This joint workshop proposes to bring together two different but closely related strands of research. On the one hand it will look at the overlap between ontologies and computational linguistics and on the other it will explore the relationship between knowledge modelling and terminologies.
A significant amount of human knowledge can be found in texts. This knowledge is encoded at the semantic, syntactic, and pragmatic levels and so to a certain degree language can be regarded as mirroring underlying cognitive structures. It is not surprising then that formal ontologies in languages such as OWL have become more and more popular both in linguistics and in automated language processing. For instance, knowledge models and ontologies are now of core interest to many NLP fields including Machine Translation, Question Answering, Text Summarization, Information Retrieval, and Word Sense Disambiguation. And at a more abstract level ontologies can also help us to model and reason about phenomena in natural language semantics. In addition they can also be used in the organisation and formalisation of linguistically relevant categories such as those used in tagsets for corpus annotation.
At the same time the fact that formal ontologies are being increasingly accessed by users with limited to no background in formal logic has led to a growing interest in developing accessible front ends that allow for easy querying and summarisation of ontologies. It has also led to work in developing natural language interfaces for authoring ontologies and evaluating their design.
In recent years there has also been a renewed interest in the linguistic aspects of accessing, extracting, representing, modelling and transferring knowledge. Numerous tools for the automatic extraction of terms, term variants, knowledge-rich contexts, definitions, semantic relations, and taxonomies from specialized corpora have been developed for a number of languages, and new theoretical approaches have emerged as potential frameworks for the study of specialized communication. However, the building of adequate knowledge models for practitioners (e.g. experts, researchers, translators, teachers etc.), on the one hand, and NLP applications (including cross-language, cross-domain, cross-device, multi-modal, multi-platform applications), on the other, still remains a challenge.