In geographic information systems (GIS), heterogenous data can be seen as amajor obstacle for a project-oriented integration. Usually data has to be inte-grated into a single system and conversion mechanisms have to be implemented.
Linked Open Data (LOD) is public data, which is published and interconnected with the Resource Description Framework (RDF) on the web. The size and interconnection of the data within the LOD cloud makes it hard to grasp all the information that is explicitly and implicitly available.
We survey a selection of inconsistency measures from the literature and investigate their computational complexity wrt. decision problems related to bounds on the inconsistency value and the functional problem of determining the actual value.
Linked Open Data can be a treasure chest for data scientists with plenty of data sources from different domains. Most of the time however, it feels more like a dusty desert, where you have to investigate multiple treasure chambers in search of something valuable. In this scenario, wouldn't it be useful to have a guide that can point you where to look?
Despite a number of frameworks, programming with Description Logics is still challenging in unique ways. Part of the problem are static type systems, that are supposed to prevent runtime errors.
In distributed RDF stores, the stored graph is partitioned and each partition is stored on a computer. While querying this distributed graph, the information stored in one partition might not be enough to produce the requested answer.
This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them.
Transfer learning refers to a class of machine learning methods concerned with transferring knowledge learned from one or more domains (the sources) to be applied to a given domain (the target).
Belief Revision and Formal Argumentation are two subfields of Knowledge Representation and Reasoning (KR) that deal with resolving inconsistencies in logic- based representations of information through dynamic processes.
One of the main goals of the artificial intelligence community is to create machines able to reason with dynamically changing knowledge.