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Principal Investigator:
Research Team:


Maximilian Mackeprang       01.02.2017 - 31.03.2020

Michael Tebbe                      07.06.2019 - 31.03.2020


Abderrahmane Khiat            11.04.2017 - 28.02.2019

Student researcher:

Lotte Miehle                         01.06.2017 - 08.06.2018

Maria Sigal                          15.09.2017 - 31.03.2018

Prashant Dangwal               03.09.2018 - 30.08.2019

Phil Wernberger                  02.07.2018 - 31.03.2020

Thomas Hadler                   15.04.2018 - 31.03.2020

Luka Stärk                          10.09.2018 - 30.03.2020

Project partners:

Fraunhofer Institute for Responsible Research and Innovation

Leibniz Institute for Knowledge Media


Federal Ministry of Education and Research (BMBF) [project number 03IO1617]

Nov 01, 2016 — Mar 31, 2020


One of the main processes in innovation is the generation of ideas for new applications of technologies. This process can be implemented using collaborative ideation platforms (i.e. online large-scale platforms for generating ideas), which has shown remarkable success so far: Leveraging the crowd allows the generation of a large number of ideas and the heterogeneity of the crowd increases the potential for high diversity of ideas due to participants' different backgrounds. However, finding valuable ideas has proven challenging: Related work on open innovation challenges found out that having the users generate ideas without some form of inspiration leads to mundane and repetitive ideas. Furthermore the large amount of ideas makes it unfeasible to check every idea manually.

At the Human-Centered-Computing lab, we research how to address these challenges by leveraging semantic technologies and human-computer collaboration. Our approach is based on three key steps:

Understanding users' ideas

We obtain information about the users' ideas, by extracting entities from the submitted idea text and link them to existing knowledge bases (e.g. Wikidata and Dbpedia, using SPARQL) [1]. By using an interactive validation approach, we get insights about the ideas without distracting the ideators from their main task of generating ideas.

Improving the ideation outcome for each individual user

Related work has shown that providing exemplars (inspiring ideas of others) greatly improves the idea generation outcome in terms of diversity and novelty of the generated ideas. We carry out research on constructing a semantic model (i.e. extracting super-classes, calculating conceptual similarity, finding relationships between ideas) and using it to chose effective inspirations for the ideators [2].

Improving the understandability of idea generation results

The availability of semantic knowledge about the ideas will enable us to present the outcome of an idea generation result in various visualizations. For example, calculating the conceptual similarity between ideas allows us to build a two-dimensional representation of all ideas, the so-called 'solution map'. This solution map can be used to get a quick overview over group efforts and to detect idea clusters.

By building a semantic model of the ideas generated, we are also taking additional steps towards our greater vision: Building an interactive system that, based on an idea-based knowledge graph, actively collaborates with the user to generate new and valuable ideas (so-called Human-Computer Co-Creativity). This visionary system could provide automatic recombination of ideas, adaptive system behavior based on the users mental state or customized inspiration provision based on idea relationships.


Master Theses

Kern, Kim: Ideator Types in Electronic Brainstorming

Bork, Konstantin: Investigating Idea Quality in Crowdsourced Ideation when using Exemplars Based on People's Curiosity

Stauss, Maximilian: Evaluation of Mixed Initiative Concept Annotation

Bachelor Theses

Moog, Dominik Schlomo: Spam-Erkennung in Crowdsourced Ideation

Nanthakumar, Gobie: A tool driven approach to Mechanical Turk user experiments

Schneider, Gerold: An adversarial interface design pattern to support ideation

Tchilibou, Ingrid: A WordNet based backend for the Interactive Concept Validation (ICV)



[1] A. Khiat, M. Mackeprang, C. Müller-Birn, "OntoIdea: Ontology-based Approach for Enhancing Collaborative Ideation", in Proceedings of Semantics Conference, Amsterdam, 2017.

[2] A. Khiat, M. Mackeprang, "I-Match and OntoIdea results for OAEI 2017", in Proceedings of the 12th International Workshop on Ontology Matching co-located with the 16th International Semantic Web Conference (ISWC 2017), P. Shvaiko et al., Eds., Wien: CEUR-WS.org, pp. 135–137.

[3] A. Khiat, M. Mackeprang and C. Müller-Birn, "Semantic Annotation for Enhancing Collaborative Ideation", in Proceedings of the 13th International Conference on Semantic Systems, New York, NY: ACM, 2017, pp. 173–176.

[4] A. Breitenfeld, M. Mackeprang, M. Hong, C. Müller-Birn, "Enabling Structured Data Generation by Nontechnical Experts", in Mensch und Computer 2017 - Tagungsband: Spielend einfach interagieren, M. Burghardt et al., Eds., Regensburg: Gesellschaft für Informatik e.V., 2018, pp. 181–192.

[5] M. Mackeprang, A. Khiat and C. Müller-Birn, "Concept Validation during Collaborative Ideation and Its Effect on Ideation Outcome", in Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, pp. LBW033:1–LBW033:6.

[6] A. Khiat,  M. Mackeprang and C. Müller-Birn,  "Innovonto:  An  enhanced crowd ideation platform with semantic annotation (hallway test)", Technical Report TR-B-18-02, Freie Universität Berlin, Berlin, 2018.

[7] M. Mackeprang, J. Strama, G. Schneider, P. Kuhnz, J. J. Benjamin and C. Müller-Birn, "Kaleidoscope: An rdf-based exploratory data analysis tool for ideation outcomes", in The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings, pages 75–77, 2019.

[8] M. Mackeprang, C. Müller-Birn and M. T. Stauss, "Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction", in Proceedings of the ACM on Human-Computer Interaction, ACM New York, NY, USA, 2019, pp. 1–30.

[9] M. Mackeprang, A. Khiat, M. Stauss, T. S. Müller, C. Müller-Birn, "The Impact of Concept Representation in Interactive Concept Validation (ICV)", Freie Universität Berlin: Berlin, Rep. TR-B-19-03, 2019.

[10] M. Mackeprang, A. Khiat, C. Müller-Birn, H. Computing, "Leveraging General Knowledge Graphs in Crowd-powered Innovation", 2019.

[11] M. Mackeprang, "Understanding and Augmenting Ideation Processes", in Proceedings of the 2019 on Creativity and Cognition, New York, NY, USA: Association for Computing Machinery, pp. 640–645.

[12] M. Karahan, M. Mackeprang, and M. Tebbe, "The effects of problem formulation on idea originality and feasibility", in XXXI ISPIM INNOVATION CONFERENCE, 2020.