Matthias Broecheler

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Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This(More)
This paper introduces hinge-loss Markov random fields (HL-MRFs), a new class of probabilistic graphical models particularly well-suited to large-scale structured prediction and learning. We derive HL-MRFs by unifying and then generalizing three different approaches to scalable inference in structured models: (1) randomized algorithms for MAX SAT, (2) local(More)
RDF is an increasingly important paradigm for the representation of information on the Web. As RDF databases increase in size to approach tens of millions of triples, and as sophisticated graph matching queries expressible in languages like SPARQL become increasingly important, scalability becomes an issue. To date, there is no graph-based indexing method(More)
Multiple phenomena often diffuse through a social network, sometimes in competition with one another. Product adoption and political elections are two examples where network diffusion is inherently competitive in nature. For example, individuals may choose to only select one product from a set of competing products (i.e. most people will need only one(More)
Subgraph matching is a key operation on graph data. Social network (SN) providers may want to find all subgraphs within their social network that match certain query graph patterns. Unfortunately, subgraph matching is NP-complete, making its application to massive SNs a major challenge. Past work has shown how to implement subgraph matching on a single(More)
Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear(More)
There has been extensive work in many different fields on how phenomena of interest (e.g., diseases, innovation, product adoption) “diffuse” through a social network. As social networks increasingly become a fabric of society, there is a need to make “optimal” decisions with respect to an observed model of diffusion. For example, in(More)
Declaration The research subsumed in this thesis has been conducted under the supervision of Prof. Michael Kohlhase from Jacobs University Bremen. All material presented in this Bachelor Thesis is my own, unless specifically stated. I hereby declare that this submission is my own work, and that — to the best of my knowledge — it does not contain any(More)
Users querying massive social networks or RDF databases are often not 100% certain about what they are looking for due to the complexity of the query or heterogeneity of the data. In this paper, we propose "probabilistic subgraph" (PS) queries over a graph/network database, which afford users great flexibility in specifying "approximately" what they(More)