A Method for Ontology Conflict Resolution and Integration on Relation Level

Nguyên, Ngoc Thanh

doi:10.1080/01969720701601098

Cited by 36 publications

(12 citation statements)

References 13 publications

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“…Notice that in this work we do not consider appearance negative knowledge which was investigated in other works (N. T. Nguyen 2002Nguyen , 2007. This means that we assume that opinions are given by agents (or experts) considered as positive knowledge.…”

Section: Methods For Knowledge Integration Knowledge Integration Problemmentioning

confidence: 92%

“…The main idea of this kind of functions is: the minimal cost needed for transformation of one set into the other (N. T. Nguyen 2007). Similarly, the difference between two time intervals with probabilities is considered as the cost 230…”

Section: Functions Minimizing Transformation Costs (D Pr )mentioning

confidence: 99%

“…We also use some notions defined in those works. N. T. Nguyen (2007) defined two kinds of distance functions between attribute values as follows:…”

Section: Basic Notionsmentioning

confidence: 99%

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A Method for Temporal Knowledge Integration Using Indeterminate Model of Time

Nguyen

Nguyen²

2013

Cybernetics and Systems

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Integration of knowledge is one of the most important tasks for knowledge management in distributed environments. For the same subject in the real world, each different source may generate different versions of data. Of course, with local integrity constraints these data are consistent, but they may be inconsistent with global integrity constraints. This is a popular phenomenon in processing data from distributed sources in the real world. In this work we will investigate processing inconsistency of knowledge and its integration process using the temporal model of indeterminate valid time and probability. This data model is used to describe events that take place in the future with some certainty degrees. With this model, conflict is considered as a situation in which for the same event agents give different time intervals and probabilities about the occurrence of that event. In the integration process we need to determine a proper time interval and probability (based on consensus method) that properly represent that event. With this aim, two kinds of distance functions are defined as well as analyzed. In addition, some postulates and algorithms for knowledge integration are also worked out and analyzed.

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Section: Methods For Knowledge Integration Knowledge Integration Problemmentioning

confidence: 92%

Section: Functions Minimizing Transformation Costs (D Pr )mentioning

confidence: 99%

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A Method for Temporal Knowledge Integration Using Indeterminate Model of Time

Nguyen

Nguyen²

2013

Cybernetics and Systems

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“…After that, we will generate non-redundant rules from these sets. Several relations between objects as reflexive relation, coherent relation, equivalence relation and so on were used for conflict resolution in the classification of ontology conflicts [10]. So, we will consider and study how to apply the one of these relations for mining sequential pattern problem in the future.…”

Section: Discussionmentioning

confidence: 99%

MSGPs: A Novel Algorithm for Mining Sequential Generator Patterns

Pham

Luo

Hong

et al. 2012

Computational Collective Intelligence. Technologies and Applications

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Abstract. Sequential generator pattern mining is an important task in data mining. Sequential generator patterns used together with closed sequential patterns can provide additional information that closed sequential patterns alone are not able to provide. In this paper, we proposed an algorithm called MSGPs, which based on the characteristics of sequential generator patterns and sequence extensions by doing depth-first search on the prefix tree, to find all of the sequential generator patterns. This algorithm uses a vertical approach to listing and counting the support, based on the prime block encoding approach of the prime factorization theory to represent candidate sequences and determine the frequency for each candidate. Experimental results showed that the proposed algorithm is effective.

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“…The main goal of belief merging problem is to obtain the common beliefs from several belief bases. It is applied in database integration 1,2 , information retrieval 3,4,5,6 , sensor data fusion 7 , coordination in multi-agent 8,9,10 , and multimedia systems 11,12 .…”

Section: Introductionmentioning

confidence: 99%

On the Belief Merging by Negotiation

Tran

Nguyen

2014

Procedia Computer Science

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Belief merging is an active research field with many important applications. Most existing work addresses the belief merging issue using a centralised approach. In this paper, we investigate a distributed approach to the problem of belief merging. The contribution of this paper is two-fold: (i) we develop a negotiation-based model for belief merging, and (ii) we investigate the computational complexity of the belief merging problem within the proposed framework. Through the proposed model of negotiation-based belief merging, we will present and discuss several significant logical properties and computational complexity results.

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A Method for Ontology Conflict Resolution and Integration on Relation Level

Cited by 36 publications

References 13 publications

A Method for Temporal Knowledge Integration Using Indeterminate Model of Time

A Method for Temporal Knowledge Integration Using Indeterminate Model of Time

MSGPs: A Novel Algorithm for Mining Sequential Generator Patterns

On the Belief Merging by Negotiation

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