The Knowledge Increase Estimation Framework for Ontology Integration on the Instance Level

In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.

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An approach to ontology integration for ontology reuse in knowledge based digital ecosystems

Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems - MEDES '15 ◽

10.1145/2857218.2857219 ◽

2015 ◽

Cited By ~ 17

Author(s):

Enrico G. Caldarola ◽

Antonio Picariello ◽

Antonio M. Rinaldi

Keyword(s):

Digital Ecosystems ◽

Ontology Integration ◽

Knowledge Based ◽

Ontology Reuse

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Ontology Integration Using ε-Connections

Lecture Notes in Computer Science - Modular Ontologies ◽

10.1007/978-3-642-01907-4_14 ◽

2009 ◽

pp. 293-320 ◽

Cited By ~ 14

Author(s):

Bernardo Cuenca Grau ◽

Bijan Parsia ◽

Evren Sirin

Keyword(s):

Ontology Integration

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Local Neighbor Enrichment for Ontology Integration

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-28487-8_16 ◽

2012 ◽

pp. 156-166 ◽

Cited By ~ 3

Author(s):

Trong Hai Duong ◽

Hai Bang Truong ◽

Ngoc Thanh Nguyen

Keyword(s):

Ontology Integration

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Semantic Integration of Structured and Unstructured Data in Data Warehousing and Knowledge Management Systems

Semantic Technologies for Business and Information Systems Engineering ◽

10.4018/978-1-60960-126-3.ch005 ◽

2011 ◽

pp. 81-105

Author(s):

Liane Haak

Keyword(s):

Knowledge Management ◽

Information Systems ◽

Data Warehousing ◽

Semantic Integration ◽

Unstructured Data ◽

Management Systems ◽

Knowledge Management Systems ◽

Application Area ◽

Ontology Integration ◽

Basic Ideas

Nowadays, increasing information in enterprises demands new ways of searching and connecting the existing information systems. This chapter describes an approach for the integration of structured and unstructured data focusing on the application to Data Warehousing (DW) and Knowledge Management (KM). Semantic integration is used to improve the interoperability between two well-known and established information systems in the business context of nowadays enterprises. The objective is to introduce a semantic solution in the field of Business Intelligence based on ontology integration. The main focus of this chapter is not to provide a complete literature review of all existing approaches or just to point put the motivation for such an approach. In fact, it presents, under consideration of the most important research approaches, a solution for how a Semantic Integration could be technically achieved in this specific application area. After pointing out the motivation, a short introduction to Semantic Integration, the problems and challenges occurring from it, and the application area of Knowledge Management and Data Warehousing are given. Besides the basic ideas of ontologies and ontology integration are introduced. The approach itself starts with a short overview on the determined requirements, followed by a concept for generating an ontology from a Data Warehouse System (DWS) to be finally integrated with Knowledge Management Systems (KMS) ontology. Finally SENAGATOR, an exemplarily system for semantic navigation based on integrated ontologies, is shortly introduced.

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