Abstract
Machine learning and knowledge representation are two fields of artificial intelligence that lead with intelligent reasoning, each one differently. When Knowledge Representation (KR) focuses more on the epistemological face of knowledge to carry out a power-expression model with detriment of the computation efficiency. Machine learning pays more attention to the computation efficiency, often with loss of expressing power. In this paper we show that features of one may overwhelm drawbacks of the other. Taking the uncertainty artefact from machine learning and symbolic representation from KR, we propose in this paper a new memory modelling for knowledge based systems which is at the same time machine-learning structure and a knowledge representation model. In terms of machine learning, our structure allows an unlimited flexibility where no restraining architecture is imposed at the beginning (think about decision trees [18, 6, 5, 2]). Classification can be performed with incomplete vectors where the most likely corresponding class is assigned to the vector with missing attributes. Viewed as a knowledge model, a basic knowledge is easily specified graphically. Inference is defined by rules expressed in the same manner, where the existing sub-instances are used to generate new connections and entities. Inference on existing knowledge is described in two algorithms. Our approach is mainly based on the representation of the context concept. Our model brings together advantages of the symbolic knowledge representation, namely human to computer knowledge coding, and those of machine learning structures, namely ease of efficient coding of inference to perform the so-called intelligent tasks like pattern recognition, prediction and others. Its graphical representation allows visualization of both dynamic and static sides of the model (i.e. inference and knowledge).