In product development reuse of product models and product development processes leads to considerable savings of costs and time. Due to existing IT systems supporting product development, such models and processes can be retrieved as digital artefacts. Beyond sheer retrieval the successful re-use of previous knowledge, however, requires also the intentional forgetting (IF) of aspects that do not apply to the newly targeted product. – a major challenge because IF for product development is neither dealt with in existing process models nor in corresponding IT systems.
It is our aim to extend the product development process with systematic methods for intentional forgetting such that the product developer may master the complexity intrinsic in large product models and product development processes as well as the complexity germane to the process of forgetting itself. To this aim, we will (i) systematize methods of forgetting that are now implicitly used by developers, (ii) develop expedient, consistent and sound knowledge representation methods for intentional forgetting, (iii) integrate these methods in product development processes and (iv) validate the approach and generalize it in a revised procedure model. Doing so, EVOWIPE will achieve contributions in three areas:
- Product development will be revised to include methods of intentional forgetting in a systematic and useful manner.
- Ontology-based knowledge bases will be provided with novel IF operators that allow for (i) forgetting inferred knowledge including (ii) the remembering of forgetting actions, (iii) temporary forgetting, (iv) representation of product development gaps, and (v) cascading of forgetting operations.
- The interdisciplinary collaboration between the two institutes will allow for integratingand validating novel IF operators in the product development process and for its generalization for future product development applications.
January 2017 - December 2019
Source of funding:
DFG - Deutsche Forschungsgemeinschaft, Priority Research Programme "Intentional Forgetting in Organisations"
- University Erlangen-Nürnberg, Lehrstuhl für Konstruktionstechnik http://www.mfk.uni-erlangen.
de/ (Prof. Dr. Sandro Wartzack)
I have studied computer science and computational linguistics at the Universität Erlangen-Nürnberg and at the University of Pennsylvania. I worked in the previous computational linguistics research group at the Universität Freiburg and did my Ph.D. in computer science in the faculty for technology in 1998. Afterwards I joined Universität Stuttgart, Institute IAT & Fraunhofer IAO, before I moved on to the Universität Karlsruhe (now: KIT), where I progressed from project lead, over lecturer and senior lecturer and did my habilitation in 2002. In 2004 I became professor for databases and information systems at Universität Koblenz-Landau, where I founded the Institute for Web Science and Technologies (WeST) in 2009. In parallel, I hold a Chair for Web and Computer Science at University of Southampton since March 2015.
Data represent the world on our computers. While the world is very intriguing, data may be quite boring, if one does not know what they mean. I am interested in making data more meaningful to find interesting insights in the world outside.
How does meaning arise?
- One can model data and information. Conceptual models and ontologies are the foundations for knowledge networks that enable the computer to treat data in a meaningful way.
- Text and data mining as well as information extraction find meaningful patterns in data (e.g. using ontology learning of text clustering) as well as connections between data and its use in context (e.g. using smartphones). Hence, knowledge networks may be found in data.
- Humans communicate information. In order to understand what data and information means, one has to understand social interactions. In the context of social network knowledge networks become meaningful for human consumption.
- Eventually meaning is nothing that exists in the void. Data and information must be communicated to people who may use insights into data and information. Interaction between humans and computers must happen in a way that matches the meaning of data and information.
The World Wide Web is the largest information construct made by mankind to convey meaningful data. Web Science is the discipline that considers how networks of people and knowledge in the Web arise, how humans deal with it and which consequences this has for all of us. The Web is a meaning machine that I want do understand by my research.
Where else you might find me?
Ich bin seit Oktober 2016 wissenschaftliche Mitarbeiterin bei WeST. Zur Zeit arbeite ich im DFG-geförderten Forschungsprojekt CoRg. Ziel dieses Projektes ist es, ein System zum Cognitive Computing zu erstellen. Dafür werden unter anderem Aspekte menschlichen Schließens wie Emotionen und zwischenmenschliche Interaktionen modelliert und klassische Logik mit normativem Schließen und Techniken des maschinellen Lernens kombiniert.
Außerdem bin arbeite ich im DFG geförderten Projekt EVOWIPE mit, in dem wir Methoden entwickeln, um Teile einer Ontologie bewusst zu vergessen.
Meine Forschungsinteressen liegen im Bereich der künstlichen Intelligenz insbesondere Commonsense Reasoning, dem Semantic Web und der Logik (insbesondere Beschreibungslogiken).
Vor meinem Eintritt in das Institut für Web Science and Technologies war ich Mitglied der Arbeitsgruppe Künstliche Intelligenz an der Universität Koblenz-Landau. In meiner Dissertation habe ich Methoden zur Veränderung der Instanzebene von beschreibungslogischen Wissensbasen entwickelt und Präkompilationstechniken für beschreibungslogische Wissensbasen untersucht.
Hier geht es zu meiner Homepage.
Liste meiner Publikationen bei WeST siehe unten. Weitere Publikationen z.B. unter http://dblp.uni-trier.de/pers/hd/s/Schon:Claudia.