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Citation Segmentation from Sparse & Noisy Data: An Unsupervised Joint Inference Approach with Markov Logic Networks

Dustin Heckmann, Anette Frank, Matthias Arnold, Peter Gietz, Christian Roth
2015 Zenodo  
A single abstract from the DHd-2015 Book of Abstracts.  ...  In unserer Arbeit präsentieren wir ein Verfahren für Zitationsanalyse mittels Markov Logic Networks und Joint Inference.  ...  Nach Beispiel von Poon & Domingos (2007) stützt sich unser Verfahren auf Markov Logic Networks (MLN), einem Framework für Statistical Relational Learning, das Prädikatenlogik mit probabilistischer Modellierung  ... 
doi:10.5281/zenodo.4623282 fatcat:hzb3ry6laffg3ikjgv3r6xuhsy

Just Add Weights: Markov Logic for the Semantic Web [chapter]

Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla
2008 Lecture Notes in Computer Science  
Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction.  ...  Markov logic brings the power of probabilistic modeling to first-order logic by attaching weights to logical formulas and viewing them as templates for features of Markov networks.  ...  Sloan Fellowship and NSF CAREER Award to the first author, and a Microsoft Research fellowship awarded to the second author.  ... 
doi:10.1007/978-3-540-89765-1_1 fatcat:msbve5kalvgdtk44mvcsxhjale

Markov Logic: An Interface Layer for Artificial Intelligence

Pedro Domingos, Daniel Lowd
2009 Synthesis Lectures on Artificial Intelligence and Machine Learning  
Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution.  ...  Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields.  ...  The model has been shown to perform well on noisy and sparse data.  ... 
doi:10.2200/s00206ed1v01y200907aim007 fatcat:em6ggc2ha5f4lgaie53jkdjtbu

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data [article]

Shuo Yang
2018 arXiv   pre-print
It presents the work on cost-sensitive statistical relational learning for mining structured imbalanced data, the first continuous-time probabilistic logic model for predicting sequential events from longitudinal  ...  structured data as well as hybrid probabilistic relational models for learning from heterogeneous structured data.  ...  It is worth mentioning that we also tried to experiment with Markov Logic Networks on the same data with Alchemy 2.  ... 
arXiv:1811.00749v1 fatcat:5tbyk62ahjh3zkosrwem7picpi

Data-Driven Grasp Synthesis—A Survey

Jeannette Bohg, Antonio Morales, Tamim Asfour, Danica Kragic
2014 IEEE Transactions on robotics  
We also draw a parallel to the classical approaches that rely on analytic formulations.  ...  Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps.  ...  There are various ways to deal with sparse, incomplete and noisy data from real sensors such as stereo cameras: we divided the approaches into methods that i) approximate the full shape of an object, ii  ... 
doi:10.1109/tro.2013.2289018 fatcat:kvm53bbmq5ebvdj4q3kqmoqjmy

Solving inverse problems using data-driven models

Simon Arridge, Peter Maass, Ozan Öktem, Carola-Bibiane Schönlieb
2019 Acta Numerica  
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge  ...  This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.  ...  Acknowledgements This article builds on lengthy discussions and long-standing collaborations with a large number of people.  ... 
doi:10.1017/s0962492919000059 fatcat:2f7te542wrftphdhurcdnw6dqu

Sentiment Analysis [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Case Study: Markov Logic Networks Markov logic combines first-order logic with Markov networks.  ...  Anguelov et al. (2005) have used relational Markov networks for segmentation of 3D scan data.  ...  The term should not be confused with a planning problem such as BOXWORLD or BLOCKSWORLD.).  ... 
doi:10.1007/978-1-4899-7687-1_100512 fatcat:ce4yyqo2czftzcx2kbauglh3fu

Data Provenance

Peter Buneman, Wang-Chiew Tan
2019 SIGMOD record  
with a small amount of coordination.  ...  We demonstrate the effectiveness of our proposed techniques with experimental results. CREATE TABLE pascalsTri[i:1...][i] (val) AS SELECT * FROM pascalsTri[i-1][i-1]  ...  This work was supported in part by a Hellman Fellowship and by the NIDDK of the NIH under award number R01DK114945.  ... 
doi:10.1145/3316416.3316418 fatcat:u4lmbha4fjgajlc46r66ryh7vi

Randomized Decision Rule [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Synonyms Kernel methods; Networks with kernel functions;  ...  Markov logic networks (MLNs) (Richardson and Domingos 2006) upgrade Markov networks to first-order logic and allow networks with cycles.  ...  Inference in PRMs occurs by constructing a Bayesian network by instantiating the PRM with the data in the database and performing the inference in the latter.  ... 
doi:10.1007/978-1-4899-7687-1_100393 fatcat:wys64niz3rcdxmei376insgmsq

Spike-Timing-Dependent Plasticity [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Case Study: Markov Logic Networks Markov logic combines first-order logic with Markov networks.  ...  Anguelov et al. (2005) have used relational Markov networks for segmentation of 3D scan data.  ...  The term should not be confused with a planning problem such as BOXWORLD or BLOCKSWORLD.).  ... 
doi:10.1007/978-1-4899-7687-1_774 fatcat:2jprihjaxfbtpb3ttwuuz3u34y

Recommender Systems [chapter]

Prem Melville, Vikas Sindhwani
2017 Encyclopedia of Machine Learning and Data Mining  
Synonyms Kernel methods; Networks with kernel functions;  ...  Markov logic networks (MLNs) (Richardson and Domingos 2006) upgrade Markov networks to first-order logic and allow networks with cycles.  ...  Inference in PRMs occurs by constructing a Bayesian network by instantiating the PRM with the data in the database and performing the inference in the latter.  ... 
doi:10.1007/978-1-4899-7687-1_964 fatcat:3voghk7xz5cindlgj4pwek7r6u

On community outliers and their efficient detection in information networks

Jing Gao, Feng Liang, Wei Fan, Chi Wang, Yizhou Sun, Jiawei Han
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields (HMRF).  ...  Examples include web data or hypertext documents connected via hyperlinks, social networks or user profiles connected via friend links, co-authorship and citation information, blog data, movie reviews  ...  This is particularly true for very large networks, since information from both nodes and links can be noisy and incomplete.  ... 
doi:10.1145/1835804.1835907 dblp:conf/kdd/GaoLFWSH10 fatcat:hdrupc54bnb6tkrqjsngsfqfti

Inferring cellular networks – a review

Florian Markowetz, Rainer Spang
2007 BMC Bioinformatics  
The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.  ...  The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks.  ...  Regression Rogers and Girolami [28] use sparse Bayesian regression based on a Gaussian linear model to estimate a dependency network from knock-out data.  ... 
doi:10.1186/1471-2105-8-s6-s5 pmid:17903286 pmcid:PMC1995541 fatcat:3cqptcs6ord5zkrhnyuvhu7svq

Sensor data quality: a systematic review

Hui Yie Teh, Andreas W. Kempa-Liehr, Kevin I-Kai Wang
2020 Journal of Big Data  
Their framework also uses ontology to represent sensors and data quality along with fuzzy logic to evaluate the quality of data received.  ...  The joint probability distribution of the variables, A, B, C, and D is represented as, according to the Chain Rule of probability: It also follows the Local Markov property, which states that each variable  ...  Authors' contributions HYT conducted the systematic review which includes gathering and extracting data from all the papers from various databases that were used for the manuscript and wrote the first  ... 
doi:10.1186/s40537-020-0285-1 fatcat:cbl346kh35cqvn6nh7njzvrq5e

L-Diversity Based Dynamic Update for Large Time-Evolving Microdata [chapter]

Xiaoxun Sun, Hua Wang, Jiuyong Li
2008 Lecture Notes in Computer Science  
Typical approaches for citation matching are Joint Segmentation (Jnt-Seg) and Joint Segmentation Entity Resolution (Jnt-Seg-ER).  ...  In this paper we propose an alternative joint inference approach---Generalized Joint Segmentation (Generalized-Jnt-Seg).  ... 
doi:10.1007/978-3-540-89378-3_47 fatcat:3srytfhtszejdidu62nvzwia3m
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