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21
A Bayesian Network Approach to Ontology Mapping
 In: Proceedings ISWC 2005
, 2005
"... Abstract. This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Baye ..."
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Cited by 40 (5 self)
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Abstract. This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small realworld ontologies. 1
Modifying Bayesian Networks by Probability Constraints
, 2005
"... This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We pro ..."
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Cited by 18 (11 self)
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This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm EIPFP is further developed to DIPFP, which reduces the computational cost by decomposing a global EIPFP into a set of smaller local EIPFP problems. Limited analysis is provided, including the convergence proofs of the two algorithms. Computer experiments were conducted to validate the algorithms. The results are consistent with the theoretical analysis. 1
A Hybrid Model For Sharing Information Between Fuzzy, Uncertain And Default Reasoning Models In MultiAgent Systems
, 2002
"... This paper develops a hybrid model which provides a unified framework for the fol lowing four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truthqualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an exten ..."
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Cited by 12 (3 self)
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This paper develops a hybrid model which provides a unified framework for the fol lowing four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truthqualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4) truthqualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of rea soning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledgebased multiagent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper
A Bayesian Methodology towards Automatic Ontology Mapping
 Proc of AAAI C&O2005 Workshop
, 2005
"... This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modelling uncertainty in semantic web. The proposed method includes four components: 1) learning probabilities (priors about conc ..."
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Cited by 10 (4 self)
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This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modelling uncertainty in semantic web. The proposed method includes four components: 1) learning probabilities (priors about concepts, conditionals between subconcepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naïve Bayes text classification technique, by explicitly associating a concept with a group of sample documents retrieved and selected automatically from World Wide Web (WWW); 2) representing in OWL the learned probability information concerning the entities and relations in given ontologies; 3) using the BayesOWL framework to automatically translate
Paid: A probabilistic agentbased intrusion detection system
 IN COMPUTERS & SECURITY
, 2005
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Belief Update in Bayesian Networks Using Uncertain Evidence
 18th IEEE International Conference on Tools with Artificial Intelligence
, 2006
"... This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three exi ..."
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Cited by 7 (2 self)
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This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey’s rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This indepth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past. 1.
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
"... Abstract. In this paper we analyze the performance of three algorithms for soft evidential update, in which a probability distribution represented by a Bayesian network is modified to a new distribution constrained by given marginals, and closest to the original distribution according to cross entro ..."
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Cited by 6 (3 self)
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Abstract. In this paper we analyze the performance of three algorithms for soft evidential update, in which a probability distribution represented by a Bayesian network is modified to a new distribution constrained by given marginals, and closest to the original distribution according to cross entropy. The first algorithm is a new and improved version of the big clique algorithm [1] that utilizes lazy propagation [2]. The second and third algorithm [3] are wrapper methods that convert soft evidence to virtual evidence, in which the evidence for a variable consists of a likelihood ratio. Virtual evidential update is supported in existing Bayesian inference engines, such as Hugin. To evaluate the three algorithms, we implemented BRUSE (Bayesian Reasoning Using Soft Evidence), a new Bayesian inference engine, and instrumented it. The resulting statistics are presented and discussed.
Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis
 Reliability Engineering and System Safety
, 2008
"... Abstract: This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identi ..."
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Cited by 5 (3 self)
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Abstract: This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian networks ’ inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.
Logical and probabilistic reasoning to support information analysis in uncertain domains
 In Progic Workshop
, 2007
"... Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require special ..."
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Cited by 4 (2 self)
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Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domainspecific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemas not supportable in Prolog.
AgentEncapsulated Bayesian Networks and the Rumor Problem (Extended Abstract)
"... We present a multiagent organization for data interpretation and fusion in which each agent uses an encapsulated Bayesian network for knowledge representation, and agents communicate by exchanging beliefs (marginal posterior probabilities) on shared variables. We call this organization an AgentEnca ..."
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Cited by 1 (1 self)
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We present a multiagent organization for data interpretation and fusion in which each agent uses an encapsulated Bayesian network for knowledge representation, and agents communicate by exchanging beliefs (marginal posterior probabilities) on shared variables. We call this organization an AgentEncapsulated Bayesian Network (AEBN) system. Communication of probabilities among agents leads to rumors, i.e. potential double counting of information. We propose a new and correct method to compensate for rumors in AEBN systems by passing extended messages that contain joint probabilities. Categories and Subject Descriptors I.2.3 [Artificial Intelligence]: Deduction and Theorem