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This paper is about problems in the area of the artificial intelligence (AI) of an embodied agent, i.e. a robot or any other autonomous machine, whether simulated or real. We discuss the necessity of grounding the concepts that form relations between symbols in the decision unit of such an agent. We explore existing concepts for knowledge engineering in the field of the Semantic Technologies to determine which grounded base vocabulary might be necessary, we detect that besides bodily experienced basics intelligence also deals with mental experiences and show a possible way to ground the concept for subclass as an example.

Database technologies are evaluated in respect to their performance in model extension, data integration, data access, querying and distributed data management. The structure of the data sources is partially unknown. Additional value is gained combination of data sources. Data models for a relational, a document and a graph oriented database are compared showing strengths and weaknesses of each data model.

This paper introduces an alternative method for using ontologies to create a dynamic data model for RDF databases or other schema-less databases. The main challenge is how to continuously adapt the data model and its queries to new data, which may be imported with any given structure.

This paper presents a new solution for making existing web-databases accessible for semantic agents. Instead of adding semantic annotations to a page itself or publishing the content separately, the elements of a web-page are annotated and published in an external file, maybe even on an external site. A semantic agent can use these annotations like an instruction manual to find out how to interpret the page's content and how to access databases such as product catalogues, price lists or technical specifications for components. Since these annotations can be published separately from the actual page, anyone can annotate any website and make it accessible for semantic agents.