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

1. Links generated to the document as a whole Links generated to the concept “Plant Pathology” DLSI Collaboration Opportunity 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 you do this! Michael Bieber, Il Im, Yi-Fang WuXin Chen, Dong-ho Kim, Nkechi Nnadi, Prateek ShrivastavaInformation Systems DepartmentCollege of Computing SciencesNew Jersey Institute of Technology DLSI Integration: What Users See DLSI automatically generates links to related collections and services. Digital Library Service Integration (DLSI)--> Looking for Collections and Services to be DLSI Testbeds <-- Collaborative Filtering:Customizing the Set of Generated Links 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 • Purposes: • To identify concepts that are not recognized by structural analysis • To organize concepts and link them to relevant text for passage retrieval • Purposes: • To present links most relevant to current user’s task • To reduce information overload by reducing the number of links presented Lexical Analysis and Concept Extraction Noun Phrase Extractor parses documents to find noun phrases by using syntactic rules and Wordnet lexical database. Collaborative Filtering “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. Returned Documents Two Kinds of Links (1) Structural Links based on object type (2) Links based on lexical analysis Collaborative Filtering in DL Service Integration Calculate degree of similarity between the current user and other users. Identify a group of people (Reference group) who share common interests with the current user. 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. Rank order the items according to the estimated evaluations and select the top n items to recommend to the current user. Concept Organization and Linking DLSI Integration Architecture Services and collections integrate with minimal or no changes. They also continue to operate independently of DLSI. Structural Links: Based on Object Type Example: 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. 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. • 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 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. Collaborative Filtering Architecture in DL Digital Libraries Core Search & Discovery Service Clickstream Evaluations Time information Relevant Paragraphs display mode Fulltext display mode Dashed paths indicate that once integrated, collections and services can share features through DLSI links automatically. DLSI Integration Manager Evaluation Acquisition Engine 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 Inferred evaluation Unsorted list Recommendation request Sorted list Evaluation Database Collaborative Filtering Engine