Knowledge base exchange: the case of OWL 2 QL

In this article, we define and study the problem of exchanging knowledge between a source and a target knowledge base (KB), connected through mappings. Differently from the traditional database exchange setting, which considers only the exchange of data, we are interested in exchanging implicit knowledge. As representation formalism we use Description Logics (DLs), thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. We define a general framework of KB exchange, and study the problem of translating the knowledge in the source KB according to the mappings expressed in OWL 2 QL, the profile of the standard Web Ontology Language OWL 2 based on the description logic DL-LiteR. We develop novel game- and automata-theoretic techniques, and we provide complexity results that range from NLogSpace to ExpTim

Representability in DL-Lite_R Knowledge Base Exchange

Knowledge base exchange can be considered as a generalization of data exchange in which the aim is to exchange between a source and a target connected through mappings, not only explicit knowledge, i.e., data, but also implicit knowledge in the form of axioms. Such problem has been investigated recently using Description Logics (DLs) as representation formalism, thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. In this paper we are interested in the problem of representing a given source TBox by means of a target TBox that captures at best the intensional information in the source. In previous work, results on representability have been obtained for DL-LiteRDFS , a DL corresponding to the FOL fragment of RDFS. We extend these results to the positive fragment of DLLiteR, in which, differently from DL-LiteRDFS , the assertions in the TBox and the mappings may introduce existentially implied individuals. For this we need to overcome the challenge that the chase, a key notion in data and knowledge base exchange, is not guaranteed anymore to be finite

Quantum Algorithm of Imperfect KB Self-organization. Pt II: Robotic Control with Remote Knowledge Base Exchange

The technology of knowledge base remote design of the smart fuzzy controllers with the application of the "Soft / quantum computing optimizer" toolkit software developed. The possibility of the transmission and communication the knowledge base using remote connection to the control object considered. Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies. Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk. As examples, two different models of robots described (mobile manipulator and (“cart-pole” system) inverted pendulum). A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented. The ability to connect and work with a physical model of control object without using than mathematical model demonstrated. The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers. It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge. Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA (at the first stage for the cooling system of superconducted magnets) is discussed. The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft / quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems. The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line

Cooperative agent-based software architecture for distributed simulation

This paper proposes a cooperative multiagent model using distributed object-based systems for supporting distributed virtual environment and distributed simulation technologies for military and government applications. The agent model will use the condition-event driven rule based system as the basis for representing knowledge. In this model, the updates and revision of beliefs of agents corresponds to modifying the knowledge base. These agents are reactive and respond to stimulus as well as the environment in which they are embedded. Further, these agents are smart and can learn from their actions. The distributed agent-based software architecture will enable us to realise human behaviour model environment and computer-generated forces (also called computer-generated actor (CGA)) architectures. The design of the cooperative agent-based architecture will be based on mobile agents, interactive distributed computing models, and advanced logical modes of programming. This cooperative architecture will be developed using Java based tools and distributed databases

Driving factors for cluster development - Which kind of spatial rootedness and change?

Driving factors and mechanisms for cluster development have often been investigated based on the standard cluster approach as conceptualised e.g. by Michael Porter. These studies have revealed certain insights regarding the role of local entrepreneurship, factor conditions, demand, and related industries in supporting clusters. However, such factors were analysed often from a static competitiveness perspective, and they were often seen as rooted in a region or part of an overly schematic local-global pattern. We suggest instead that driving factors of cluster development coexist at several spatial scales such as regional, national, European and global levels. We also argue that specific factors change in their importance for firms and for clusters over time, and that these changes are industry- and knowledge base specific. Relying on insights from cluster life cycle-, evolutionary- and knowledge base approaches among others we investigate changes in driving factors for cluster development and their relationship to different geographical scales. We provide some answers to these questions by comparing the environmental technology sector of Upper Austria and the New Media sector of Vienna, industries that differ in their knowledge bases and their spatial rootedness. (authors' abstract)Series: SRE - Discussion Paper