Results 1  10
of
29
Hingeloss Markov Random Fields: Convex Inference for Structured Prediction
 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 hingeloss Markov random ..."
Abstract

Cited by 28 (19 self)
 Add to MetaCart
(Show Context)
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 hingeloss Markov random fields (HLMRFs), an expressive class of graphical models with logconcave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HLMRFs 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 HLMRFs, and show how to train HLMRFs with several learning algorithms. Our experiments show that HLMRFs match or surpass the predictive performance of stateoftheart methods, including discrete models, in four application domains. 1
Knowledge graph identification
 In ICML workshop on Structured Learning
, 2013
"... Abstract. Largescale 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 ..."
Abstract

Cited by 16 (10 self)
 Add to MetaCart
(Show Context)
Abstract. Largescale 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 coreferent entities, and incorporating ontological information that constrains the knowledge graph. Our approach uses probabilistic soft logic (PSL), a recentlyintroduced 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 realworld set of extractions from the NELL webextraction project, with over 1M extractions and 70K ontological relations, showing superior results to existing methods on both datasets. 1
Probabilistic soft logic for semantic textual similarity.
 for Computational Linguistics.
, 2014
"... Abstract Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of naturallanguage meaning, where distributional information is represented in the form of weighted inference rules. We apply this fra ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
(Show Context)
Abstract Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of naturallanguage meaning, where distributional information is represented in the form of weighted inference rules. We apply this framework to the task of Semantic Textual Similarity (STS) (i.e. judging the semantic similarity of naturallanguage sentences), and show that PSL gives improved results compared to a previous approach based on Markov Logic Networks (MLNs) and a purely distributional approach.
NetworkBased DrugTarget Interaction Prediction with Probabilistic Soft Logic
 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
, 2014
"... Drugtarget interaction studies are important because they can predict drugsâ 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 ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
Drugtarget interaction studies are important because they can predict drugsâ 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 drugtarget interactions augmented with drugdrug and targettarget 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 drugtarget 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 noncollective and single similarity assumptions. Finally, we illustrate that our PSL model achieves stateoftheart performance with simple, interpretable rules and evaluate our novel predictions using online datasets.
Learning Latent Groups with Hingeloss Markov Random Fields
"... Probabilistic models with latent variables are powerful tools that can help explain related phenomena by mediating dependencies among them. Learning in the presence of latent variables can be difficult though, because of the difficulty of marginalizing them out, or, more commonly, maximizing a lower ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
Probabilistic models with latent variables are powerful tools that can help explain related phenomena by mediating dependencies among them. Learning in the presence of latent variables can be difficult though, because of the difficulty of marginalizing them out, or, more commonly, maximizing a lower bound on the marginal likelihood. In this work, we show how to learn hingeloss Markov random fields (HLMRFs) that contain latent variables. HLMRFs are an expressive class of undirected probabilistic graphical models for which inference of most probable explanations is a convex optimization. By incorporating latent variables into HLMRFs, we can build models that express rich dependencies among those latent variables. We use a hard expectationmaximization algorithm to learn the parameters of such a model, leveraging fast inference for learning. In our experiments, this combination of inference and learning discovers useful groups of users and hashtags in a Twitter data set. 1.
Collective Activity Detection using Hingeloss Markov Random Fields
"... We propose hingeloss Markov random fields (HLMRFs), a powerful class of continuousvalued graphical models, for highlevel computer vision tasks. HLMRFs are characterized by logconcave density functions, and are able to perform efficient, exact inference. Their templated hingeloss potential fun ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
We propose hingeloss Markov random fields (HLMRFs), a powerful class of continuousvalued graphical models, for highlevel computer vision tasks. HLMRFs are characterized by logconcave density functions, and are able to perform efficient, exact inference. Their templated hingeloss potential functions naturally encode softvalued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HLMRFs via familiar constructs from firstorder logic. We apply HLMRFs 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 lowlevel detectors. 1.
Inferring user preferences by probabilistic logical reasoning over social networks. arXiv preprint arXiv:1411.2679
, 2014
"... We propose a framework for inferring the latent attitudes or preferences of users by performing probabilistic firstorder logical reasoning over the social network graph. Our method answers questions about Twitter users like Does this user like sushi? or Is this user a New York Knicks fan? by bui ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
We propose a framework for inferring the latent attitudes or preferences of users by performing probabilistic firstorder logical reasoning over the social network graph. Our method answers questions about Twitter users like Does this user like sushi? or Is this user a New York Knicks fan? by building a probabilistic model that reasons over user attributes (the user’s location or gender) and the social network (the user’s friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York). The algorithm uses distant supervision, semisupervised data harvesting and vector space models to extract user attributes (e.g. spouse, education, location) and preferences (likes and dislikes) from text. The extracted propositions are then fed into a probabilistic reasoner (we investigate both Markov Logic and Probabilistic Soft Logic). Our experiments show that probabilistic logical reasoning significantly improves the performance on attribute and relation extraction, and also achieves an Fscore of 0.791 at predicting a users likes or dislikes, significantly better than two strong baselines.
DrugTarget Interaction Prediction for Drug Repurposing with Probabilistic Similarity Logic
"... 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 ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
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 drugtarget interaction prediction framework based on probabilistic similarity logic (PSL) [5]. Interaction prediction corresponds to link prediction in a bipartite network of drugtarget interactions extended with a set of similarities between drugs and between targets. Using probabilistic firstorder 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 drugtarget interactions obtained from Drugbank [27] augmented with five drugbased and three targetbased similarities. We integrate domain knowledge in drugtarget interaction prediction and match the performance of the stateoftheart drugtarget interaction prediction systems [22] with our model using simple triadbased rules. Furthermore, we apply techniques that make link prediction in PSL more efficient for drugtarget interaction prediction.
Semantic parsing using distributional semantics and probabilistic logic
 In ACL 2014 Workshop on Semantic Parsing
, 2014
"... We propose a new approach to semantic parsing that is not constrained by a fixed formal ontology and purely logical inference. Instead, we use distributional semantics to generate only the relevant part of an onthefly ontology. Sentences and the onthefly ontology are represented in probabilist ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
We propose a new approach to semantic parsing that is not constrained by a fixed formal ontology and purely logical inference. Instead, we use distributional semantics to generate only the relevant part of an onthefly ontology. Sentences and the onthefly ontology are represented in probabilistic logic. For inference, we use probabilistic logic frameworks like Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL). This semantic parsing approach is evaluated on two tasks, Textual Entitlement (RTE) and Textual Similarity (STS), both accomplished using inference in probabilistic logic. Experiments show the potential of the approach. 1
LargeScale Knowledge Graph Identification using PSL
"... Building a webscale 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 ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Building a webscale 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 realworld dataset containing over 1M facts and 80K ontological constraints in 12 hours and produces a highprecision set of facts for inclusion into a knowledge graph. 1.