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Graph-based learning for information...

The University of Arizona., Management Information Systems.

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Graph-based learning for information systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Graph-based learning for information systems./
Author:	Li, Xin.
Description:	197 p.
Notes:	Adviser: Hsinchun Chen.
Contained By:	Dissertation Abstracts International70-04A.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3352368
ISBN:	9781109092318

Graph-based learning for information systems.
Li, Xin.

Graph-based learning for information systems. - 197 p.

Adviser: Hsinchun Chen.

Thesis (Ph.D.)--The University of Arizona, 2009.

The advance of information technologies (IT) makes it possible to collect a massive amount of data in business applications and information systems. The increasing data volumes require more effective knowledge discovery techniques to make the best use of the data. This dissertation focuses on knowledge discovery on graph-structured data, i.e., graph-based learning. Graph-structured data refers to data instances with relational information indicating their interactions in this study. Graph-structured data exist in a variety of application areas related to information systems, such as business intelligence, knowledge management, e-commerce, medical informatics, etc. Developing knowledge discovery techniques on graph-structured data is critical to decision making and the reuse of knowledge in business applications.

ISBN: 9781109092318Subjects--Topical Terms:

626628
Business Administration, Management.

Graph-based learning for information systems.
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020 $a 9781109092318
035 $a (UMI)AAI3352368
035 $a AAI3352368
040 $a UMI $c UMI
100 1 $a Li, Xin. $3 999627
245 1 0 $a Graph-based learning for information systems.
300 $a 197 p.
500 $a Adviser: Hsinchun Chen.
500 $a Source: Dissertation Abstracts International, Volume: 70-04, Section: A, page: 1345.
502 $a Thesis (Ph.D.)--The University of Arizona, 2009.
520 $a The advance of information technologies (IT) makes it possible to collect a massive amount of data in business applications and information systems. The increasing data volumes require more effective knowledge discovery techniques to make the best use of the data. This dissertation focuses on knowledge discovery on graph-structured data, i.e., graph-based learning. Graph-structured data refers to data instances with relational information indicating their interactions in this study. Graph-structured data exist in a variety of application areas related to information systems, such as business intelligence, knowledge management, e-commerce, medical informatics, etc. Developing knowledge discovery techniques on graph-structured data is critical to decision making and the reuse of knowledge in business applications.
520 $a In this dissertation, I propose a graph-based learning framework and identify four major knowledge discovery tasks using graph-structured data: topology description, node classification, link prediction, and community detection. I present a series of studies to illustrate the knowledge discovery tasks and propose solutions for these example applications. As to the topology description task, in Chapter 2 I examine the global characteristics of relations extracted from documents. Such relations are extracted using different information processing techniques and aggregated to different analytical unit levels. As to the node classification task, Chapter 3 and Chapter 4 study the patent classification problem and the gene function prediction problem, respectively. In Chapter 3, I model knowledge diffusion and evolution with patent citation networks for patent classification. In Chapter 4, I extend the context assumption in previous research and model context graphs in gene interaction networks for gene function prediction. As to the link prediction task, Chapter 5 presents an example application in recommendation systems. I frame the recommendation problem as link prediction on user-item interaction graphs, and propose capturing graph-related features to tackle this problem. Chapter 6 examines the community detection task in the context of online interactions. In this study, I propose to take advantage of the sentiments (agreements and disagreements) expressed in users' interactions to improve community detection effectiveness. All these examples show that the graph representation allows the graph structure and node/link information to be more effectively utilized in addressing the four knowledge discovery tasks.
520 $a In general, the graph-based learning framework contributes to the domain of information systems by categorizing related knowledge discovery tasks, promoting the further use of the graph representation, and suggesting approaches for knowledge discovery on graph-structured data. In practice, the proposed graph-based learning framework can be used to develop a variety of IT artifacts that address critical problems in business applications.
590 $a School code: 0009.
650 4 $a Business Administration, Management. $3 626628
650 4 $a Information Science. $3 1017528
690 $a 0454
690 $a 0723
710 2 $a The University of Arizona. $b Management Information Systems. $3 1026782
773 0 $t Dissertation Abstracts International $g 70-04A.
790 $a 0009
790 1 0 $a Chen, Hsinchun, $e advisor
790 1 0 $a Hariri, Salim $e committee member
790 1 0 $a Nunamaker, Jay F. $e committee member
790 1 0 $a Zeng, Daniel $e committee member
790 1 0 $a Zhao, Leon $e committee member
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3352368