A Bioinspired Algorithm for Improving the Effectiveness of Knowledge Processing

The paper deals with an approach to improve the effectiveness of knowledge processing in terms of large dimensions. The authors suggest the model of classification of the information resources to be used as a preprocessing stage for their further integration. The amount of information produced, transferred and processed by people and various technical devices rapidly grow every year, so the problem of improving the knowledge processing efficiency is very important these days. Ontological structures as used in this work to represent knowledge of the information systems, because they allow us to consider the semantics of the processed knowledge. The authors propose performing the classification using two components of semantic similarity between the objects of the ontologies. They are equivalent and horizontal semantic similarity components. To solve the classification task, we use bioinspired algorithms since they are proven to be effective in terms of solving the optimization problems of large dimensions. One of the suggested bioinspired algorithms developed for solving the mentioned tasks is the bacteria optimization algorithm. The paper describes the algorithm and provides the results of its work. The experiments show that the bacteria algorithm gives effective results with polynomial time complexity.