1 citations · 2 across the 6 of their papers we have counts for
7 papers · 1 filter
Discovering Locally Maximal Bipartite Subgraphs
Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally…
Orometric Methods in Bounded Metric Data
Maximilian Stubbemann, Tom Hanika, Gerd Stumme
A large amount of data accommodated in knowledge graphs (KG) is actually metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like ci…
Discovering Implicational Knowledge in Wikidata
Tom Hanika, Maximilian Marx, Gerd Stumme
Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs o…
Relevant Attributes in Formal Contexts
Tom Hanika, Maren Koyda, Gerd Stumme
Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task. There are various approaches to deal with this, e.g., random s…
Formal Context Generation using Dirichlet Distributions
Maximilian Felde, Tom Hanika
We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant way to generate formal contexts, a…
Probably approximately correct learning of Horn envelopes from queries
Daniel Borchmann, Tom Hanika, Sergei Obiedkov
We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attrib…