Tomasz Jach

Towards a practical approach to discover internal dependencies in rule-based knowledge bases

In this paper, we intend to introduce the conception of discovering the knowledge about rules saved in large rule-based knowledge bases, both generated automatically and acquired from human experts in the classical way. This paper presents a preliminary study of a new project in which we are going to join the two approaches: the hierarchical decomposition of large rule bases using cluster analysis and the decision units conception. Our goal is to discover useful, potentially implicit and directly unreadable information from large rule sets.

Inference Processes Using Incomplete Knowledge in Decision Support Systems – Chosen Aspects

The authors propose to use cluster analysis techniques (particularly clustering) to speed-up the process of finding rules to be activated in complex decision support systems with incomplete knowledge. The authors also wish to inference within such decision support systems using rules, of which premises are not fully covered by the facts. The AHC or mAHC algorithm is used. The authors adapted Salton’s most promising path method with own modifications for a fast look-up of the rules.

Inference in Expert Systems Using Natural Language Processing

The authors show the real life application of an expert system using queries submitted by the user using natural language. The system is based on polish language. The two stage process (involving data preparation and the inference itself) is proposed in order to complete the inference.

The Incompleteness Factor Method as a Support of Inference in Decision Support Systems

The authors propose the incompleteness factor (IF) method to improve the effectiveness of browsing in knowledge bases with missing data. The paper explains the whole method, which is based on certainty factors and cluster analysis. The experiments’ results conducted to obtain optimal parameters for the algorithm are presented. The evaluation is made by using recall, precision, F-measure and other factors.

WaterSocial.org as the social platform aimed to promote an efficient water use

ISS-EWATUS is a EU founded project which aims to increase the effectiveness of water saving awareness. One of its key components is the social media platform, along with connectors and information exchanging throughout whole system. In this paper, the brief description of this platform is stated, as well as the dissemination results of a whole ISS-EWATUS project.

Tips Service and Water Diary An Innovative Decision Support System for the Efficient Water Usage at Households

In the paper, we present the tips service and water diary, which are the modules of the mobile decision support system (DSS) for the efficient water usage at households. This system is a part of the Integrated Support System for Efficient Water Usage and resources management (ISS-EWATUS). The DSS at the household level is aimed at provoking the usage of the water more effective. Therefore, the tip service was designed and implemented. Tips are generated for each household as a result of the analysis of the previously gathered data concerning water consumption. In order to enhance and to personalise the influence of the DSS, the water diary was created. The water diary gives the possibility to members of the household to follow to identify their own water consumption measurement separately. Additionally, the water consumption data are presented to them as charts in the various views. The DSS was validated and accepted at 40 households in Poland and Greece.