DATA MINING OF CRM KNOWLEDGE BASES FOR EFFECTIVE MARKET SEGMENTATION - A Conceptual Framework

2004 ◽  
Keyword(s):  
Data Mining ◽  
Conceptual Framework ◽  
Market Segmentation ◽  
Knowledge Bases

Data mining for building knowledge bases: techniques, architectures and applications

2016 ◽  
Vol 31 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
John Calvo Martinez ◽  
Paul Compton
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Event Extraction ◽  
Data Sources ◽  
Small Scale ◽  
Knowledge Mining ◽  
Practical Applications ◽  
Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

Integrated Experiences

2015 ◽  
pp. 278-295
Author(s):  
Carol Carruthers ◽  
Dragana Martinovic ◽  
Kyle Pearce
Keyword(s):  
Young Adults ◽  
Statistical Analysis ◽  
Mathematics Education ◽  
Conceptual Framework ◽  
Knowledge Bases ◽  
Successful Implementation ◽  
Community Of Learners ◽  
Mathematical Concepts ◽  
Education Environment ◽  
K 12

This chapter discusses the integrated experiences of a group of instructors who are using tablets to teach mathematics to adolescents and young adults. iPad technology offers learners in different educational streams and with different knowledge bases an environment that fosters the growth of a community of learners engaged in mathematical concepts and processes. The authors present an in-depth examination of the design of a tablet-based mathematics education environment and provide a statistical analysis to highlight the full richness of their classroom-based experiments. The results are presented using the five foundational aspects of a conceptual framework for the successful implementation of technology in a K-12 environment.

Ontologies in the Health Field

2011 ◽  
pp. 37-56 ◽  
Author(s):  
Michel Simonet ◽  
Radja Messai ◽  
Gayo Diallo
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Health Data ◽  
Efficient Manner ◽  
Common Definition ◽  
Ontology Design ◽  
Definition Of ◽  
Health Field

Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.

An Approach to Normative Segmentation

1978 ◽  
Vol 15 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Vijay Mahajan ◽  
Arun K. Jain
Keyword(s):  
Conceptual Framework ◽  
Market Segmentation ◽  
Resource Constraints ◽  
Allocation Of Resources ◽  
Market Segments ◽  
Use Of Resources

In current approaches to normative market segmentation, development of segments and allocation of resources to these segments are considered as two independent steps. The result may be infeasible or suboptimal segmentation schemes which contribute to inefficient use of resources. The authors propose a conceptual framework wherein the market segments are developed within the managerial, institutional, and resource constraints. Mathematical formulations and illustrative examples are provided.

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