Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

Wehrle, Marcel; Osswald, Marc; Portmann, Edy (2016). Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps. In: Portmann, Edy; Finger, Matthias (eds.) Towards cognitive cities: advances in cognitive computing and its applications to the governance of large urban systems. 63: Vol. 63 (pp. 123-144). Switzerland: Springer International Publishing 10.1007/978-3-319-33798-2_7

Full text not available from this repository. (Request a copy)

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.

Item Type:

Book Section (Book Chapter)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems > Information Management
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems

UniBE Contributor:

Portmann, Edy

Subjects:

000 Computer science, knowledge & systems
600 Technology > 650 Management & public relations
300 Social sciences, sociology & anthropology > 330 Economics

ISSN:

2198-4182

ISBN:

978-3-319-33798-2

Series:

63

Publisher:

Springer International Publishing

Projects:

[378] Cognitive Cities
[455] Modelling with Words Official URL
[388] Knowledge Aggregation, Representation and Reasoning
[459] Big Data Analytics and Management

Language:

English

Submitter:

Sara D'Onofrio

Date Deposited:

05 Aug 2015 08:05

Last Modified:

26 Sep 2016 15:01

Publisher DOI:

10.1007/978-3-319-33798-2_7

URI:

https://boris.unibe.ch/id/eprint/70601

Actions (login required)

Edit item Edit item
Provide Feedback