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Ontology Learning from Incomplete Semantic Web Data by BelNet

Man Zhu, Zhiqiang Gao, Jeff Z. Pan, Yuting Zhao, Ying Xu, Zhibin Quan
2013 2013 IEEE 25th International Conference on Tools with Artificial Intelligence  
In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks.  ...  The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue.  ...  We also plan to investigate the combination of learning algorithms with reasoning engines such as TrOWL [17] . VIII.  ... 
doi:10.1109/ictai.2013.117 dblp:conf/ictai/ZhuGPZXQ13 fatcat:io3sj7ufzjaofo5m35n3xmxgdq

An Overview of Bayesian Network Applications in Uncertain Domains

Khalid Iqbal, Xu-Cheng Yin, Hong-Wei Hao, Qazi Mudassar Ilyas, Hazrat Ali
2015 Journal of clean energy technologies  
The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains.  ...  Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association  ...  Bayesian Networks Learning for PPDM with Reliability In Fig. 3, BN   . The learning process of BN can be described over  .  ... 
doi:10.7763/ijcte.2015.v7.996 fatcat:qlhgx3kuevenxlvn3q5uu5lubu

Constrained parameter estimation with uncertain priors for Bayesian networks

Ali Karimnezhad, Peter J. F. Lucas, Ahmad Parsian
2017 Electronic Journal of Statistics  
In this paper we investigate the task of parameter learning of Bayesian networks and, in particular, we deal with the prior uncertainty of learning using a Bayesian framework.  ...  Parameter learning is explored in the context of Bayesian inference and we subsequently introduce Bayes, constrained Bayes and robust Bayes parameter learning methods.  ...  Learning BNs from data is normally split into two different, although related steps: (1) learning the structure of the network and (2) learning the parameters [8, 18] .  ... 
doi:10.1214/17-ejs1350 fatcat:4rk5zts3qvhohovsxpfyjnp7au

Decision-theoretic specification of credal networks: A unified language for uncertain modeling with sets of Bayesian networks

Alessandro Antonucci, Marco Zaffalon
2008 International Journal of Approximate Reasoning  
We also consider the problem of inference on Bayesian networks, when the reason that prevents some of the variables from being observed is unknown.  ...  Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets.  ...  Learning from incomplete data Given three binary random variables A, B and C, let the DAG A ! B ! C express independencies between them.  ... 
doi:10.1016/j.ijar.2008.02.005 fatcat:7sajtcscwbhwbo24vljn6czxpm

Retrospective analysis of uncertain eruption precursors at La Soufrière volcano, Guadeloupe, 1975–77: volcanic hazard assessment using a Bayesian Belief Network approach

Thea K Hincks, Jean-Christophe Komorowski, Stephen R Sparks, Willy P Aspinall
2014 Journal of Applied Volcanology  
A statistical tool to formalize such inferences is the Bayesian Belief Network (BBN).  ...  Revisiting the episode highlights many challenges for modern, contemporary decision making under conditions of considerable uncertainty, and suggests the BBN is a suitable framework for marshalling multiple, uncertain  ...  For data-rich applications, network parameters and even the network structure itself can be estimated from data, using learning algorithms (Murphy 2002) .  ... 
doi:10.1186/2191-5040-3-3 fatcat:4o7r3jhewbbf7nfsqf2otsvo44

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

Louise K. Lloyd, Jonathan J. Forster
2014 Computational Statistics & Data Analysis  
Results from analysis of these data directly influence Government road safety policy and ensure the introduction of effective safety interventions across the country.  ...  For an explicit way to include this uncertainty we use a Bayesian analysis to combine three sources of exposure using a log-Normal model with model priors representing our uncertainty in each data source  ...  registered vehicle data and Maureen Keigan and Jeremy Broughton at TRL for helpful advice.  ... 
doi:10.1016/j.csda.2013.10.020 fatcat:b4uq2mro2rawvk6cl4u5ifoaci

Learning Bayesian Network Structure from Incomplete Data without Any Assumption [chapter]

Céline Fiot, G. A. Putri Saptawati, Anne Laurent, Maguelonne Teisseire
Database Systems for Advanced Applications  
In particular, bayesian networks are one machine learning technique that allow for reasoning with incomplete data, but training such networks with incomplete data may be a difficult task.  ...  Many methods were thus proposed to learn bayesian network structure with incomplete data, based on multiple structure generation and scoring of their adequacy to the dataset.  ...  Conclusion In this paper, we introduced a new method for learning bayesian networks from incomplete data.  ... 
doi:10.1007/978-3-540-78568-2_30 dblp:conf/dasfaa/FiotSLT08 fatcat:rlytxo6l2nhy5igkbzivbzn6fy

Using Bayesian network on network tomography

Weiping Zhu
2003 Computer Communications  
In this paper, we propose an approach in the multicast class that uses the Bayesian network to carry out statistical inference.  ...  The obtained information can help us to understand the dynamic nature of networks.  ...  By exploiting the learning ability of a BN on discovering uncertain/hidden knowledge from incomplete data, the linklevel characteristics can be uncovered.  ... 
doi:10.1016/s0140-3664(02)00131-7 fatcat:xrvvq6zgpzgz7cljfonsp6s2ay

Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network [chapter]

Keum-Sung Hwang, Sung-Bae Cho
2006 Lecture Notes in Computer Science  
The vision-based scene understanding technique that infers scene-interpreting contexts from real-world vision data has to not only deal with various uncertain environments but also reflect user's requests  ...  The logic network works for supporting logical inference of Bayesian network.  ...  Interactive Learning Method We propose a method that adapts logic network and combine it with Bayesian network to deal with the interactive data collected from the inference module application process.  ... 
doi:10.1007/11864349_104 fatcat:s2yamyuxl5enhj5hrnjfuuqrfq

Inference in Bayesian networks

Chris J Needham, James R Bradford, Andrew J Bulpitt, David R Westhead
2006 Nature Biotechnology  
Learning in Bayesian networks The representation and use of probability theory make Bayesian networks suitable for learning from incomplete data sets, expressing causal relationships, combining domain  ...  of the variables, which avoids overfitting to the data, which may be noisy, limited, incomplete and uncertain.  ... 
doi:10.1038/nbt0106-51 pmid:16404397 fatcat:2of6gyvus5cpxclyeb3l4mib6y

Bayesian networks for supporting query processing over incomplete autonomous databases

Rohit Raghunathan, Sushovan De, Subbarao Kambhampati
2013 Journal of Intelligent Information Systems  
We learn this distribution in terms of Bayesian networks.  ...  Our approach involves mining/"learning" Bayesian networks from a sample of the database, and using it to do both imputation (predict a missing value) and query rewriting (retrieve relevant results with  ...  Thus, we use techniques for learning from complete training data.  ... 
doi:10.1007/s10844-013-0277-0 fatcat:ghxs4a4t6zgzfpcgt2kglizbou

The Hugin Tool for Learning Bayesian Networks [chapter]

Anders L. Madsen, Michael Lang, Uffe B. Kjærulff, Frank Jensen
2003 Lecture Notes in Computer Science  
The performance of the Hugin Tool is illustrated using real-world Bayesian networks, commonly used examples from the literature, and randomly generated Bayesian networks.  ...  The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks.  ...  Each of these consists of a number of sub-steps, which guide the user in the process of learning the Bayesian network from data and possibly expert knowledge.  ... 
doi:10.1007/978-3-540-45062-7_49 fatcat:xu4cay2ru5fdven2lt5i5s2bua

Bayesian Networks Modeling for Crop Diseases [chapter]

Chunguang Bi, Guifen Chen
2011 IFIP Advances in Information and Communication Technology  
The paper describes the flowchart of a Bayesian network and the principles used to calculate the conditional probabilities required in it.  ...  In the presence of risk and uncertainty, this paper focuses on finding out the best pest control decisionmaking program which is based on the Bayesian network.  ...  It uses probability theory to express the correlation between the variables, also could learn and reason under a limited, incomplete and uncertain information condition.  ... 
doi:10.1007/978-3-642-18333-1_37 fatcat:p7l2b7fcffbuxkeultuvruiami

Modeling Traffic Information using Bayesian Networks

W.P. van den Haak, Rothkrantz L.J.M, P. Wiggers, B.M.R. Heijligers, T. Bakri, D. Vukovic
2010 Transactions on Transport Sciences  
KEY WORDS: Bayesian Networks, prediction, vehicle speed, inductive loop detector data.  ...  This prediction model based only on historical data and our Bayesian Network are combined in a hybrid model, where we evaluate performance as well.  ...  Bayesian Networks allow us to reason about an uncertain domain (Korb & Nicholson, 2004) .  ... 
doi:10.2478/v10158-010-0018-09 fatcat:6cr57cthifbejmhhdau7cg34pm

Learning-Based Routing in Cognitive Networks

Tahir Alyas, Nadia Tabassum, Shahid Naseem, Fahad Ahmed, Qura Tul Ein
2014 IARS' International Research Journal  
Learning from the network environment, in order to optimally adapt the network settings, is an essential requirement for providing efficient communication services in such environments.  ...  Cognitive networks are capable of learning and reasoning. They can energetically adapt to varying network conditions in order to optimize end-to-end performance and utilize network resources.  ...  Bayesian Networks has an advantage that they visually represent all the relationships between the variables in the system via connecting arcs and they can handle situations where the data set is incomplete  ... 
doi:10.51611/iars.irj.v4i2.2014.40 fatcat:6vg3tueddnbppai4cylzml3tg4
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