Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
First-order logic and automated theorem proving (2nd ed.)
First-order logic and automated theorem proving (2nd ed.)
The Unified Modeling Language user guide
The Unified Modeling Language user guide
Attempto Controlled English - Not Just Another Logic Specification Language
LOPSTR '98 Proceedings of the 8th International Workshop on Logic Programming Synthesis and Transformation
An introduction to description logics
The description logic handbook
IEEE Internet Computing
Web-Annotations for Humans and Machines
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
AceRules: executing rules in controlled natural language
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
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Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.