Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Structured probabilistic models: Bayesian networks and beyond
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Parameter Estimation in Stochastic Logic Programs
Machine Learning
From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
Learning probabilistic relational models
Relational Data Mining
Complex Probabilistic Modeling with Recursive Relational Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Distributed Repositories of Highly Expressive Reusable Ontologies
IEEE Intelligent Systems
Learning Probabilistic Relational Models
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Expressive Probability Models in Science
DS '99 Proceedings of the Second International Conference on Discovery Science
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Probabilistic discovery of overlapping cellular processes and their regulation
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Methods for evaluating and creating data quality
Information Systems - Special issue: Data quality in cooperative information systems
PRL: A probabilistic relational language
Machine Learning
Principles of dataspace systems
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Images, Frames, and Connectionist Hierarchies
Neural Computation
Diagnosis using a first-order stochastic language that learns
Expert Systems with Applications: An International Journal
A formal analysis of information disclosure in data exchange
Journal of Computer and System Sciences
Knowledge model-based heterogeneous multi-robot system implemented by a software platform
Knowledge-Based Systems
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
ACM Transactions on Computational Logic (TOCL)
Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain
The Knowledge Engineering Review
Report on the probabilistic language scheme
Proceedings of the 2007 symposium on Dynamic languages
Natural interaction in intelligent spaces: Designing for architecture and entertainment
Multimedia Tools and Applications
Reasoning with recursive loops under the PLP framework
ACM Transactions on Computational Logic (TOCL)
Structured machine learning: the next ten years
Machine Learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Characteristic relational patterns
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
MPE and partial inversion in lifted probabilistic variable elimination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic reasoning with hierarchically structured variables
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Exploring optimization of semantic relationship graph for multi-relational Bayesian classification
Decision Support Systems
Classifying Multiple Imbalanced Attributes in Relational Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Probabilistic logic reasoning about traffic scenes
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
A partition-based first-order probabilistic logic to represent interactive beliefs
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Toward general analysis of recursive probability models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
OMEN: a probabilistic ontology mapping tool
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A fuzzy frame-based knowledge representation formalism
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A unified context model: bringing probabilistic models to context ontology
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Probabilistic reasoning techniques for the tactical military domain
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Converting a naive bayes models with multi-valued domains into sets of rules
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Data Mining and Knowledge Discovery
Understanding tuberculosis epidemiology using structured statistical models
Artificial Intelligence in Medicine
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
Privacy-Preserving EM algorithm for clustering on social network
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Combining heterogeneous classifiers for relational databases
Pattern Recognition
Scalable relation prediction exploiting both intrarelational correlation and contextual information
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS's) and Bayesian networks (BNs). FRS's provide an excellent representation for the organizational structure of large complex domains, but their applicability is limited because of their inability to deal with uncertainty and noise. BNs provide an intuitive and coherent probabilistic representation of our uncertainty, but are very limited in their ability to handle complex structured domains. In this paper, we provide a language that cleanly integrates these approaches, preserving the advantages of both. Our approach allows us to provide natural and compact definitions of probability models for a class, in a way that is local to the class frame. These models can be instantiated for any set of interconnected instances, resulting in a coherent probability distribution over the instance properties. Our language also allows us to represent important types of uncertainty that cannot be accomodated within the framework of traditional BNs: uncertainty over the set of entities present in our model, and uncertainty about the relationships between these entities. We provide an inference algorithm for our language via a reduction to inference in standard Bayesian networks. We describe an implemented system that allows most of the main frame systems in existence today to annotate their knowledge bases with probabilistic information, and to use that information in answering probabilistic queries.