Multidimensional knowledge representation of text analytics results in knowledge bases
In the age of digitization, intelligent systems have to cope with an ever growing amount of data. Therefore, knowledge representation plays a key role for applications to handle continuously created data and to enable an access on flexible and extensively well described data structures. This paper introduces a knowledge base design which has the capability of dimensional structuring of semantically related data and explains how text analytic results can be integrated into a knowledge base. The paper discusses the main advantages of this design and shows how the data can be arranged in the knowledge base. The multidimensional structure of the knowledge base helps to resolve one of the main challenges of knowledge discovery which is the extraction of meaningful information from data in a context.
2016 IEEE International Conference on Electro Information Technology (EIT)