LRE for Actionable Knowledge

Important information to support a range of applications is hidden in Big Data. Automated content analytics is needed for the interpretation of the data and their context, so that it is accurately understood and can be integrated and used in applications. Content analytics makes use of various technologies, like semantic search, keyword suggestions, clustering, classification, etc. What is the role of LRs in such correlation of digital content and context? Can for example relations between LRs and Knowledge Graphs for entity linking, disambiguation, reasoning, etc. support the generation of actionable knowledge in Big Data analytics?

More generally we would like to bring to discussion all issues related to LRs and evaluation means for semantic processing in the Big Data environment.

LRE for Interaction with Devices

There is a growing interest in adapting and improving Natural Language Processing for providing intelligent language interfaces to all kind of devices that are connected to the Internet (of Things), and also to robots, sensors and the like. We encourage investigating how to relate LRs in this communication set-up with data that are in principle of a non-linguistic nature. How to improve multilingual and multimodal generation of information from sensors, robots and in general from structured data in the Internet of Things? How can LRs optimally be designed and used in this (bi-directional) interaction? How to combine language and sensor streams in multilingual and multimodal virtual worlds?

Are there new or past approaches to Human-Machine dialogue offering easily adaptable solutions, so that we need “only” to upgrade them to the enormously increased quantity of data and number of interconnected devices?