Digital Library Service Integration (DLSI)--> Looking for Collections and Services to be DLSI Testbeds <--

Michael Bieber, Il Im, Yi-Fang Wu Xin Chen, Dong-ho Kim, Nkechi Nnadi, Prateek Shrivastava

Information Systems DepartmentCollege of Computing Sciences

New Jersey Institute of Technology

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Lexical Analysis

Two Kinds of Links

(1) Structural Links based on object type

(2) Links based on lexical analysis

Why Integrate with DLSI?

•Users gain direct access to related systems

– enlarges your system’s feature set

•DLSI leads users to your system

– your system gains wider use

•Users become aware of other systems

– your system gains wider awareness

•Direct access to your system’s features

– adds streamlined access

Structural Links:

Based on Object TypeExample: document• link to author information

• link to all locations for this document

• link to peer reviewing for this document

Example: concept• link to definition

• link to related concepts

Example: every object• link to discussions about this object

• link to comments about this object

• link to service for starting a discussion, comment, etc.

Issue: Generalizing Services

Looking for services to generalize and share among collections!

Example: peer review• originally designed for 3 reviewers and anonymous

• how to generalize for another collection wanting 5 reviewers and not anonymous

Collaboration Opportunity

DLSI Integration ArchitectureServices and collections integrate with minimal or no changes.

They also continue to operate independently of DLSI.

To Integrate a Collection or Service with DLSI:• Write a wrapper for the collection/service• Initiate communications between

collection/service & the wrapper• Define relationship rules for generating links

Collaboration Opportunity: We’ll

help youdo this!

Dashed paths indicate that once integrated, collections and services can share features through DLSI links automatically.

AVC Collection WIKI ServiceCore Search &

Discovery Service

User`s WebBrowser

Core SearchWrapper

Collection (i) Service (j)

Digital Library Service Integration Manager

AVC CollectionWrapper

CollectionWrapper (i)

WIKI ServiceWrapper

ServiceWrapper (j)

DLSI

Core Search &Discovery Service

Here are some examples. The agricultural system is verycomplex. It consists of farmers in interaction with the envi-ronment (weather, soil, pests), the economy and society.One problem currently receiving a lot of attention isIntegrated Pest Management.

The problem unfortunately is being addressed separatelyby experts in Plant Pathology, Entomology, Agronomy, Bot-any, and Soil Science, with no real common ground (and lit-tle common understanding) to base our decisions on.Meetings and workshops tasked with integration have takenplace, but these various subdisciplines could not seem toform an integrated viewpoint. No real methodology existsto discuss and analyze the systems, which eachsubdiscipline has developed. In the end, all IPM decisionsend up unintegrated and therefore only partially effective. Asystematic approach that would enable the different partici-pants to discuss, and determine all the interrelationships,which would help researchers link their practices and derivetheir effects on each others area and the environment,would greatly help the IPM research and lead to better deci-sion for the environment and society as a whole.

Thus, developing a systems methodology for understandingthe relationships, cause and effects, and repercussions inthe agricultural field would be very helpful. The research inthis incubation proposal would make an important contribu-tion towards this goal.

i

Deeply Understanding Complexity{document}

View Peer Review Comments{JESSE Peer Review service}

Enter your own Peer Review Comment{JESSE Peer Review service}

Search for similar/related documents{Core Search service}

Other collections with this document{DLSI Collection Registry}

Create a new comment on document{Core Annotation service}

Add document to current Guided Tour{DLSI Guided Tour service}

Start your own link from this document{DLSI Link service}

Plant Pathology {concept}

Ask an expert about this concept{in the Virtual Reference Desk}

Relevant NASA Experiments in Space{ National Space Science Data Center}

Search for this concept{Core Search service}

View Comments on this concept{Core Annotation service}

Create a new comment on this concept{Core Annotation service}

Guided Tours concerning this concept{DLSI Guided Tour service}

Start your own link from this concept{DLSI Link service}

Start a discussion on this concept{DLSI discussion environment}

Related journal articles{in JESSE}

DLSI Integration: What Users SeeDLSI automatically generates links to related collections and services.

Linksgenerated to the concept “Plant

Pathology”

Linksgenerated to the document as a

whole

Our Concept Hierarchy Developer uses indexed noun phrases and their co-occurrences in the text to develop document-set dependent concept hierarchies for faster browsing and navigation.

Purposes:1. To identify concepts that are not recognized by structural analysis2. To organize concepts and link them to relevant text for passage retrieval

Lexical Analysis and Concept Extraction

Noun Phrase Extractor parses documents to find noun phrases by using syntactic rules and Wordnet lexical database.

Wu JCDL Workshop 1 3

Returned Documents

Wu JCDL Workshop 1 3

Wu JCDL Workshop 1 3

Concept Organization and Linking

Upon selecting a term of interest, a user first sees relevant paragraphs. This saves the user’s time by filtering out irrelevant parts of a long document. For more information, fulltext is also available.

Relevant Paragraphs display mode Fulltext display mode

Collaborative Filtering:Customizing the Set of Generated Links

Purposes:1. To present links most relevant to current user’s task2. To reduce information overload by reducing the number of links presented

Collaborative Filtering

Evaluation Acquisition Engine

Collaborative Filtering Engine EvaluationDatabase

“Computerized Word-of-Mouth”: Finds people with similar tastes/interests and utilizes their evaluations to estimate the likelihood that the current user would like an item.

1. Calculate degree of similarity between the current user and other users.

2. Identify a group of people (Reference group) who share common interests with the current user.

3. Calculate estimated evaluations for items that the current user has not seen (or evaluated). An estimated evaluation predicts the current user’s evaluation on an item.

4. Rank order the items according to the estimated evaluations and select the top n items to recommend to the current user.

Collaborative Filtering in DL Service Integration

Three types of data to be used as users’ evaluations: Direct evaluation Clickstream (a sequence of mouse clicks) Time spent for each link

Multiple needs (multiple contexts) will also be supported

Collaborative Filtering Architecture in DL

DLSIIntegration Manager

Sorted list Unsorted listRecommendation request

ClickstreamEvaluations

Time information

Inferredevaluation

Digital Libraries