SensAI

Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories
 
01.08.2020 – 31.07.2023

grant no. 01IW20007

About the Project


Potentials

Knowledge Graphs (KGs) get an increased interest in research and industry to represent data, information and knowledge from various sources in ontology-based graph structures. In addition, research in Deep Learning (DL) addresses challenges in graph networks to incorporate knowledge into DL approaches with first promising results in the area of Graph Neural Networks. To utilize emerging potentials, SensAI combines both technologies and investigates how to incorporate knowledge into Deep Learning approaches in order to address challenges arising from real-world application scenarios as well as using it to tackle emerging contexts from knowledge work scenarios. These scenarios originate from our corporate memory approach when introduced to a company and used in daily work. Although we mostly speak of companies, they are applicable to any institutional setting: educational, governmental, agricultural, etc. Our approach is a human-centered assistance for knowledge work using personal KGs for representing the user’s mental model as well as their work environment. This is combined with enterprise KGs representing the information space of the company, leveraging various heterogeneous data silos to interconnected, machine understandable information and enabling various knowledge services.

Challenges

Challenges arise from the usually messy, sparse and diverse data sets, the effort for building and maintaining the evolving KGs using this heterogeneous data. Besides these company-driven challenges, our human-centered approach to engage users and respecting their personal views onto the company’s information space, requires dealing with their personal data and interactions, assistance in the evolving complexity of the corporate memory and the need to explain proposals from knowledge services.

Vision

Therefore, our vision for SensAI is a self-adapting personal assistant which is embedded in a user’s work environment and is part of and contributes to a corporate memory. To reach this vision, we will pursue three goals: Pursuing these goals, SensAI combines the department’s experience in corporate memories with our insights of Deep Learning approaches and architectures to research a novel approach to human-centered assistance in knowledge work scenarios.
 
also see:
SensAI DFKI website
Short project summary (in German)

Selected Works and Projects SensAI is based on


News

Jan. 24th, 2022

Markus Schröder and Christian Jilek gave a lecture on SensAI topics at TU Kaiserslautern as part of the lecture series on "Applications of Machine Learning and Data Science".

Dec. 3rd, 2021

SensAI paper "Spread2RML" (see Publications) received K-CAP 2021 best paper award.

Jan. 11th, 2021

SensAI paper "The Person Index Challenge" (see Publications) nominated for best poster at ICAART 2021.

Showcase


Team

The following people contribute (or have contributed) to the project:

Christian Jilek

Project Lead

Desiree Heim
Junior Researcher

Mahta Bakhshizadeh
PhD Student

Rudolf Koch
Software Engineer

Dr. Heiko Maus

Associated Researcher

Dr. Jörn Hees

Associated Researcher

Dr. Sven Schwarz

Associated Researcher

Johannes Bayer
Researcher

Tirtha Chanda
Researcher

Jessica Chwalek
Hiwi

Publications


2022

2021

2020


Ontologies, Datasets, Tools, etc.


Here are ontologies, datasets, dataset generators, tools, tutorials and other documents created in SensAI:

Ontologies

Datasets

Dataset Generators

Tools

Tutorials & Further Reading