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Relational Models [chapter]

Volker Tresp, Maximilian Nickel
2018 Encyclopedia of Social Network Analysis and Mining  
In social network analysis, nodes would be individuals or actors and links would correspond to ties Linked Data Linked (Open) Data describes a method of publishing structured data so that it can be interlinked  ...  Much of Linked Data is based on the RDF data model Collective learning refers to the effect that an entity's relationships, attributes or class membership can be predicted not only from the entity's attributes  ...  of structural learning.  ... 
doi:10.1007/978-1-4939-7131-2_245 fatcat:fst5kd2d7vhx7f7gmcjnl2ko3a

Relational Models [chapter]

Volker Tresp, Maximilian Nickel
2014 Encyclopedia of Social Network Analysis and Mining  
In social network analysis, nodes would be individuals or actors and links would correspond to ties Linked Data Linked (Open) Data describes a method of publishing structured data so that it can be interlinked  ...  Much of Linked Data is based on the RDF data model Collective learning refers to the effect that an entity's relationships, attributes or class membership can be predicted not only from the entity's attributes  ...  of structural learning.  ... 
doi:10.1007/978-1-4614-6170-8_245 fatcat:f5qvlgr4bva2vfr7uyzxsvpl4y

Relational Models [chapter]

Volker Tresp, Maximilian Nickel
2016 Encyclopedia of Social Network Analysis and Mining  
In social network analysis, nodes would be individuals or actors and links would correspond to ties Linked Data Linked (Open) Data describes a method of publishing structured data so that it can be interlinked  ...  Much of Linked Data is based on the RDF data model Collective learning refers to the effect that an entity's relationships, attributes or class membership can be predicted not only from the entity's attributes  ...  of structural learning.  ... 
doi:10.1007/978-1-4614-7163-9_245-1 fatcat:tl44qhp7sfa5zi3hi3pnjpeueq

Relational Models [article]

Volker Tresp, Maximilian Nickel
2016 arXiv   pre-print
Relational models have applications in social networks analysis, the modeling of knowledge graphs, bioinformatics, recommendation systems, natural language processing, medical decision support, and linked  ...  Relational models typically are based on probabilistic graphical models, e.g., Bayesian networks, Markov networks, or latent variable models.  ...  of structural learning.  ... 
arXiv:1609.03145v1 fatcat:ytomwo4l5nfsrnpm2ens5iww6m

Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis [article]

Jiuchuan Jiang, Manfred Jaeger
2015 arXiv   pre-print
In particular, we use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature  ...  The resulting framework provides natural relational extensions of classical probabilistic models for categorical data.  ...  The convergence here shows that even if the exact values of the parameters cannot be learned, a probabilistically equivalent model is learned (the learned value of the parameter α also converges to the  ... 
arXiv:1506.05055v1 fatcat:isgp7yixqzgbphdi3wshtyxpli

DC Proposal: Ontology Learning from Noisy Linked Data [chapter]

Man Zhu
2011 Lecture Notes in Computer Science  
In particular, we will learn OWL axioms inductively from Linked Data under probabilistic setting, and analyze the noises in the Linked Data on the basis of the learned axioms.  ...  As the 'Web of Linked Data' vision of the Semantic Web is coming true, the 'explosion' of Linked Data provides more than sufficient data for ontology learning algorithms in terms of quantity.  ...  Additionally, we gratefully acknowledge funding from the National Science Foundation of China under grants 60873153, 60803061, and 61170165. References  ... 
doi:10.1007/978-3-642-25093-4_31 fatcat:uggfwwvk75d57de4gpd2vtclly

The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

David A. Cohn, Thomas Hofmann
2000 Neural Information Processing Systems  
Furthermore, the relationships between topics is mapped out in order to build a predictive model of link content.  ...  We describe a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives.  ...  Once a decomposition is learned, the model may be used to address questions like "What words are likely to be found in a document with this link structure?"  ... 
dblp:conf/nips/CohnH00 fatcat:kzj2u6ekg5dwrdxrohwyze6vfa

:{unav)

Y. Xiang, S.K.M. Wong, N. Cercone
2012 Machine Learning  
Search procedures that modify a network structure one link at a time have been commonly used for efficiency. Our study indicates that a class of domain models cannot be learned by such procedures.  ...  Several scoring metrics are used in different search procedures for learning probabilistic networks. We study the properties of cross entropy in learning a decomposable Markov network.  ...  The authors are members of the Institute for Robotics and Intelligent Systems (IRIS) and wish to acknowledge the support of the Networks of Centres of Excellence Program of the Government of Canada, NSERC  ... 
doi:10.1023/a:1007324100110 fatcat:wbxajeih6rg3lc5xmzd3lyxiai

Social networks and statistical relational learning: a survey

Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro
2012 International Journal of Social Network Mining  
Statistical relational learning (SRL) is a very promising approach to SNM, since it combines expressive representation formalisms, able to model complex relational networks, with statistical methods able  ...  One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people.  ...  When the structure of the model is known, there is a need to learn the parameters of the model. In general, both the structure and its parameters must be learned.  ... 
doi:10.1504/ijsnm.2012.051057 fatcat:6i7n2zrrj5geho3eh6nicay5nm

Juice: A Julia Package for Logic and Probabilistic Circuits

Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
2021 AAAI Conference on Artificial Intelligence  
Additionally, it supports several parameter and structure learning algorithms proposed in the recent literature.  ...  JUICE is an open-source Julia package providing tools for logic and probabilistic reasoning and learning based on logic circuits (LCs) and probabilistic circuits (PCs).  ...  Design We model circuits as linked node structures. Inference routines iterate over the circuit forward or backward, passing results from node to node.  ... 
dblp:conf/aaai/DangKLVB21 fatcat:ojlysnccl5a7bbwiccwu5shxla

STATISTICAL RELATIONAL LEARNING: A STATE-OF-THE-ART REVIEW

Muhamet Kastrati, Marenglen Biba
2019 Journal of Engineering Technology and Applied Sciences  
(PILP) [16] is based on the idea that we can build models that can effectively represent, reason and learn in domains with presence of uncertainty and complex relational structure.  ...  As shown in Figure 1 , SRL combines a logic-based representation with probabilistic modeling and machine learning.  ...  Probabilistic relational models (PRMs) PRMs [25] represent a rich representation language for structured statistical models [27] . PRMs combine advantages of relational logic with BNs.  ... 
doi:10.30931/jetas.594586 fatcat:qoei3pteibd6la4oqin6rvrxqi

Learning the Latent State Space of Time-Varying Graphs [article]

Nesreen K. Ahmed, Christopher Cole, Jennifer Neville
2014 arXiv   pre-print
This has led to an increased interest in mechanisms to model the dynamic structure of time-varying graphs.  ...  We use the two representations as inputs to a mixture model to learn the latent state transitions that correspond to important changes in the Email graph structure over time.  ...  In addition, the probabilistic model can distinguish between different types of events and breaks based on the intensity of their effect on the graph structure.  ... 
arXiv:1403.3707v1 fatcat:wv3mbwshljgbrc5et2hpl5olwu

Model accuracy for hierarchical problems

Hossein Karshenas, Amin Nikanjam, B. Hoda Helmi, Adel T. Rahmani
2009 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems  
Estimation of distribution algorithms, especially those using Bayesian network as their probabilistic model, have been able to solve many challenging optimization problems, including the class of hierarchical  ...  The efforts in this field are mainly concentrated on single-level pr oblems, due to complex structure of hierarchical pr oblems which makes them hard to treat.  ...  Estimation of distribution algorithms (also known as probabilistic model-building genetic algorithms) as a type of genetic algorithms and a solution to linkage learning, exploit a probabilistic model to  ... 
doi:10.1109/icicisys.2009.5358041 fatcat:m6isfwwn7jfopmh2gyucgzbxz4

A Performance Improvement Inference Method For Link Prediction in Social Graphs

Ashly Ann Jo, Prince. V. Jose
2018 IOP Conference Series: Materials Science and Engineering  
Social network analysis has turned into a conspicuous field in link prediction.The precise social network models are additionally address a few downsides.  ...  The proposed system predicts the accuracy and efficiency of link prediction utilizing MAP Markov Logic induction technique and MPE Bayesian derivation strategy.  ...  On contrasted with the factual social learning approaches, the majority of the above works are just centered around easier probabilistic models for query handling Proposed System A model to appraise  ... 
doi:10.1088/1757-899x/396/1/012021 fatcat:xgiernssa5cfxds7v7tz5kiwuq

Link mining

Lise Getoor
2003 SIGKDD Explorations  
A key challenge for data mining is tackling the problem of mining richly structured datasets, where the objects are linked in some way.  ...  Links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models.  ...  In learning statistical models for multi-relational data, we must not only search over probabilistic dependencies, as is standard in any type of statistical model selection problem, but potentially we  ... 
doi:10.1145/959242.959253 fatcat:th3ijcstpvdyzbaxq7payz2vle
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