability between different ontologies in the domain of bioinformatics. As a shared understanding of biological knowledge involves the commitment of participants of that domain, ontologies are organized by the Open Biological and Biomedical Ontology (OBO) Foundry ([[http://www.obofoundry.org/](http://www.obofoundry.org/)]([http://www.obofoundry.org/](http://www.obofoundry.org/))), which is a collective of ontology developers for committing to shared principles in ontology design. The successful use of ontologies in bioinformatics, especially the GO, has led to the participation of many interest groups, enabling the comparison of experimental results as well as computational analysis through the shared common knowledge base. Besides domain-specific approaches, [PERSON] and [PERSON] [22] presented a more general approach for providing a formal description of experimental models and results. The ontology of scientific experiments (EXPO) is introduced as an intermediate-level ontology, which specifies the upper ontology SUMO (Suggested Upper Merged Ontology) [23] and generalizes experimental design and result documentation independent from the domain. Using an upper ontology, which provides quite general concepts and relations, is a best practice in ontology modeling as it maximizes scheme compatibility and reasoning interoperability. Therefore, the EXPO consists of very general concepts from SUMO, which are specialized with concepts for annotating and sharing experimental results of different domains, like high-energy physics and phylogenetic investigations [22]. Through the specification of concepts from EXPO, the intermediate-level ontology can be extended by domain-specific ontologies. The existence of successfully applied ontologies in experimental-intensive domains like bioinformatics was driven by the lack of uniformity in the terminology and formats, shareability, and comparability of experimental results. Therefore, the formalization and unification of scientific knowledge within a domain is a necessity for avoiding misinterpretation and re-testing the same phenomena as well as for simplifying the search for the required information. The initially described practice of presenting experimental results in the natural language, the ambiguity in terminology and formats, as well as ongoing projects intending to establish shared infrastructures emphasize the need for formalization and unification of experimental knowledge in the tribological domain. The first step in formalizing knowledge is the definition of an explicit ontology, which has its strength in the homogenization of distributed and heterogeneous data sources independent from a database specification [15]. Therefore, the present contribution is devoted to the suggestion of an ontology named tribAln in order to overcome the lack of uniformity, comparability, and shareability of tribological experiments and their results. The tribAln ontology is designed as a domain ontology for use in experimental knowledge bases in the field of tribology, providing formal defined concepts supporting knowledge reuse.