Language, Data and Knowledge (LDK) aims at bringing together researchers from across disciplines concerned with the acquisition, curation and use of language data in the context of data science and knowledge-based applications. With the advent of the Web and digital technologies, an ever increasing amount of language data is now available across application areas and industry sectors, including social media, digital archives, company records, etc. The efficient and meaningful exploitation of this data in scientific and commercial innovation is at the core of data science research, employing NLP and machine learning methods as well as semantic technologies based on knowledge graphs
Language data is of increasing importance to machine learning-based approaches in NLP, Linked Data and Semantic Web research and applications that depend on linguistic and semantic annotation with lexical, terminological and ontological resources, manual alignment across language or other human-assigned labels. The acquisition, provenance, representation, maintenance, usability, quality as well as legal, organizational and infrastructure aspects of language data are therefore rapidly becoming major areas of research that are at the focus of the conference.
Knowledge graphs is an active field of research concerned with the extraction, integration, maintenance and use of semantic representations of language data in combination with semantically or otherwise structured data, numerical data and multimodal data among others. Knowledge graph research builds on the exploitation and extension of lexical, terminological and ontological resources, information and knowledge extraction, entity linking, ontology learning, ontology alignment, semantic text similarity, Linked Data and other Semantic Web technologies. The construction and use of knowledge graphs from language data, possibly and ideally in the context of other types of data, is a further specific focus of the conference.
A further focus of the conference is the combined use and exploitation of language data and knowledge graphs in data science-based approaches to use cases in industry, including biomedical applications, as well as use cases in humanities and social sciences.