Agnieszka Nowak-Brzezińska

Mining Rule-based Knowledge Bases Inspired by Rough Set Theory

Rule-based knowledge bases are constantly increasing in volume, thus the knowledge stored as a set of rules is getting progressively more complex and when rules are not organized into any structure, the system is inefficient. The aim of this paper is to improve the performance of mining knowledge bases by modification of both their structure and inference algorithms, which in author’s opinion, lead to improve the efficiency of the inference process. The good performance of this approach is shown through an extensive experimental study carried out on a collection of real knowledge bases. Experiments prove that rules partition enables reducing significantly the percentage of the knowledge base analysed during the inference process. It was also proved that the form of the group’s representative plays an important role in the efficiency of the inference process.

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.

Knowledge Mining Approach for Optimization of Inference Processes in Rule Knowledge Bases

The main aim of the article is to present modifications of inference algorithms based on information extracted from large sets of rules. The conception of cluster analysis and decision units will be used for discovering knowledge from such data.

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.

Goal-Driven Inference for Web Knowledge Based System

Traditional knowledge based systems were developed as the desktop applications. Meanwhile, web applications have grown rapidly and have had significant impact on the application of such systems. In the presented work, we introduce the modified goal-driven inference algorithm which allow us to divide some parts of them into the client and server layers of the web application. Proposed approach assumes that the rule knowledge base is decomposed into the decision oriented group of rules. We argue that the knowledge base in the form of such rules group contains enough information, which allows to divide inference into the client and server side, ensuring the convenience and the effectiveness.

KBExplorator and KBExpertLib as the Tools for Building Medical Decision Support Systems

Medicine had been considered a good domain in which the concepts of rule-based decision support system could be applied. The early medical decision support systems were designed over forty years ago. Since that time, many different methods were proposed in the decision support area. Regardless of the development of different non knowledge-based methods, the rule representation and inference on the rules bases are still popular. In this paper the KBExplorator system and KBExpertLib software library are introduced in the context of medical decision support system implementation. The KBExplorator system may be considered as tool for building medical knowledge bases. This system is designed for knowledge engineers and domain experts, which are responsible for creating the knowledge base for particular problem. The software library KBExpertLib is a tool for programmers to develop software which utilize the knowledge bases designed with use of KBExplorator. The main properties and methods of practical usage of KBExplorator and KBExpertLib are described as well as experiments focused on the software effectiveness evaluation.

Exploratory Clustering and Visualization

Abstract In this work the topic of applying clustering as a knowledge extraction method from real-world data is discussed. Authors propose a two-phase cluster creation and visualization technique, which combines hierarchical and density-based algorithms 1 . What is more, authors analyze the impact of data sampling on the result of searching through such a structure. Particular attention was also given to the problem of cluster visualization. Authors review selected, two-dimensional approaches, stating their advantages and drawbacks in the context of representing complex cluster structures.

Sexuality of Disabled Athletes Depending on the Form of Locomotion.

Abstract The main purpose of this study was to determine sexuality of disabled athletes depending on the form of locomotion. The study included 170 disabled athletes, aged between 18 and 45. The entire population was divided into 3 research groups depending on the form of locomotion: moving on wheelchairs (n=52), on crutches (n=29) and unaided (n=89). The research tool was a questionnaire voluntarily and anonymously completed by the respondents of the research groups. The questionnaire was composed of a general part concerning the socio-demographic conditions, medical history, health problems, a part dedicated to physical disability as well as the Polish version of the International Index of Erectile Function (IIEF) and the Female Sexual Function Index (FSFI) evaluating sexual life. STATISTICA 10.0 for Windows was used in the statistical analysis. Subjects moving on crutches were significantly older than ones moving on wheelchairs and unaided (34.41 ±11.00 vs. 30.49 ±10.44 and 27.99 ±10.51 years, respectively) (p=0.018). Clinically significant erectile dysfunctions were most often diagnosed in athletes moving on wheelchairs (70.27%), followed by athletes moving on crutches and moving unaided (60% and 35.42%, respectively; p=0.048). Clinical sexual dysfunctions were diagnosed on a similar level among all female athletes. It was concluded that the form of locomotion may determine sexuality of disabled men. Males on wheelchair revealed the worst sexual functioning. Female athletes moving on wheelchairs, on crutches and moving unaided were comparable in the aspect of their sexual life.

Complex Decision Systems and Conflicts Analysis Problem

This paper discusses the issues related to the conflict analysis method and the rough set theory, process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The value of the rough set theory and conflict analysis applied in practical decision support systems with complex domain knowledge are expressed. The furthermore examples of decision support systems with complex domain knowledge are presented in this article. The paper proposes a new approach to the organizational structure of a multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of knowledge inconsistencies elimination will be implemented for created groups. Global decisions will be made using one of the methods for analysis of conflicts.

Density-Based Method for Clustering and Visualization of Complex Data

In this paper the topic of clustering and visualization of the data structure is discussed. Authors review currently found in literature algorithmic solutions ([3], [5]) that deal with clustering large volumes of data, focusing on their disadvantages and problems. What is more the authors introduce and analyze a density-based algorithm OPTICS (Ordering Points To Identify the Clustering Structure) as a method for clustering a real-world dataset about the functioning of transceivers of a cellular phone operator located in Poland. This algorithm is also presented as an relatively easy way for visualization of the data’s inner structure, relationships and hierarchies. The whole analysis is performed as a comparison to the well-known and described DBSCAN algorithm.