The continuous growth of the Linked Open Data (LOD) Cloud is extending to various new domains. In many of these, facts change continuously: political landscapes evolve, medical discoveries lead to new cures, artists form new collaborations. In terms of knowledge representation, we observe that instances change their roles, new relations appear, old ones become invalid, and classes change both their definition and memberinstances.
The evolution of LOD poses new challenges to interested stakeholders: LOD publishers need to detect changes in the real world and capture them in their datasets; users and applications need automated tools to adapt querying over such diachronic datasets; knowledge engineers want to understand modelling practices behind ontology changes; philosophers study drift in the meaning of words.
Given the rapidly increasing deployment of semantic technologies in today’s Web, the impact of semantic change becomes more and more relevant, since it compromises semantic interoperability and access to digital content. Whereas the semantic web field has been concerned largely with static knowledge for a long time, there is now more and more interest in timedependent content. Event modeling, event extraction, stream reasoning, and the emergence of Web Observatories are examples of that. While the infrastructure to represent and query timestamped data is coming together, there is still a lack of knowledge about how to detect that facts and concepts have changed, how to interpret changes, and how to deliver results to users in a meaningful way.
For this workshop we invite contributions from researchers working on detecting, representing and managing concept drift in and for LOD, either as input or output for their acquisition, representation or modeling methods. The goal is to bring together different communities that define, identify and manage the dynamics of concepts in their knowledge bases using various domainspecific methods (statistical inference, symbolic reasoning, natural language processing, etc.), leveraging Linked Data as a data source or as a result publishing platform.
== TOPICS ==
Topics of interest include, but are not restricted to:
* detecting and predicting concept drift (using any method, incl. reasoning, data mining, word embeddings, and natural language processing)
* representation of concept drift
* reasoning, querying, machine learning in the presence of drifting concepts
* theoretical explanations of drift dynamics
* empirical studies of how concepts drift
* theoretical and practical investigations of semantic change
* frameworks addressing concept identity over time
== IMPORTANT DATES ==
Paper submission: September 15th, 2016
Notification: October 6th, 2016
Workshop dates: November 19th or 20th, 2016
== SUBMISSION GUIDELINES ==
Papers should not exceed 8 pages, in PDF, formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). For details on the LNCS style, see Springer's Author Instructions at http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0. Shorter papers describing significant work in progress, late breaking results, or lessons learned, are also welcome. Contributions will be accepted for either an oral presentation or as a poster. Accepted contributions will be published on the CEUR-WS website (or equivalent).
== WORKSHOP ORGANIZERS (alphabetically) ==
Sándor Darányi, University of Borås, Sweden
Laura Hollink, Centrum Wiskunde & Informatica, The Netherlands
Albert Meroño Peñuela, Vrije Universiteit Amsterdam, The Netherlands
Efstratios Kontopoulos, Center for Research & Technology Hellas, Greece