Impact of Clustering Parameters on the Efficiency of the Knowledge Mining Process in Rule-based Knowledge Bases

In this work the subject of the application of clustering as a knowledge extraction method from real-world data is discussed. The authors analyze an influence of different clustering parameters on the quality of the created structure of rules clusters and the efficiency of the knowledge mining process for rules / rules clusters. The goal of the experiments was to measure the impact of clustering parameters on the efficiency of the knowledge mining process in rulebased knowledge bases denoted by the size of the created clusters or the size of the representatives. Some parameters guarantee to produce shorter/longer representatives of the created rules clusters as well as smaller/greater clusters sizes

Backward chaining inference as a database stored procedure – the experiments on real-world knowledge bases

In this work, two approaches of backward chaining inference implementation were compared. The first approach uses a classical, goal-driven inference running on the client device – the algorithm implemented within the KBExpertLib library was used. Inference was performed on a rule base buffered in memory structures. The second approach involves implementing inference as a stored procedure, run in the environment of the database server – an original, previously not published algorithm was introduced. Experiments were conducted on real-world knowledge bases with a relatively large number of rules. Experiments were prepared so that one could evaluate the pessimistic complexity of the inference algorithm. This work also includes a detailed description of the classical backward inference algorithm – the outline of the algorithm is presented as a block diagram and in the form of pseudo-code. Moreover, a recursive version of backward chaining is discussed

Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results