Results 1 - 10 of 17

A short introduction to Probabilistic Soft Logic.

by Angelika Kimmig , Stephen H Bach , Matthias Broecheler , Bert Huang , Lise Getoor - In Proceedings of NIPS Workshop on Probabilistic Programming: Foundations and Applications (NIPS Workshop-12). , 2012
"... Abstract 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 co ..."
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Abstract 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 paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.
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Hingeloss Markov Random Fields: Convex Inference for Structured Prediction

by Stephen H. Bach, Bert Huang, Ben London, Lise Getoor - In Uncertainty in Artificial Intelligence , 2013
"... Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random ..."
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Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HL-MRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains. 1
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Knowledge graph identification

by Jay Pujara, Hui Miao, Lise Getoor, William Cohen - In ICML workshop on Structured Learning , 2013
"... Abstract. Large-scale information extraction systems extract massive amounts of interrelated information, but unfortunately transforming these noisy and incomplete extractions into useful knowledge or facts is a formidable challenge. We consider the setting where uncertain extractions about entities ..."
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Abstract. Large-scale information extraction systems extract massive amounts of interrelated information, but unfortunately transforming these noisy and incomplete extractions into useful knowledge or facts is a formidable challenge. We consider the setting where uncertain extractions about entities and the relations between them constitute candidate facts for inclusion in a knowledge graph. To remove noise and infer missing information in the knowledge graph, we propose knowledge graph identification: jointly reasoning about these candidate facts and their associated extraction confidences, identifying co-referent entities, and incorporating ontological information that constrains the knowledge graph. Our approach uses probabilistic soft logic (PSL), a recently-introduced probabilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL web-extraction project, with over 1M extractions and 70K ontological relations, showing superior results to existing methods on both datasets. 1
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Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic

by Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daume ́ Iii, Lise Getoor
"... Massive open online courses (MOOCs) attract a large number of student registra-tions, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding stud ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
Massive open online courses (MOOCs) attract a large number of student registra-tions, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding student engagement as a course progresses is essential for minimizing dropout rates. Formally defining student engagement in an online setting is challenging. In this paper, we leverage activity (such as posting in discussion forums, timely submission of assignments, etc.), linguistic features from forum content and structural features from forum interaction to identify two different forms of student engagement (passive and ac-tive) in MOOCs. We use probabilistic soft logic (PSL) to model student engage-ment by capturing domain knowledge about student interactions and performance. We test our models on MOOC data from Coursera and demonstrate that modeling engagement is helpful in predicting student performance. 1

Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic

by Shobeir Fakhraei, Bert Huang, Louiqa Raschid, Lise Getoor - IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , 2014
"... Drug-target interaction studies are important because they can predict drugs’ unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem us ..."
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Drug-target interaction studies are important because they can predict drugs’ unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL). Using probabilistic rules in PSL, we predict interactions with models based on triad and tetrad structures. We apply (blocking) techniques that make link prediction in PSL more efficient for drug-target interaction prediction. We then perform extensive experimental studies to highlight different aspects of the model and the domain, first comparing the models with different structures and then measuring the effect of the proposed blocking on the prediction performance and efficiency. We demonstrate the importance of rule weight learning in the proposed PSL model and then show that PSL can effectively make use of a variety of similarity measures. We perform an experiment to validate the importance of collective inference and using multiple similarity measures for accurate predictions in contrast to non-collective and single similarity assumptions. Finally, we illustrate that our PSL model achieves state-of-the-art performance with simple, interpretable rules and evaluate our novel predictions using online datasets.

Hinge-loss Markov random fields and probabilistic soft logic

by Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor , 2015
"... A fundamental challenge in developing high-impact machine learning technologies is balancing the ability to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge ..."
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A fundamental challenge in developing high-impact machine learning technologies is balancing the ability to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, distinguished from previous approaches by their ability to both capture rich structure and scale to big data. The first, hinge-loss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then derive HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We next introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization software, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.

Collective Activity Detection using Hinge-loss Markov Random Fields

by Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis
"... We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential fun ..."
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We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HL-MRFs via familiar constructs from first-order logic. We apply HL-MRFs to the task of activity detection, using principles of collective classification. Our model is simple, intuitive and interpretable. We evaluate our model on two datasets and show that it achieves significant lift over the low-level detectors. 1.
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Drug-Target Interaction Prediction for Drug Repurposing with Probabilistic Similarity Logic

by Shobeir Fakhraei, Louiqa Raschid, Lise Getoor
"... The high development cost and low success rate of drug discovery from new compounds highlight the need for methods to discover alternate therapeutic effects for currently approved drugs. Computational methods can be effective in focusing efforts for such drug repurposing. In this paper, we propose a ..."
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The high development cost and low success rate of drug discovery from new compounds highlight the need for methods to discover alternate therapeutic effects for currently approved drugs. Computational methods can be effective in focusing efforts for such drug repurposing. In this paper, we propose a novel drug-target interaction prediction framework based on probabilistic similarity logic (PSL) [5]. Interaction prediction corresponds to link prediction in a bipartite network of drug-target interactions extended with a set of similarities between drugs and between targets. Using probabilistic first-order logic rules in PSL, we show how rules describing link predictions based on triads and tetrads can effectively make use of a variety of similarity measures. We learn weights for the rules based on training data, and report relative importance of each similarity for interaction prediction. We show that the learned rule weights significantly improve prediction precision. We evaluate our results on a dataset of drug-target interactions obtained from Drugbank [27] augmented with five drug-based and three target-based similarities. We integrate domain knowledge in drug-target interaction prediction and match the performance of the state-of-the-art drug-target interaction prediction systems [22] with our model using simple triad-based rules. Furthermore, we apply techniques that make link prediction in PSL more efficient for drug-target interaction prediction.
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Large-Scale Knowledge Graph Identification using PSL

by Jay Pujara, Hui Miao, Lise Getoor, William Cohen
"... Building a web-scale knowledge graph, which captures information about entities and the relationships between them, represents a formidable challenge. While many largescale information extraction systems operate on web corpora, the candidate facts they produce are noisy and incomplete. To remove noi ..."
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Building a web-scale knowledge graph, which captures information about entities and the relationships between them, represents a formidable challenge. While many largescale information extraction systems operate on web corpora, the candidate facts they produce are noisy and incomplete. To remove noise and infer missing information in the knowledge graph, we propose knowledge graph identification: a process of jointly reasoning about the structure of the knowledge graph, utilizing extraction confidences and leveraging ontological information. Scalability is often a challenge when building models in domains with rich structure, but we use probabilistic soft logic (PSL), a recentlyintroduced probabilistic modeling framework which easily scales to millions of facts. In practice, our method performs joint inference on a real-world dataset containing over 1M facts and 80K ontological constraints in 12 hours and produces a high-precision set of facts for inclusion into a knowledge graph. 1.

A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

by Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor
"... In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data. Much of this data is best represented as multi-relational graphs, making vertex-programming abstractions such as those of Pregel ..."
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In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data. Much of this data is best represented as multi-relational graphs, making vertex-programming abstractions such as those of Pregel and GraphLab ideal fits for modern large-scale data analysis. In this paper, we describe a vertex-programming implementation of a popular consensus optimization technique known as the alternating direction method of multipliers (ADMM) [1]. ADMM consensus optimization allows the elegant solution of complex objectives such as inference in rich probabilistic models. We also introduce a novel hypergraph partitioning technique that improves over the state-of-the-art vertex programming framework and significantly reduces the communication cost by reducing the number of replicated nodes by an order of magnitude. We implement our algorithm in GraphLab and measure scaling performance on a variety of realistic bipartite graphs and a large synthetic voter-opinion analysis application. We show a 50 % improvement in running time over the current GraphLab partitioning scheme.
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