1. Virginia Tech, Blacksburg, VA 24061 USA
RDEC-ACE Discussion Virginia Tech’s Digital Library Research Laboratory
Jan. 5, SAIC
Edward A. Fox,
Virginia Tech, Blacksburg, VA USA

3. Acknowledgements (Selected)
Sponsors: ACM, Adobe, AOL, CAPES, CNI, CONACyT, DFG, IBM, Microsoft, NASA, NDLTD, NLM, NSF (IIS , , , ; ITR ; DUE , , , ), OCLC, SOLINET, SUN, SURA, UNESCO, US Dept. Ed. (FIPSE), VTLS
Our efforts have been made possible through the support of sponsors, faculty, staff, and students. We gratefully acknowledge their assistance and collaboration. IBM has donated a large amount of equipment. The largest grants have come from NSF, FIPSE, and SURA. Content has been shared by ACM in a variety of efforts related to learning about computing. Many companies, like Adobe, Microsoft, and OCLC, have provided software and related assistance.
A large number of colleagues have worked on the various projects discussed. Students, serving as research assistants, preparing a thesis or dissertation, or engaged in class projects, have helped develop many of the systems and publications about Virginia Tech digital library initiatives.

4. Acknowledgements: Faculty, Staff
Lillian Cassel, Debra Dudley, Roger Ehrich, Joanne Eustis, Weiguo Fan, James Flanagan, C. Lee Giles, Eberhard Hilf, John Impagliazzo, Filip Jagodzinski, Rohit Kelapure, Neill Kipp, Douglas Knight, Deborah Knox, Aaron Krowne, Alberto Laender, Gail McMillan, Claudia Medeiros, Manuel Perez, Naren Ramakrishnan, Layne Watson, …

5. Acknowledgements: Students
Pavel Calado, Yuxin Chen, Fernando Das Neves, Shahrooz Feizabadi, Robert France, Marcos Goncalves, Nithiwat Kampanya, S.H. Kim, Aaron Krowne, Bing Liu, Ming Luo, Paul Mather, Saverio Perugini, Unni. Ravindranathan, Ryan Richardson, Rao Shen, Ohm Sornil, Hussein Suleman, Ricardo Torres, Wensi Xi, Xiaoyan Yu, Baoping Zhang, Qinwei Zhu, …

6. Stepping Stones & Pathways:
Improving retrieval by chains of relationships between document topics
Fernando Das-Neves, Virginia Tech DLRL

7. A Little Experiment (Compare a simple query with a longer version that explicitly includes stepping stones)
“Literary Style in Sherlock Holmes stories”
Note: Numbers are total relevant web pages in top 20 Google results for the query made up of terms on either end of the link.
No. of rel. docs.
VS.

8. Another Example
“What is the Relationship between Data Mining and Recommender Systems?”
Naïve Results: There are many matches that are possible answers.
Discussion: But, many of the pages with co-occurrences give no real information about the requested relationship. 7
Recommender
Systems
Data Mining
VS. 10
Machine Learning 10
Data Mining
Collaborative
Recommender
Social Networks
Filtering 15
Systems 9 11

9. An Alternative Interpretation of a Query in IR:
A query represents two related, separable concepts.
Objective: Retrieve a sequence of documents that support a valid set of chains of relationships between the two concepts.
Input: a query representing two concepts.
Output: two groups of documents + a set of stepping stones (document groups, i.e., clusters) connecting the topics by pathways (relations among clusters).

10. Type of Questions Matching Alternative Interpretation
Ill-defined questions, with non-enumerated answers:
“How or why is X related to Y?”
“What is the X of Y?”
Even if queries with form “give me something about X” lead to relevant docs, it is possible to increase the quantity and quality of information in the query result, when relations are explicit (as a result of our semi-automatic method).

11. Why is this useful? Questions of this type are common.
For example, such questions often occur during research studies.
These occur often in educational settings, e.g., for homework.
These occur often in workplace settings, requiring gathering and relating of information.
Handling of this type of question by current systems often is inadequate.

12. How to Build Stepping Stones and Pathways?
Our approach involves a belief network, to combine content+structure in document similarity calculation, including citation and co-citation similarities.
Find two relevant document sets, each related to one of the two original sub-queries.
Find a diverse set of strong candidates, each connecting the two subsets, but as different as possible from other candidates.
Create stepping stones by finding similar documents to those candidates; keep the clusters that are heavily cited, or whose documents are highly correlated (in all aspects).
Repeat the process, finding a new stepping stone in between each pair of clusters that are weakly related, until the pathway length is too long, or the similarity is sufficient.

13. Streams, Structures, Spaces, Scenarios, and Societies (5S): A Formal Digital Library Framework and Its Applications
Marcos André Gonçalves
Doctoral defense
Virginia Tech, Blacksburg, VA USA

14. Informal 5S Definition: DLs are complex systems that
help satisfy info needs of users (societies)
provide info services (scenarios)
organize info in usable ways (structures)
present info in usable ways (spaces)
communicate info with users (streams)

15. 5Ss Ss Examples Objectives Streams Structures Spaces Scenarios
Text; video; audio; image
Describes properties of the DL content such as encoding and language for textual material or particular forms of multimedia data
Structures
Collection; catalog; hypertext; document; metadata
Specifies organizational aspects of the DL content
Spaces
Measure; measurable, topological, vector, probabilistic
Defines logical and presentational views of several DL components
Scenarios
Searching, browsing, recommending
Details the behavior of DL services
Societies
Service managers, learners, teachers, etc.
Defines service managers, responsible for running DL services; actors, that use those services

16. Hypotheses A formal theory for DLs can be built based on 5S.
The formalization can serve as a basis for modeling and building high-quality DLs.

17. 5S Framework and DL Development (Gonçalves)

18. 5SLGen: Automatic DL Generation

19. Research Questions 1. Can we formally elaborate 5S?
2. How can we use 5S to formally describe digital libraries?
3. What are the fundamental relationships among the Ss and high-level DL concepts?
4. How can we allow digital librarians to easily express those relationships?
5. Which are the fundamental quality properties of a DL? Can we use the formalized DL framework to characterize those properties?
6. Where in the life cycle of digital libraries can key aspects of quality be measured and how?

20. Outline Motivation: the problem Part 1:Theory
Hypotheses and research questions
Part 1:Theory
5S: introduction, formal definitions
The formal ontology
Part 2: Tools/Applications
Language
Visualization
Generation
Logging
Part 3: Quality
Conclusions, Future Work

21. 5S and DL formal definitions and compositions (April 2004 TOIS)

22. Digital Library Formal Ontology

23. Composition of key infrastructure services

24. Composition of additional services

25. Ontology: Taxonomy of Services
Infrastructure Services
Information Satisfaction
Services
Repository-Building
Add
Value
Creational
Preservational
Acquiring
Authoring
Cataloging
Crawling (focused)
Describing
Digitizing
Harvesting
Submitting
Conserving
Converting
Copying/Replicating
Translating (format)
Annotating
Classifying
Clustering
Evaluating
Extracting
Indexing
Linking
Logging
Measuring
Rating
Reviewing (peer)
Surveying
Training (classifier)
Translating
Visualizing
Binding
Browsing
Customizing
Disseminating
Expanding(query)
Filtering
Recommending
Requesting
Searching

26. 5SL: a DL Modeling language
Domain specific languages
Address a particular class of problems by offering specific abstractions and notations for the domain at hand
Advantages: domain-specific analysis, program management, visualization, testing, maintenance, modeling, and rapid prototyping.
XML-based realization of 5S
Interoperability
Use of many standard sub-languages (e.g., MIME types, XML Schemas, UML notations)

27. Overview of 5SGraph Workspace (instance model) Structured toolbox
(metamodel)

28. 5SGen – Version 2: ODL, Services, Scenarios
5SL
5SL - -
Scenario
Scenario
Model
Model
(6)
(6) DL DL
5SL
5SL - -
Societies
Societies
XPath/JDOM
XPath/JDOM
Designer
Designer
Model
Model
(1)
(1)
Transform
Transform
(7)
(7) DL DL
ODL
Search
Java
Wrapping
import
Component
Pool
Browse .
StateChart
StateChart
Component
Component
Designer
Designer
Model
Model
Pool
Pool
(8)
(8)
XPATH/JDOM
XPATH/JDOM . .
Transform
Transform
(2)
(2)
Scenario
Scenario . .
Synthesis
Synthesis . .
(9)
(9)
5SGen
Java
Java
XMI:Class
XMI:Class
Deterministic
Deterministic
Model
Model
ODL
ODL
(3)
(3)
FSM
FSM
(10)
(10)
Search
Search
Wrapping
Wrapping
Xmi2Java
Xmi2Java
(4)
(4)
import
import
SMC
SMC
(11)
(11)
Java
Java
Java
Java
ODL
ODL
Finite
Finite
JSP
JSP
Java
Java
Browse
Browse
import
import
binds
binds
User
User
Classes
Classes
State Machine
State Machine
Class
Class
Interface
Interface
Wrapping
Wrapping
Model
Model
(5)
(5)
Controller
Controller
(12)
(12)
View
View
(13)
(13)
Generated DL Services
Generated DL Services

29. The XML Log Format Log Transaction SessionId MachineInfo Timestamp
Statement
Event
SessionInfo
RegisterInfo
ErrorInfo
StatusInfo
Action
Search
Browse
Update
StoreSysInfo
SearchBy
QueryString
Collection
Catalog
Timeout
PresentationInfo

30. Quality and the Information Life Cycle

31. Rao Shen’s Preliminary Exam: Hypothesis and Research Questions
The 5S framework provides effective solutions to DL integration.
Formally define the DL integration problem?
Guide integration of domain focused DLs?
How to formally model such domain specific DLs?
How to integrate formally defined DL models into a union DL model?
How to use the union DL model to help design and implement high quality integrated DLs?
Assess the integration?

32. DL interoperability approach
Related Work
DL interoperability approach
Intermediary-based
mapping-based
Consists of
Interrelated with
mediator
wrapper
agent
use
schema mapping
use
two architectures
federation
Union Archiving
used in
Consists of
hybrid mapper
composite mapper
use
SemInt
has an example
LSD
has an example

33. DL integration formalization
based on
DL interoperability approach
Intermediary-based
mapping-based
Consists of
Interrelated with
mediator
wrapper
agent
use
schema mapping
use
two architectures
federation
Union Archiving
used in
Consists of
hybrid mapper
composite mapper
use
trained by GA

34. Formal Definition of DL Integration
DLi=(Ri, DMi, Servi, Soci), 1 i n
Ri is a network accessible repository
DMi is a set of metadata catalogs for all collections
Servi is a set of services
Soci is a society
UnionRep
UnionCat
UnionServices
UnionSociety

35. Architecture of a Union DL
   
Archaeologists
General Public
Union Society
 
archaeologists
Society
 
General Public
Society
Union Service
Service
Service
Harvesting, Mapping,
Searching, Browsing,
Clustering, Visualization
Searching
Browsing
Union
Catalog
Catalog1
Catalog2
Union
Repository
Repository1
Repository2

36. Example of Union Service: CitiViz

37. CitiViz: A Visual User Interface to the CITIDEL System
ECDL 2004, Bath, England, September 2004
Nithiwat Kampanya, Rao Shen, Seonho Kim, Chris North, and
Edward A. Fox

38. A Minimal DL in the 5S Framework
Streams
Structures
Spaces
Scenarios
Societies
Structured Stream
Structural Metadata Specification
services
Descriptive Metadata Specification
indexing
browsing
searching
hypertext
Digital Object
Metadata Catalog
Collection
Repository
Minimal DL

39. A Minimal ArchDL in the 5S Framework
Streams
Structures
Spaces
Scenarios
Societies
indexing
browsing
searching
services
hypertext
Structured Stream
Descriptive Metadata specification
ArchDO
ArchObj
ArchColl
SpaTemOrg
StraDia
Arch Descriptive Metadata specification
Minimal ArchDL
Arch Metadata catalog
ArchDColl
ArchDR

40. ETANA-DL Metadata Format
5SGraph
5S Archaeology
MetaModel
ArchDL Expert
ArchDL Designer
VN Metadata Format
ETANA-DL Metadata Format
HD Metadata Format
Scenario
Sub-model
Structure
Sub-model
Mapping Tool
ETANA-DL
Union Services
Descriptions
Harvesting
Mapping
Searching
Browsing … VN
Catalog HD
Wrapper4VN
Wrapper4HD
Inverted Files
XOAI
Search
Service
Web Interface
5SGen
Component
Pool
Browsing …
Index
Union
Catalog
Browse DB
Index
Browse
Service
Services DB
Other
ETANA-DL
Services
XOAI

41. Computing and Information Technology Interactive Digital Educational Library (CITIDEL)
Domain: computing / information technology
Genre: one-stop-shopping for teachers & learners: courseware (CSTC, JERIC), leading DLs (ACM, IEEE-CS, DB&LP, CiteSeer), PlanetMath.org, NCSTRL (technical reports), …
Submission & Collection: sub/partner collections 

42. www.CITIDEL.org Led by Virginia Tech, with co-PIs:
Fox (director, DL systems)
Lee (history)
Perez (user interface, Spanish support)
Students: Ryan Richardson, Kate McDevitt, Jon Pryor, Baoping Zhang
Partners
College of New Jersey (Knox)
Hofstra (Impagliazzo)
Villanova (Cassel)
Penn State (Giles) 3 3

43. Digital library architecture for local
and interoperable CITIDEL services

46. CITIDEL Technology Features
Component architecture (Open Digital Library)
Re-use and compose re-deployable digital library components.
Built Using Open Standards & Technologies
OAI: Used to collect DL Resources and DL Interoperability
XSL and XML: Interface rendering with multi-lingual community based translation of screens and content (Spanish, …)
Perl: Component Integration
ESSEX: Search Engine Functionality
Very fast, utilizing in-memory processing
Includes snap-shots for persistence
Multi-scheming (Aaron Krowne, now at Emory U. Library)
Integrates multiple classifications / views through maps, closure
Extensions: clustering, visualization, personalization, …

47. Cluster Search Results from CITIDEL

48. Cluster NDLTD-Computing

49. Naren Ramakrishnan and Saverio Perugini (U. Dayton)
CITIDEL + PIPE
Adds Interaction Personalization to CITIDEL
Automatically handles multi-modal conversion to Cell phone, PDA, Etc.
Can be adopted to any digital data set, only requires XML file of content with hierarchy maintained.

50. OCKHAM Library Network (NSDL)

51. OCKHAM (Ming Luo) Simplicity (a la OCCAM’s razor)
Support by Mellon and DLF
Four main ideas:
Components
Lightweight protocols
Open reference models (e.g., 5S, OAIS)
Community perspective and involvement
Funded by NSF in NSDL, with P2P, with Emory, Notre Dame, Oregon State, …

52. OCKHAM Proposed Services
Alerting
Browsing
Cataloging
Conversion
OAI – Z39.50
Pathfinding
Registry
(plus others such as from adapted ODL)

53. A Digital Library Case Study
Project:
Networked Digital
Library of Theses
& Dissertations
(NDLTD)
(supported by
Ming Luo)
Domain: graduate education, research
Genre:ETDs=electronic theses & dissertations
Submission:
Collection:

57. OCLC SRU Interface => Dr. A.K. Tyagi

60. ETD Union Search Mirror Site in China (CALIS) (http://ndltd.calis.edu.cn – popular site!)

61. LOCKSS Extensions: Bing Liu, Xiaoyu Zhang, Ji-Sun Kim
Lots of copies keep stuff safe
Stanford (Vicky Reich)
Initial focus on lower levels, journals
Shift to OAI, esp. for ETDs
Collab with Emory (Martin Halbert)
NDIIP: AmericanSouth, MetaArchive
Help deploy and adapt, apply in other contexts
Another registry
Set of publisher manifests (information providers)
Set of storage systems (archival storage)

62. Hussein Suleman (Capetown, S. Africa)
Program
Document
Image
Video
XPMH OA
PMH
open digital library

63. Example Open Digital Library
Program
Document
ETD-1
ETD-2
Image
ETD-3
Video
ETD-4
ODLRecent
USER INTERFACE
Recent
ODLUnion
PMH
Filter
PMH
ODLUnion
Browse
Union
PMH
ODLBrowse
PMH
ODLUnion
Filter
PMH
Search
ODLSearch
ETD DL for the Networked Digital Library of Theses and Dissertations (
Students and
researchers
ETD collections

64. Open Digital Library Deployments
NDLTD (
Computer Science Teaching Center (
Computing and Information Technology Interactive Digital Educational Library (
Open Archives Distributed (NSF, DFG) – enhancements to PhysNet
OCKHAM
Open to others through DL-in-a-box

67. Virginia Tech, Blacksburg, VA 24061 USA
Interest-based User Grouping Model for Collaborative Filtering in Digital Libraries
7th ICADL 2004
Shanghai, P.R. China
Dec. 15, 2004
Edward A. Fox, Seonho Kim
Virginia Tech, Blacksburg, VA USA

68. Some Other Students/Projects
Wensi Xi: Matrices, reinforcement, clusters (Microsoft)
Paul Mather: mod/sim of large DLs on clusters; characterization: uses, files (NASA)
Ming Luo: personalization aided by demographics
Ryan Richarson: CLIR with concept maps
Xiaoyan Yu: Stepping Stones and Pathways (NSF, Fernando Das Neves completed & returned to Argentina)
Baoping Zhang: Physics and classification (NSF, DFG)
Several: TREC with GP
New projects:
Superimposed information w. PSU (NSF NSDL)
Quality and metasearch and structure w. Emory (IMLS) …

69. Conclusion Many DL/IR: areas, projects, students Theory Architecture
Modeling and simulation
Systems development and testing to: validate above, demonstrate innovations
Users, interfaces, visualization, usability
Special thanks to AOL for 4 years of Fellowships!

70. Further - Install Citeseer in the small (from PSU)?
PNNL visualization tools (from Battelle)?
Develop
Proposers assistant?