Authors:
Fabian Ohler
;
Kai Schwarz
;
Karl-Heinz Krempels
and
Christoph Terwelp
Affiliation:
RWTH Aachen University, Germany
Keyword(s):
Rule-based System, Discrimination Network, Rating.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Change Detection
;
Computing and Telecommunications in Cardiology
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Management for Analytics
;
Data Structures and Data Management Algorithms
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Informatics in Control, Automation and Robotics
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
OLAP and MDA Models
;
Predictive Modeling
;
Signal Processing, Sensors, Systems Modeling and Control
;
Symbolic Systems
Abstract:
The amount of information stored in a digital form grows on a daily basis but is mostly only understandable by humans, not machines. A way to enable machines to understand this information is using a representation suitable for further processing, e. g. frames for fact declaration in a Rule-based System. Rule-based Systems heavily rely on Discrimination Networks to store intermediate results to speed up the rule processing cycles. As these Discrimination Networks have a very complex structure it is important to be able to optimize them or to choose one out of many Discrimination Networks based on its structural efficiency. Therefore, we present a rating mechanism for Discrimination Networks structures and their efficiencies. The ratings are based on a normalised representation of Discrimination Network structures and change frequency estimations of the facts in the working memory and are used for comparison of different Discrimination Networks regarding processing costs.