Approval Sheet - Computer Science and Electrical Engineering

148 Pages·2006·0.89 MB·English

Approval Sheet

Title of Dissertation: Semantically-Linked Bayesian Networks: A
Framework for Probabilistic Inference Over Multiple
Bayesian Networks

Name of Candidate: Rong Pan
Doctor of Philosophy, 2006

Dissertation and Abstract Approved: ______________________________
Doctor Yun Peng
Professor (Chair)
Department of Computer Science
and Electrical Engineering

Date Approved: __________________
Curriculum Vitae

Name: Rong Pan

Permanent address: 20 Casey Ct, Catonsville, Maryland, 21228.

Degree and date to be conferred: Ph.D., 2006.

Date of Birth: June 13, 1979.

Place of Birth: Beijing, People’s Republic of China .

Secondary education:
The Fifth High School of Beijing,
Beijing, People’s Republic of China, June 1997

Collegiate institutions attended:
Peking University, Beijing, People’s Republic of Chin a
September 1997 – June 2001, B.S., June 2001
Major: Computer Science

University of Maryland, Baltimore County, Baltimore, Maryland
September 2001 – August 2006, Ph.D., December 2005
Major: Computer Science
Professional Publications:

Rong Pan, Yun Peng, and Zhongli Ding, Belief Update in Bayesian Networks Using
Uncertain Evidence. Submitted to the 18th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI06).
Rong Pan, Zhongli Ding, Yang Yu, and Yun Peng, A Bayesian Network Approach to
Ontology Mapping. In Proceedings of the Fourth International Semantic Web Conference
(ISWC2005), Galway, Ireland, November 2005.
Zhongli Ding, Yun Peng, and Rong Pan, BayesOWL: Uncertainty Modeling in Semantic
Web Ontologies. In Soft Computing in Ontologies and Semantic Web, Springer-Verlag,
December 2005.
Li Ding, Rong Pan, Tim Finin, Anupam Joshi, Yun Peng and Pranam Kolari, Finding
and Ranking Knowledge on the Semantic Web. In Proceedings of the Fourth International
Semantic Web Conference (ISWC2005), Galway, Ireland, November 2005.
Li Ding, Rong Pan, Tim Finin, Anupam Joshi, Yun Peng and Pranam Kolari, Search on
the Semantic Web. IEEE Computer, October 2005, 62-69.
Rong Pan and Yun Peng, A Framework for Bayesian Network Mapping. The Twentieth
National Conference on Artificial Intelligence (AAAI-05), student abstract, Pittsburgh,
PA, July 2005.
Zhongli Ding, Yun Peng, Rong Pan, and Yang Yu, A Bayesian Methodology Towards
Automatic Ontology Mapping. AAAI-05 Workshop on Contexts and Ontologies: Theory,
Practice and Applications, Pittsburgh, PA, July 2005.
Tim Finin, Li Ding, Rong Pan, Anupam Joshi, Pranam Kolari, Akshay Java and Yun
Peng, Swoogle: Searching for knowledge on the Semantic Web. In proceedings of AAAI
05 (intelligent systems demo) , July, 2005.
Zhongli Ding, Yun Peng, and Rong Pan, A Bayesian Approach to Uncertainty Modeling
in OWL Ontology. In Proceedings of 2004 International Conference on Advances in
Intelligent Systems - Theory and Applications (AISTA2004). November 15-18, 2004,
Luxembourg-Kirchberg, Luxembourg.
Li Ding, Tim Finin, Anupam Joshi, Rong Pan, R. Scott Cost, Joel Sachs, Vishal Doshi,
Pavan Reddivari, and Yun Peng, Swoogle: A Search and Metadata Engine for the
Semantic Web, Thirteenth ACM Conference on Information and Knowledge
Management (CIKM'04), Washington DC, November 2004.
Youyong Zou, Tim Finin, Li Ding, Harry Chen, and Rong Pan, Using Semantic Web
technology in Multi-Agent Systems: a case study, in the TAGA trading agent environment
5th International Conference On Electronic Commerce: Technologies, Pittsburg, 1-3
October 2003.
Youyong Zou, Tim Finin, Li Ding, Harry Chen, and Rong Pan, TAGA: Trading Agent
Competition in Agentcities, Workshop on Trading Agent Design and Analysis, held in
conjunction with the Eighteenth International Joint Conference on Artificial Intelligence,
11 August, 2003, Acuulco MX.
Professional Positions Held

2003 – 2006:
Research Assistant
Ebiquity Lab, UMBC

2005 (summer)
Summer Intern
Stottler Henke Associates Inc.

2002 (spring):
Intern
Silicon Graphic, Inc., China
Abstract

Title of Dissertation: Semantically-Linked Bayesian Networks: A Framework for
Probabilistic Inference Over Multiple Bayesian Networks

Rong Pan, Doctor of Philosophy, 2006

Dissertation Directed by:
Yun Peng
Professor
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County

At the present time, Bayesian networks (BNs), presumably the most popular uncertainty
inference framework, are still widely used as standalone systems. When the problem
itself is distributed, domain knowledge has to be centralized and unified before a single
BN can be created. Alternatively, separate BNs describing related sub-domains or
different aspects of the same domain may be created, but it is difficult to combine them
for problem solving even if the interdependent relations between variables across these
BNs are available. Existing approaches have greatly restricted expressiveness and
applicability as they either impose very strong constraints on the distributed domain
knowledge or only focus on a specific application. What is missing is a principled
framework that can support probabilistic inference over separately developed BNs.
In this thesis, we propose a theoretical framework, named Semantically-Linked Bayesian
Networks (SLBN), to fill this blank. SLBN is distinguished from existing work in that it
defines linkages between semantically similar variables and probabilistic influences are
carried by variable linkage from one BN to another by soft evidences and virtual
evidences. To support SLBN’s inference, weh ave developed two algorithms for belief
update with soft evidences. Both of these algorithms have clear computational and
practical advantages over the methods proposed by others in the past. To justify SLBN’s
inference process, we propose J-graph to represent the jointed knowledge of the linked
BNs and the variable linkages. Finally, SLBN is applied to the problem of concept
mapping between semantic web ontologies.
Semantically-Linked Bayesian Network:

A Framework for Probabilistic Inference Over

Multiple Bayesian Networks

by
Rong Pan

Dissertation submitted to the Faculty of the Graduate School
of the University of Maryland in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2006
© Copyright Rong Pan 2006
To my family
Table of Contents

1 Introduction...............................................................................................................1
1.1 The Motivations................................................................................................1
1.2 Thesis Statement................................................................................................3
1.3 Dissertation Outline...........................................................................................4
2 Background and Related Work...............................................................................6
2.1 Bayesian Network.............................................................................................7
2.2 Iterative Proportional Fitting Procedure..........................................................12
2.3 Distributed Bayesian Network Models...........................................................14
2.3.1 Multiply Sectioned Bayesian Network (MSBN)........................................15
2.3.2 Agent Encapsulated Bayesian Network (AEBN).......................................17
2.4 Semantic Web..................................................................................................20
2.4.1 Probabilistic Extension of OWL...............................................................21
2.4.2 Information Integration in Semantic Web.................................................25
2.5 Summary.........................................................................................................29
3 Belief Update in Bayesian Network Using Uncertain Evidence.........................30
3.1 Jeffrey’s Rule and Soft Evidence....................................................................32
3.2 Virtual Evidence..............................................................................................34
3.3 IPFP on Bayesian Network.............................................................................37
3.4 Inference with Multiple Soft Evidential Findings...........................................38
3.4.1 Iteration on the Network...........................................................................39
3.4.2 Iteration on Local Distribution.................................................................44
3.4.3 Time and Space Performance...................................................................47
3.5 Experiments and Evaluation............................................................................48
3.6 Summary.........................................................................................................50
4 Variable Linkage.....................................................................................................52
4.1 Semantic Similarity.........................................................................................52
4.2 Variable Linkage.............................................................................................55
4.2.1 Pair-Wise Variable Linkage.....................................................................55
4.2.2 Variable Linkage.......................................................................................59
4.2.3 Expressiveness of Variable Linkage.........................................................62
4.3 Consistency between Variable Linkages and Linked Bayesian Networks.....67
4.4 Summary.........................................................................................................70
5 Evidential Inference with Variable Linkage........................................................72
5.1 Informal Descriptions of Probabilistic Influence via Linkages.......................72
5.1.1 Probabilistic Influence on Destination Variables.....................................73
5.1.2 Probabilistic Influence on Other Variables..............................................74
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