Nowadays the Web is fundamental to any type of professional communication. Specialized communication today is strongly supported by knowledge management systems and machine readable knowledge objects. To meet the challenges of inter- and intra-organizational processes and demands of internationalization, knowledge representation needs to be multilingual and accessible across systems and platforms. The language industry has successfully developed and deployed tools and resources for multilingual content, such as for localization and translation management, but its support to knowledge engineering has not yet been sufficiently explored.  

Knowledge representation systems on the Semantic Web and in the Linked Open Data (LOD) cloud have experienced a rapid growth over the last decades. They provide a common body of knowledge as a basis for informed decision-making in form of factual data. Information is expressed in a language-independent fashion using the Resource Description Framework (RDF) or the Web Ontology Language (OWL), which bears the potential of being accessible not only by machines, but also by speakers of all languages. To truly benefit from this potential the right mediation mechanism between factual and language-related knowledge needs to be in place. This would allow for semantically structured data to be queried and accessed in any language and resources to be aligned and harmonized across languages, which goes beyond technical and semantic interoperability in the direction of content interoperability

Multilingual knowledge representation is an open research area calling for interdisciplinary approaches for creating, managing, and using combined linguistic and factual information on the Web. Language resources are understood as collections of natural language data, such as terminologies, lexicons, corpora, NLP resources, annotation resources. The exposure of such language resources on the Semantic Web and in the Linked Open Data (LOD) cloud requires a collaborative effort of domain experts, knowledge engineers, software experts, and language professionals alike. The emerging Linguistic Linked Data framework is an example on how language resources can be encoded in RDF and so to be integrated with other types of data sets in the LOD cloud.

The goal of this workshop is to bring together researchers and practitioners from various disciplines interested in multilingual knowledge representation and discuss possibilities of leveraging mutual benefits of language and knowledge representation resources. This includes but is not limited to knowledge engineering, terminology engineering, ontology-based systems, lexicographic approaches, computational linguistics, natural language processing and any other context that supports multilingual knowledge representation methods.