The new biennial conference series on 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.

The LDK conference has been initiated by a consortium of researchers from the Insight Centre for Data Analytics, InfAI (University Leipzig) and Wolfgang Goethe University and a Scientific Committee of leading researchers in Natural Language Processing, Linked Data and Semantic Web, Language Resources and Digital Humanities. LDK is endorsed by several international organisations: DBpedia, ACL SIGANN, Global Wordnet Association, CLARIN and Big Data Value Association (BDVA). The first edition, LDK 2017, will be held in Galway (Ireland) with a second edition planned for 2019 in Leipzig (Germany).

Important Dates
23 February 2017 Paper submission
30 March 2017 Notification
20 April 2017 Camera-ready submission
19-20 June 2017 Conference


Paper submission

We welcome submission of both long and short papers of relevance to the topics listed below. Submissions can be in the form of long or short research papers, scientific abstracts on use cases or position papers. Accepted submissions will be published in a conference proceedings and will be selected for presentation as oral or poster presentation based on recommendations of reviewers, this choice does not reflect the quality of the work.
Topics

Language Data
Language data portals
Language data construction, acquisition and management
Crowdsourcing of language data
Metadata about language data
Multilingual, multimedia and multimodal language data
Evaluation, provenance and quality of language data
Usability, validation and visualization of language data
Organizational and infrastructural management of language data
Standards and interoperability of language data
Legal aspects of publishing language data
Typological databases
Under-resourced languages

Knowledge Graphs
Ontologies, terminology, wordnets and lexical resources
Information and knowledge extraction (taxonomy extraction, ontology learning)
Data, information and knowledge integration across languages
(Cross-lingual) Ontology Alignment
Semantic text similarity
Entity linking and relatedness
Linked Data profiling
Linguistic Linked Data
Multilingual Linked Data and multilingual Web of Data
Knowledge representation and reasoning on the Multilingual Semantic Web

Applications in NLP
Semantic search
Semantic content management
Question answering
Computer-aided Language Learning
Text analytics for Internet of Things
Multilingual Internet of Things
Applying big data to text analytics
Natural language interfaces to (big) data

Use Cases in Digital Humanities, Social Sciences, BioNLP
Applications in Digital Humanities such as distant reading
Analysis, enrichment of text archives
Text mining for Social Science research
Text mining from biomedical literature