Archive for the ‘Publication’ Category

Conference Poster, International World Wide Web Conference, WWW 2012, Lyon, France
Authors: Matthias Broecheler, Andrea Pugliese, VS Subrahmanian
Direct link to paper
Abstract
The Social Semantic Web (SSW) refers to the mix of RDF data in web content, and social network data associated with those who posted that content. Applications to monitor the SSW are becoming increasingly popular. For [...]

Even long journeys eventually come to an end. After 4 years of intensive research I successfully defended my doctoral dissertation at the University of Maryland. The thesis ties together my work on indexing and querying huge social networks and machine learning on multi-relational data under the common theme of social network data management. A lot [...]

Conference Paper, International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, Kaohsiung, Taiwan
Authors: Matthias Broecheler, Andrea Pugliese, VS Subrahmanian
Direct link to paper
Abstract
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. [...]

Workshop Paper, ICDE Workshop on Graph Data Management, 2011, Hannover, Germany
Authors: Matthias Broecheler, Andrea Pugliese, VS Subrahmanian
Direct link to paper
Abstract
As social network and RDF data grow dramatically in size to billions of edges, the ability to scalably answer queries posed over graph datasets becomes increasingly important.
In this paper, we consider subgraph matching queries which are [...]

Abstract
There is a growing interest in methods for exploiting causal or correlational dependencies in structured domains. Exploiting such dependencies often results in improved predictive performance on complex inference tasks in diverse domains such as information integration, natural language processing, and computer vision. In this presentation, we introduce probabilistic soft logic (PSL), a general-purpose framework [...]

Workshop Paper, NIPS Workshop on Predictive Models in Personalized Medicine, 2010, Whistler, Canada
Authors: Stephen H. Bach, Matthias Broecheler, Stanley Kok, and Lise Getoor
Direct link to paper
Abstract
We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these [...]

Conference Paper, Advances in Neural Information Processing Systems (NIPS), 2010, Vancouver, Canada
Authors: Matthias Broecheler and Lise Getoor
Direct link to paper
Data set used in the experiments
Abstract
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general [...]

Conference Paper, Proceedings of the 2010 IEEE International Conference on Social Computing, Symposium Section
Authors: Matthias Broecheler, Paulo Shakarian, and V.S. Subrahmanian
Direct link to paper
Abstract
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, [...]

Conference Paper, Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, Odense, Denmark
Authors: Matthias Bröcheler, Andrea Pugliese and V.S. Subrahmanian
Direct link to paper
3rd Best Paper Award
Abstract
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” [...]

Conference Paper, Proceedings of the 2010 conference on Uncertainty in Artificial Intelligence
Presented at: Conference on Uncertainty in Artificial Intelligence, held on Santa Catalina Island, CA, USA from July 8th - July 11th, 2010
Authors: Matthias Broecheler, Lilyana Mihalkova, Lise Getoor
Direct link to paper
Data set used in the experiments
Abstract
Many machine learning applications require the ability to learn [...]


About this blog

Where is the knowledge we have lost in information?
- T.S. Elliot, The Rock

We are drowning in data - exabytes of it. My research explores technologies that can help us organize, structure, and efficiently search huge amounts of information as well as automatically deduce actionable pieces of knowledge from it. Learn more

I was a PhD student at the University of Maryland with research interests in databases, artificial intelligence, and machine learning. Learn more
 

My Homepage: http://www.matthiasb.com
Also on Twitter: @MBroecheler

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