In this paper, we address the problem of mapping identifiers in non-RDF data to URIs. Virtual knowledge graphs (VKGs), where non-RDF data, such as relational databases, CSV files, etc., are published as RDF data, allowing users to access them using a standard query language (SPARQL), has been gaining much attention to integrating heterogeneous data. There have been several VKG systems, but there has been a problem of assigning an appropriate URI to an entity included in a record, and existing systems only support simple methods to generate a URI by adding a URI prefix to the ID value in a record. However, in practice, more complex mappings are needed to meet the demands of real applications. To address this problem, we proposed to extend the relation-to-RDF mapping rules where users are allowed to specify how entities in relations are mapped to URIs in terms of a user-defined URI function. More precisely, we integrate this method into our relation-to-RDF mapping framework. We conduct a set of experiments to assess the feasibility of the proposed method.

Learning control methods require a large number of iterative trainings. Therefore, it is requested that the system makes full use of the information which the training process presents. The authors have developed a new learning control algorithm to self-organize general solutions for optimal control problem families. This paper discusses the algorithm theoretically. Then numerical simulations on the optimal control of a swing robot are discussed to demonstrate the significance of the method.>