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This paper presents an embedding of ontologies expressed in the ALC description logic into a real-valued vector space, comprising restricted existential and universal quantifiers, as well as concept negation and concept disjunction. Our main result states that an ALC ontology is satisfiable in the classical sense iff it is satisfiable by a partial faithful geometric model based on cones. The line of work to which we contribute aims to integrate knowledge representation techniques and machine learning. The new cone-model of ALC proposed in this work gives rise to conic optimization techniques for machine learning, extending previous approaches by its ability to model full ALC.

Dielectric Elastomer (DE) transducers are characterized by their geometrical dimensions and in particular by the properties of the elastomer and electrode materials. Therefore, in addition to dimensions, it is advantageous to consider optimization of material properties to fulfill transducer requirements, such as blocking force, free stroke, or response time. A big challenge in describing the properties of DE materials deals with utilizing different but commonly used hyperelastic material models and their parameters, which differ in complexity and corresponding model errors. Thus, determined material parameters are not necessarily consistent. In addition, parameters are depending on the measurement method, its conditions and the samples themselves. All of this leads to heterogeneous datasets making data access more complicated and in certain cases impossible for users. To overcome this, OBDA (ontology-based data access) approaches have been proven to access these heterogeneous datasets individually and efficiently and to gain the relevant information with the help of an ontology. Within a research project funded by the Federal Ministry of Education and Research, an extended OBDA approach is developed: OBDMA (ontology-based data and model access) combines data access with model-based working steps. While the joint project considers four different smart material classes, this paper focuses on dielectric materials and their transducers, in particular the development of methods to handle hyperelastic material models and their parameters. The various possibilities of material models and parameter identification methods are discussed on the basis of a measurement curve. Finally, the working principle and the advantages of the OBDMA system are demonstrated by means of a representative DE use case.

An agent in pursuit of a task repeatedly perceives its environment through sensors, updates its state based on observations, and then decides which action to take, given the current state of the environment. Observations have in common that they are made at a given time point and thus referred to as temporal data. Usually, such temporal data is provided as stream data if the agent continuously receives the data, or it is provided as historic data if the stream data is stored in, for instance, a database the agent has access to. DBMSs are especially designed to process static data (i.e. non-temporal data) given a declarative query language such as SQL. However, if the aim is to exploit temporal data as required in time series analysis, SQL has its limits because it does not provide useful abstractions such as a window operator. Hence high-level declarative stream query languages, equipped with time-based window operators were designed. A challenge of those abstractions is the additional overhead of the algorithms that automatically transform high-level queries into low-level queries executable over DBMSs. If not handled properly those transformation algorithms may result in low-level queries with processing times too long for agents to make decisions. We describe a robust and optimized transformation algorithm for a high-level declarative stream query language and show that it leads to low-level queries with feasible processing times on real-world data.

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