Semantic Knowledge Discovery from Heterogeneous Data Sources
نویسندگان
چکیده
Available domain ontologies are increasing over the time. However there is a huge amount of data stored and managed with RDBMS. We propose a method for learning association rules from both sources of knowledge in an integrated way. The extracted patterns can be used for performing: data analysis, knowledge completion, ontology refinement.
منابع مشابه
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources
Development of high throughput data acquisition technologies, together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. This has resulted in unprecedented opportunities in data-driven knowledge acquisition and decisionmaking in a number of emerging increasingly data-rich application ...
متن کاملContext mediation among knowledge discovery components
Acknowledgements I would like to express my gratitude to my supervisors Prof. John Hughes and Prof. David Bell for their guidance. The thesis has benefited from a number of research projects and the input provided by a range of individuals. Initial work was conducted at the University of Abertay, Scotland under the heartening guidance of Dr. Colin Miller and Dr. Louis Nathanson. During my stay ...
متن کاملIntegration of Data from Heterogeneous Sources using ETL Technology
Data integration is a crucial issue in the environments of heterogeneous data sources. At present, the afore-mentioned heterogeneity is becoming widespread. Based on various data sources, if we want to gain useful information and knowledge, we must solve data integration problems in order to apply appropriate analytical methods to comprehensive and uniform data. Such activity is known as knowle...
متن کاملInformation Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources
We present INDUS (Intelligent Data Understanding System), a federated, query-centric system for knowledge acquisition from autonomous, distributed, semantically heterogeneous data sources that can be viewed (conceptually) as tables. INDUS employs ontologies and inter-ontology mappings, to enable a user or an application to view a collection of such data sources (regardless of location, internal...
متن کاملOntology-Based Data Integration from Heterogeneous Urban Systems: A Knowledge Representation Framework for Smart Cities
This paper presents a novel knowledge representation framework for smart city planning and management that enables the semantic integration of heterogeneous urban data from diverse sources. Currently, the combination of information across city agencies is cumbersome, as the increasingly available datasets are stored in disparate data silos, using different models and schemas for their descripti...
متن کامل