Smart Data & Knowledge Services

Topic field: Knowledge Work

In the topic field knowledge work, we are investigating how information and knowledge workers can be effectively supported in their work.

To illustrate this, one can imagine a personal knowledge assistant - or, to put it more bluntly, an "information butler" - who looks over the user's shoulder and identifies the information required in each case from the most diverse information sources, data silos or legacy systems of the company as well as its own data collections and proactively makes it available for the current problem. For example, the right task for an incoming e-mail, similar solutions for a new ticket, the correct inventory file for a request.

We use approaches such as the Semantic Desktop for the personal view and embedding in the personal infrastructure as well as Corporate Memories for the views of groups, departments, the company. These are based on semantic technologies that enable the generation of machine-understandable knowledge graphs from the multitude of different data sources (such as calendars, address books, documents, web pages, notes, file directories, databases, legacy systems). By embedding them in the work, rich contexts can be derived and used for storing data/information/knowledge and realizing knowledge-based services.

To this end, we are pursuing the following topics, among others

  • Corporate Memory & Semantic Desktop
  • Semantic Technologies, Evolutionary Knowledge Graphs, Graph Neural Networks
  • Personal knowledge management, business process-oriented knowledge management
  • Context, information needs, proactive information delivery
  • Building personal information models (PIMO; personal knowledge graph) and corporate memories (organizational knowledge graphs) from distributed, heterogeneous information sources
  • Assistance & knowledge acquisition embedded in daily work, tasks, processes
  • Hybrid recommendation systems, e.g., for multi-sensor-based personalized music recommendations
  • Case-based reasoning (CBR) and deep learning for decision support e.g. in diagnosis scenarios and architecture design processes
  • Dealing with small and large text collections using faceted and semantic search, entity recognition, clustering, word and graph embeddings, etc.
  • Digital forgetting, managed forgetting, information assessment
  • Instrumented office environments (including participation in the Smart Office Space Living Lab

Our DFKI CoMem unites our research approaches and industry solutions in one ecosystem. More information: https://comem.ai

We combine research with applications from industrial practice. For example, we have collaborations with enviaM where our DFKI CoMem is in pilot operation or pursue the Semantic Ink approach for semantification of handwritten notes with Wacom.

Recent open access publication on the topic (in German): https://digitusmagazin.de/2019/02/ein-informationsbutler-mit-talent-fuer-smarte-daten/

Selected projects:

Publications:

https://comem.ai/home/publications

Links

Overview

Contact

Dr. Heiko Maus
Phone: +49 631 20575 1110

Deutsches Forschungszentrum für
Künstliche Intelligenz GmbH (DFKI)
Smart Data & Knowledge Services
Trippstadter Str. 122
67663 Kaiserslautern
Germany

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz