What are ontologies and what are they used for?

The paradigm of semantic data processing, which has been attracting increasing attention since the early 2000s, draws on the philosophical approach of ontology, which was established by Parmenides around 500 BC. With this approach, reality can be described both abstractly and concretely. The tradition of ontological "modelling" was extended in modern times by mathematical forms of description, such as horn logic. With the logical programming languages developed in the 70s and 90s, such as Prolog and F-Logic, it is possible to describe complex relationships - as the philosophers did - and to use them as a form of artificial intelligence. Thus, new knowledge is created on the basis of complex connections - the so-called semantic AI.

What makes OntoBroker particularly performant?

OntoBroker is completely implemented in Java and therefore has a very high performance. This is Further ehanced by the high parallelism in the resolution of the logical pathways. In this way, OntoBroker benefits directly from the presence of many cores for complex reasoning. Furthermore, several OntoBroker instances can optimize performance using different load balancing methods.

What APIs does OntoBroker have available?

OntoBroker has one of its greatest strengths in connectivity to the outside world. The Java API makes almost all internal structures easily accessible and is well documented.

Furthermore, OntoBroker has both a SOAP and a SPARQL server available.

An extensive library of connectors as well as a hot-plugin interface for external programs complete the API offering.

What are the differences between ontology modeling approaches: Higher-Order Logic (HOL) versus Description Logic (DL)?

Ontologies are mainly known from the Semantic Web. The Semantic Web uses the Web Ontology Language (OWL), which is based on Description Logic (DL). The primary purpose of DL is the modeling of domains, i.e. the description of concepts, roles and instances, and represents a subset of the First-Order Logic. An essential feature of DL is that it follows the Open-World Assumption (OWA). The OWA is based on the assumption that an observer does not have complete knowledge of the world and therefore there are three logical states: true, false and unknown. This approach makes sense for a flexible and open information pool like the Internet. For industrial applications (e.g. SQL), however, the Close-Word Assumption has proven to be the only viable way to have a complete overview of information for non-chaotic processes. For example, an airline must be able to rely on the fact that a passenger has not checked in if a seat has been assigned to him and this seat is empty. This remains open for reasoning with OWL. The approach commonly used to remedy this situation with downstream so-called "closure axioms" (e.g. "man is not woman") would explode the modeling effort for the exemplary unoccupied seat.

Logic languages like Prolog, DataLog and especially the ObjectLogic used here are, in contrast to DL, complete programming languages with which you can not only model domains descriptively in their fixed factual contexts but also functionally. This means that, depending on internal or external states, fact relationships can also change, i.e. ontology functions can change the ontology, including the functions. This non-monotonous property of ObjectLogic is an essential prerequisite for implementing semantic AI.

Another important criterion of a logic language is its expressivity and thus the economy of its use. HOL allows the formulation of quantities of quantities. Reasoning" over the various quantity levels makes HOL extremely expressive.


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