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The Empirical Study of Knowledge Discovery Based on Ontology

Author: ZhouShu
Tutor: WangRenWu
School: East China Normal University
Course: Information Science
Keywords: Noumenon Data warehouse Knowledge Discovery
CLC: TP182
Type: Master's thesis
Year: 2010
Downloads: 158
Quote: 0
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With the development of the database data storage and application technology, the data in the enterprise is running is also a geometric growth, which requires massive data to meet the specific needs of the data, which provide decision support for enterprise managers. Data Mining and Knowledge Discovery (KDD) is generated in this context. KDD term was first officially formed on First International KDD Conference held in August 1989 in the U.S. city of Detroit. Fayyad is defined as KDD from the data centralized identify valid, novel, potentially useful, and ultimately understandable patterns nontrivial process \A complete knowledge discovery architecture is constituted by the data source, data storage, data mining engine and front-end tools. The main content of the data storage layer, data warehousing, data warehouse, establishment of heterogeneous data integration process, but also the knowledge found in the foundation and core. Father of the data warehouse Inmon definition put forward in a book published in 1991, \to support management decision-making. Seen from the definition of the data warehouse semantic characteristics of human-computer interaction, knowledge discovery, data source computer reasoning and judgment, complex data sources through semantic modeling makes knowledge discovery. The body was first derived from the philosophy of the concept, in the calculation of the scientific field of artificial intelligence, Knowledge Systems Laboratory, Stanford University Grubber first proposed ontology is widely accepted definition: \The definition can be seen, the ontology conceptual model of the real world exists, its essence is used to implement the interaction between man and machine, and machine to machine. As semantic modeling ontology engineering knowledge discovery can help to solve the problem of heterogeneous data integration, XML to build the underlying data source described using RDF (S) to help knowledge discovery. Empirical research on ontology-based knowledge discovery ontology and knowledge discovery, based primarily on the body to build a data warehouse, knowledge in the data warehouse found this logic. Completed a series of the following work: object-oriented analysis of the real world terminology in the field of second-hand housing; using OWL language to create a field of second-hand housing body; collected to fill the field of second-hand housing ontology instances, heterogeneous data into a unified framework of the body being formed XML format data sources; heterogeneous data sources, data integration and knowledge fusion operation; analysis and design of a data warehouse of second-hand housing field; ETL ontology instances imported into the data warehouse; combination of data mining algorithms and ontology semantic characteristics of multi-dimensional data, simple data mining experiments. Found that the role of the whole system and the whole process of experiments to explore the body of knowledge, the body on the one hand as building the data source and ETL metadata; the other hand, can take advantage of the semantic characteristics of the data mining algorithms combine of knowledge discovery some support.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Expert systems, knowledge engineering
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