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Citation Segmentation from Sparse & Noisy Data: An Unsupervised Joint Inference Approach with Markov Logic Networks
2015
Zenodo
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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]
2008
Lecture Notes in Computer Science
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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
2009
Synthesis Lectures on Artificial Intelligence and Machine Learning
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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]
2018
arXiv
pre-print
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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
2014
IEEE Transactions on robotics
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2013 pre-print arXiv:1309.2660v1 fatcat:uaestqaglbcqlp5fzpwb7etsjqOther Versions
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
2019
Acta Numerica
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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
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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
2019
SIGMOD record
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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
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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
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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]
2017
Encyclopedia of Machine Learning and Data Mining
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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
2010
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10
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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
2007
BMC Bioinformatics
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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
2020
Journal of Big Data
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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]
2008
Lecture Notes in Computer Science
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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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