irish digital libraries summit

Copyright 2006 Digital Enterprise Research Institute. All rights reserved.

www.deri.ie

Irish Digital Libraries SummitDigital Libraries at the eve of the Next Generation Internet

Sebastian Ryszard Kruk, Mary Burke, Stefan Decker

http://wiki.corrib.deri.ie/index.php/SemDL/IrishDLSummit

2

Looking into the Future of Irish Digital Libraries

?

3

Why do we care?

• John teaches biology, over the Internet, using digital libraries and modern technologies (wikis, blogs)

• How to deliver the material just-in-time?• How to pre-asses students?• How to automate most of the process?

4

Goals

• Present current solutions that digital libraries to the Next Generation Internet

5

Goals

• Gather opinions, requirements and future plans of Irish libraries

6

Goals

• Build up bases for an application for funding of a national digital libraries initiative under the EU FP7 Digital Libraries theme

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Schedule

10:00-10:30

Get together, Welcome Sebastian KrukMary Burke

Semantic Digital Libraries

10:30-11:00

Ontologies for Digital Libraries Maciej Dąbrowski

11:00-11:30

Building a Semantic Digital Library Tomasz Woroniecki

11:30-11:50

Coffee break

Future of Digital Libraries

11:50-12:00

Introduction to the session Sebastian Kruk

12:00-12:30

IBM Ontological Network Miner and its applications to semantic social networks

Alexander Troussov

12:30-13:00

BRICKS Project Predrag Knezevic

13:00-14:00

Lunch break

8

Schedule

Digital Libraries in Ireland

14:00-14:30 The Irish Virtual Research Library and Archive Project – an infrastructure for humanities research.

John McDonough

14:30-15:00 OJAX: A Web 2.0 Search user Interface Judith Wusteman

15:00-15:30 With a Little Help from My Friends: Social Semantic Search and Browsing

Sebastian Kruk, Adam Gzella

15:30-15:45 Coffee break

15:45-16:45 Discussion panel:

Do we need Semantic Web and Web 2.0 technologies in Digital Libraries?

Mary Burke

16:45-17:00 Wrap-up, Conclusions


www.deri.ie

Ontologies for Digital Libraries

MarcOnt Initiative

Maciej DąbrowskiDigital Enterprise Research Institute

National University of Ireland, Galway

[email protected]

10

Outline

• Real-life and Semantic Web

• Semantic Web and Ontologies

• MarcOnt Ontology

• MarcOnt Tools

• Conclusions

11

Real-life problems

Heterogenous systems

Identified Problems:• Interoperability• Format translation

Multiple data formats in DL:• How to support them?• How to translate between them?• Who should create mappings?

Bibtex

MARC21

Dublin Core?

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Real-life problems – user’s expectations

Searching:

• Effective and AccurateWe want correct and fast answers!!

• Intuitive and SimpleAsking questions should be easy.

• MeaningJaguar – a car or an animal?

• ReasoningGive me articles written by students of X in Galway?

Identified problems:

• Intuitive interface for asking complex querries

13

Real-life problems - summary

Digital Libraries should provide:

• Interoperability

• Support for many formats

• Complex search features

• Intuitive interfaces

15

The Semantic Web – A Brief Introduction

• Current Web vs. Semantic Web?– An extension of the current Web in which information is given well-

defined meaning, better enabling computers and people to work in cooperation. [Tim Berners-Lee]

– Current Web was designed for humans, and there is little information usable for machines

• Was the Web meant to be more?– Objects with well defined attributes as opposed to untyped hyperlinks

between Internet resources– A network of relationships amongst named objects, yielding unified

information management tasks

• What do you mean by “Semantic”?– the semantics of something is the meaning of something– Semantic Web is able to describe things in a way that computers can

understand

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Semantic Web vs. Current Web

Current Web Semantic Web

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The Semantic Web – What is RDF?

Describing things on the Semantic Web– RDF (Resource Description Framework)

• a data format for describing information and resources, • the fundamental data model for the Semantic Web

– Using RDF, we can describe relationships between things like:• A is a part of B or• Y is a member of Z• and their properties (size, weight, age, price…) in a machine-understandable

format

– RDF graph-based model delivers straightforward machine processing

– Putting information into RDF files makes it possible for “scutters” or RDF crawlers to search, discover, pick up, collect, analyse and process information from the Web

18

The Semantic Web – What is RDF?

A simple RDF example– Statement:

“Stefan Decker is the creator of the resource (web page) http://www.stefandecker.org”

– Structure:Resource (subject) http://www.stefandecker.org

Property (predicate) http://purl.org/dc/elements/1.1/creator

Value (object) “Stefan Decker”

– Directed graph:

http://www.stefandecker.orgdc:creator Stefan Decker

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The Semantic Web – How RDF can help us?

How RDF can help us?• identify objects

• establish relationships

• express a new relationship just add a new RDF statement

• integrate information from different sources copy all the RDF data together

• RDF allows many points of view

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Ontologies

• What is an Ontology?„An ontology is a specification of a conceptualization.“

Tom Gruber, 1993

• Ontologies are social contracts– Agreed, explicit semantics

– Understandable to outsiders

– (Often) derived in a community process

• Ontology markup and representation languages:– RDF and RDF Schema

– OWL

– Other: DAML+OIL, EER, UML, Topic Maps, MOF, XML Schemas

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Components of ontologies

Concepts• Book• Article• Author

Properties• hasPages• hasTitle

Constraints• Cardinality is at least 1• Maximum value is 200

Axioms• Planes can fly• People can’t fly

Relationships• Is a• Part of

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Ontologies - half-time conclusions

• Data is not only human readable, it is now also machine readable

• Machines can realize much more complex tasks (eg. reasoning)

• Capturing the meaning of concepts is possible

• A new look on data storage systems (there are no data structures!!)

A d

v a

n t

a g

e s

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Usecase scenario

Author

Title

Structured resources:• Author• Title

Data storage allows:• Author • Title

Additional information

cannot be stored!!

Author

Title

Date

TitleAuthor

Regular Systems

Author TitleDate

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Ontology development process

• Many approaches

• Different life cycles

• Continuous process

• Involves community of users

• Requires tools for collaboration

• Tools for ontology development are necessary

D e

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MarcOnt Initiative

Motivation:

• Build a bibliographic ontology for the Jerome Digital Library

MarcOnt Initiative goals:

• Deliver a set of tools for collaborative ontologydevelopment

• Collaboration

• Tools for domain experts

• Enable mediation between formats (MMS)

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MarcOnt Ontology

• Central point of MarcOnt Initiative

• Translation and mediation format

• Continuous collaborative ontology improvement

• Knowledge from the domain experts

• Community influence and evaluation

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MarcOnt Ontology

Goals:

• Capture concepts from the legacy bibliographic formats– MARC21, Bibtex, Dublin Core– Lattes, ...

• Create a uniform bibliographic description format for digital libraries.

• Enable the use of Semantic Web technologies (eg. reasoning) to improve capabilities of digital libraries

• Improve interoperability

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Format Translation Scenario

Author:John SmithDate of Birth:1956-10-15Date of death:2004-09-10

Author:John SmithDate of Birth:??Date of death:??



Dublin Core

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Format Translation Scenario





RDF Storage

Dublin Core



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MarcOnt Mediation Services

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MarcOnt Mediation Services

MarcOnt OntologyMarcOnt RDF

MARC21 RDF

MARC21 XML

MARC21

Dublin Core RDF

Dublin Core XML

Dublin Core

New format RDF

New format XML

New format

Format translationInteroperability

MarcOnt Mediation Services RDF Translator

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MarcOnt Ontology in JeromeDL

• Improvement of searching capabilities• Natural Language Processing (NLP)• Templates

Show me all publications written by students of Decker.

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MarcOnt Portal

Collaborative ontology development.

Portal provides:• Suggestions• Annotations• Versioning• Ontology editor

Sugested Poposals

Initial Ontology

Proposal discussion

Proposal anotations

Proposal votingProposal autopromoting

Versioning

Next RevisionMarcOnt Portal

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MarcOnt Portal

On-line ontology editing Visualization of ontologies

35

MarcOnt Portal

Comparing versions of ontologies

36

MarcOnt Initiative Roadmap

• Lattes – CV platform used in Brasil• Release of MarcOnt draft ontology• Digital Rights Management• Sharing issues

• MarcOntX agent – automatic integration of concept from Digital Libraries

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MarcOnt Initiative summary

MarcOnt Initiative goals:

• Create a framework for collaborative ontology development

• Provide domain experts with tools to share their knowledge

• Offer tools for data mediation between different data formats

• Develop MarcOnt bibliographic ontology

• Create a community of users (domain experts)

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Conclusions

Ontologies:

• can improve the most important goal of digital libraries – searching the information

• facilitate interoperability

• capture much more information (metadata) than existing systems

• are the agreement of people (domain experts)

• need tools for collaborative development and community of users

• are the future of Digital Libraries?


www.deri.ie

Tomasz [email protected]

JeromeDL

Building a Semantic Digital Library

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Outline of the presentation

• Introduction to Semantic Digital Libraries• Overview of JeromeDL• Architecture of JeromeDL• Working with JeromeDL• Demo

Social Semantic Digital Library

• A library stores and provides access to resources (books)

• Qualified staff updates catalogues and helps users


• Machine-readable resources

• Full-text index improves searching

• Easy access

• Availability


• Resources are accessible by machines, not with machines

• Metadata is rich and extensible

• Searching reflects meaning of terms

• RDF is a standard for representing information

• Not just resources but also knowledge is shared


• Involves the community into sharing knowledge

• Utilizes social network in searching

• Allows for comments, blogs, shared bookmarks

• Easy tagging

Evolution of Libraries


Involves the community into sharing knowledge

Semantic Digital Library

Accessible by machines, not only with machines

Digital Library

Online, easy searching with a full-text index

Library

Organized collection

Semantic Digital Library

Semantic digital libraries– integrate information based on

different metadata, e.g.: resources, user profiles, bookmarks, taxonomies

– provide interoperability with other systems (not only digital libraries)

– deliver more robust, user friendly and adaptable search and browsing interfaces empowered by semantics

JeromeDL - Motivations

• Support for different kinds of bibliographic medatata, like: DublinCore, BibTeX and MARC21 at the same time.– Making use of existing rich sources of bibliographic descriptions (like

MARC21) created by human.

• Supporting users and communities:– users have control over their profile information;

– community-aware profiles are integrated with bibliographic descriptions

– support for community generated knowledge

• Delivering communication between instances:– P2P mode for searching and users authentication

– Hierarchical mode for browsing

JeromeDL – Social Semantic Digital Library

JeromeDL fulfills requirements of:

• Librarians– precise annotations– rich metadata

• Researchers– easy publishing– searching related topics

• Average users– efficient search and browsing– online collaboration

JeromeDL - Architecture

Ontologies in JeromeDL

Using JeromeDL

• Uploading a resource

– provide title, abstract, author etc.

– provide structure of the resource (e.g., chapters)

– choose domains of the subject

– choose keywords for the resource

– set additional properties

– upload digital parts of the resource

Using JeromeDL

Using JeromeDL

• An administrator either approves or rejects a published resource

Sharing bookmarks

JeromeDL for a regular user

• Browsing resources

– by type, author, keyword, domain

• Downloading the resource and its bibliographic description in various formats

• Subscribing to RSS feeds

• Searching

– simple, advanced, distributed, semantic

JeromeDL for a regular user

Summary

• An easy solution for putting resources online• A community around your repository• Support for many languages• Integration with Bibster and OpenSearch protocols

Visit www.jeromedl.org


www.deri.ie

Irish Digital Libraries SummitIrish Digital Libraries SummitDigital Libraries at the eve of the Next Generation Internet

Future of Digital Libraries


59

Looking into the Future of Irish Digital Libraries

?

60

Building the Future

• Future Internet, semantic or social, or both, will not emerge on its own, we need to build it

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Building the Future

• Digital libraries are important part of the Internet

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Building the Future

• Libraries should continue to drive the changes, not only follow

63

Building the Future

• OnNeM - IBM Ontological Network Miner and its applications to semantic social networks

• BRICKS Project – Building Resources for Integrated Cultural Knowledge Services

IBM CAS Dublin / LanguageWare group

Ontological Network Minerand its applications to models of social networks and semantics

Alexander Troussov, Mikhail Sogrin, John Judge

Agenda

Ontological Network Miner tool (project Galaxy) As generic tool to perform elements of soft clustering and fuzzy

inference on semantic networks

Applications of Galaxy to ontology-based semantic analysis of texts

– Semantic tagging, term disambiguation based on the global context

Galaxy applications to folksonomies– Community detection/Expertice location, …

Applications to unified models of semantic social networks

Research cooperation

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Ontological Network Miner (Galaxy) A generic tool to perform elements of soft clustering and

fuzzy inference on semantic networks Ongoing project based on the work we have done for

EU 6th framework integrated project Nepomuk

Applications to metadata generation

Applications to metadata generation

Currently the semantic web relies on semantic annotation mostly done manually by humans

Working in EU 7th framework project Nepomuk (which aims to build social semantic desktop) we in IBM Dublin developed a tool for automation of metadata creation: Automatic ontology-based conceptual tagging

(central concepts of the text with respect to the given lexico-semantic resource)

– Text which mentions Mulhuddart, Lansdowne, Clontarf is probably about Dublin/Ireland/Europe/Earth, this fact can be inferred from geographical relations like Mulhuddart “is-part-of” Dublin

Disambiguation of terms– Based on on the ontological knowledge from corresponding resource

(Jaguar – a car or an animal? Jaguar, car, animal, pet, …)

…

Automatic tagging based on concept mentions

NETWORK OF CONCEPTS

TEXT

Mention Mention Mention Mention

Mapping of term mentions to concepts .

Finding “focus” concept

DEMO (Lotusphere 2007) Run eclipse.exe Open lotusphere_demo.config.xml located in subfolder data Have a look at the underlying personal information management ontology

people, organisations, projects,

Open text: email1.anno Text is processed on the fly, terms are disambiguated, central concepts

are shown in the upper-right window Why US? Because most found concepts are people, and during disambiguation it was

established that most likely referents of (ambiguous) names are located in US Let us remove first line with two names

– The text now has less names. Instead of people, other (abstract) concepts now play a more prominent role. Because of this (after a small delay caused by Eclipse, not by the performance of our system) US disappears as the top concept

What is Ontological Network Miner? Text analytics demo shown before has applications to:

Context dependent smart tags Metadata generation

Although text processing is a complex process involving mapping from text to concepts and usage of empirics specific to

certain properties of the discourse

at the heart of the processing is clustering on the graph of concepts Which was shown by the animation when wide orange area becomes smaller

after “magical” shrinking This clustering is provided by IBM Dublin Ontological Network Miner

Codenamed OnNeM in Nepomuk project

What exactly Ontological Network Miner does? One algorithm (a blend of soft clustering & fuzzy inference) Depending on the parameters, this algorithm provides

“Generalisation” of the model– Output has less nodes compared to the input

“Expansion” of the model– Which might be used for query expansion:

• Query “nutrion”+”science” is expanded into properly ranked list: » nutritionist, dietologist, nutritional, scientific, ..

Our customers and partners can tune the algorithm for specific tasks using intuitively clear parameters.

Tuning Galaxy Galaxy utilises a data-driven algorithm and more importantly, tuning can

be done by a domain specialist (not necessarily a researcher or software developer), is to “tell” Galaxy what properties of the underlying semantic network are relevant to a particular task: For example, in application to geotagging the user might specify that Galaxy

favour geographical locations with bigger populations, and, in addition, favour popular resorts

Using WordNet – specify that Galaxy must favour hypernymy-hyponymy relations and disfavour meronymy-holonomy relations

Researchers (IBMers and CAS scientists) also have the opportunity to work with us on “fine-tuning” the algorithm For example, to improve usage of graph-metrics such as in-/out-

degree of nodes

Applications to folksonomy systems(Del.icio.us, IBM’s Dogear, …)

Folksonomies as ontological networks PeoplePeople DocumentsDocuments TagsTags

Instances of Instances of taggingtagging

Why a “generic” ontological network miner is needed:

Objects of interest might be wired into one unified model of lexicon, semantic and social networks

For example, the network depicted on the previous slide can be augmented with new entities and new relations One can add relations between participants, or add new people into consideration Semantic relations between tags might be added manually, or generated automatically

based on morphological similarity of words, proximity in WordNet, etc. Keywords and other metainformation about documents might be wired into the network

– Tags in folksonomies are created by humans. Keywords (preexisting in documents or extracted by text processing) and their relations to documents and tags might be added to augment folksonomies.

• Dogear can recommend tags for new document which nobody yet tagged in a style accepted in the community

Why a “generic” engine like Galaxy is needed: (cont)

Unified model of lexicon, semantic and social networks gives more context to make the right decisions in Community Detection, Community Structure Analysis, Metadata Sharing & Recommendations, etc

However, data network becomes quite intricate and irregular, and only generic, scalable and high-performing ontological network miners (like Galaxy) are up to the job

Galaxy is a generic technique, which can efficiently work on huge networks with complex topology Most tasks on MeSH and WordNet are done in 200 msc

Galaxy has native potential for explanatory module “This person might help you to understand this document because he frequently used

tags popular for this documents”

OnNeM can handle Networks like this:

PeoplePeople DocumentsDocuments TagsTags

Instances of Instances of taggingtagging

New people and New people and additional additional relations relations between thembetween them

Relations between Relations between tags: semantic tags: semantic proximity, proximity, misspellings, misspellings, translations, WordNet, translations, WordNet, ……

Relations between Relations between documents: …documents: …

New objects: e.g. New objects: e.g. keywords from texts keywords from texts might be related with might be related with documents and tagsdocuments and tags

Applications to Semantic Social Networks & Knowledge Exchange

What problems Galaxy can address Galaxy could be used to uniformly address many problems in

Semantic Social Networks & Knowledge Exchange: Tag recommendation in folksonomies; Community detection; Centrality problem in

social network analysis; Expertise location…

How? Galaxy is a generic technique: which takes as input a function on nodes of a semantic

network and transforms this input into another function according to the parameters. To simplify explanations, instead of the input/output functions, we’ll talk about the input set of nodes and ranked output set of nodes

To create solution for a particular task – A set of input nodes must be chosen– Parameters of the algorithm must be established – Output set must be interpreted according to the task

81

IBM social software “the company is serious about dominating social networking for the

enterprise” Cooking Up a Social Networking Storm With IBM Labs, March 30, 2007

IBM Social Software

Dogear– Dogear is a social-tagging service for resources such as public URLs, company-internal

URLs, and other company internal documents (e.g., Wiki pages, Domino documents, etc.) Bluepages+1

– is an enhanced version of IBM online employee directory. Among its enhancements is the ability for one person to apply a tag directly to another person’s directory page.

Blog Central– Blog Central is an internal blogging service, open to any employee. The Blog Central data

structures provide for a separate list of tags for each blog and for each entry within each blog.

Activities– Activities is a web-based version of ActivityExplorer, an activity-centric collaboration service

in which teams may create a collections of diverse objects in a tree-like structure consisting of a root “activity” and its daughter components.

Research collaborationation Create semantic social networks of your interest … in the format which can be used by Galaxy

simple XML format

Design scenario and work with us on tuning parameters of Galaxy for the tasks in your scenario

… Contacts

Alexander Troussov, CAS Chief Scientist, [email protected] Marie Wallace, LanguageWare manager, [email protected] Brian O’Donovan, CAS Program Director, [email protected]

IBM CAS Dublin https://www.ibm.com/ibm/cas/sites/dublin/ LanguageWare http://www.ibm.com/software/globalization/topics/languageware/

index.jsp NEPOMUK http://nepomuk.semanticdesktop.org/

84

Questions?

20/04/07 DERI, Galway, Ireland 85

BRICKS Project

Predrag Knežević

Fraunhofer IPSI Institute

Darmstadt, [email protected]


What is BRICKS?

• A software infrastructure for building digital library networks

– Transparent access to distributed resources

– Multilinguality– Easy installation & maintainance

• A set of end-user applications– Network & content management– Web 2.0 Tagging/Annotations– Domain specific applications

• A business model– Open Source, Platform Independent– Low cost infrastructure– User communities sustainability


• Sustainability– User Communities

– Open Source

BRICKS

• Applications– User App. Build on top of

the foundation

– User Services can become Foundation services

• Foundation/Infrastructure– Decentralized Storage

– Content&Metadata Mngt.

– Semantic Retrieval

– Security/DRM


BRICKS Architecture

• A decentralized P2P network– Avoid central coordination– Highly Scalable, increased reliability– Minimized maintainance costs

• Each P2P Node is a set of SOA components– Web Service Interface– Platform Independent– Flexible Composition

• Components for– Storing, accessing and protecting digital objects– (Semantic) search & browsing– P2P commmunication


Workstation

Workstation

Workstation

Workstation

BNodeEMF

User

User

User

User

Workstation

Workstation

Workstation

Workstation

BNodeAustrian Library

User

User

User

User

BNodeStudio Azzuro

WorkstationUser

WorkstationUser

FhG IPSIRequest

RequestRequest

Request

Req

uest

Request

Accessing Data


A Look into a BNode

BNode{

Applications

BNode

Networking (TCP/IP)

SOAP DHT

SOAP

Dec

entra

lized

XM

L St

ore

Inde

xing

Con

tent

M

anag

emen

t

Met

adat

a M

anag

emen

t

Anno

tatio

n M

anag

emen

t

Col

lect

ion

Man

agem

ent

Use

r M

anag

emen

t

Auth

entic

atio

n &

Auth

oriz

atio

n

Pilla

r Ser

vice

s

Core Bricks Basic BricksFundamental Bricks

DR

M

Inter Bnode Communication


Features

• Application development in any language with a good Web-service support• Metadata

– Support for various schemas– Indexed both locally and published in decentralized index as well

• Annotations– Support for various media types (text, images, audio, video) – Various supported types (text, audio, video, spatial, temporal)

• Content– Can be stored outside of BNode– Internally content can be managed in various binary and structured (XML) formats– Organized into collections– Location transparent for applications

• Search– Simple, advanced, ontology-based– Cross-language support– Addresses all available content


Collection Manager

• Single access point for all content and metadata related operations (local and remote)

• Physical Collection– Similar to folder/directory hierarchy in a file system– Bound to a single BNode– Each digital content object belongs to exactly one collection

• Logical Collection– Virtual folder for organizing content items independent of their physical location – Links to content items from various physical collections on different BNodes– A content item might belong to many of them

• Stored Query similar to database views


Content Manager

• Two ways to handle Content in BRICKS

– stored locally at site of a member party, accessed via URL

– stored within BRICKS

• Based on Java Content Repository (JCR)

• Provide a meta-content model

– Re-use of existing content models

– Use standard models


Metadata Manager

• Metadata descriptions RDF– Suitable for any applícation scenario– Express Relationships between objects– React to changes without changing the

model

• Schema defintions OWL– No fixed schema– Extensible (e.g. Application Profiles)– Semantic concepts instead of

schematic strucutures

• SPARQL– Metadata queries over ontology

concepts– Queries for graph patterns

System Core

Storage (Jena)

Data Transformation

Schema Manager

Metadata Manager

Metadata Record

Metadata Record

Val

idat

or

RDF/XML

Web Service

API (WSDL)

dc.xsl vra.xsl xyz.xsl

XML

OAI-PMH Server

OAI-PMH Harvester

XML

DCVRA xyz

mapping mapping

RDB File-DB

Query Adapter


Annotation Management• Rich model

– Supported fragment types: “Text fragment”, “Time fragment”, “Rectangle”, “Circle”, “Point”, “Polygon” and “Polyline”

– Supported annotation types: “Structured Annotation”, “Association”, “Text annotation” and “Symbol Annotation”

– Annotation type “association” supports n:m relations• Support of versioning• Annotation of complete objects and of fragment of objects• Supports annotation of multiple objects

9513/03/2007


Security Manager

• Transparently invoked by the Framework– any service call is checked

• Context-aware policies based on RBAC (via XACML rules), supporting Roles, Groups, at DLObject level

• Permission declaration through Javadoc @tags• Federated identity is managed through an adapted version of

OpenSAML• Reputation-based Trust calculation integrated• Web-based GUI for Security configuration

9613/03/2007


Digital Rights Management

• DRM Component– Support for licenses based on MPEG-21 REL license declaration standard– Generic API for the integration of commercial DRM systems

• Watermarking– Open-source watermarking tool for images– other tools can be integrated

• BRICKS Store web application for commercial content• Creative Commons support for other content in BRICKS

9713/03/2007


BRICKS APPLICATIONS


Application: BRICKS Workspace • What does it demonstrate?

– a web application (thin client) accessing BRICKS Foundation services– Web 2.0 image

annotations– Reference application

• Primary customers?– general end-users (citizens)– application developers

• Technology– Struts based interface to

the BCH

• Live demo athttp://saturn.researchstudio.at:8090/workspace


Application: BRICKS Desktop • What does it demonstrate?

– a rich client application accessing BRICKS Foundation services– direct access to the BCHN

• Primary customers?– expert end-users

(researchers, educators)– application developers

• Technology– Eclipse based rich

client interface

• Download athttp://develop.bricksfactory.org/projects/desktop


Application: Annotation Tool• What does it demonstrate?

– Tool which allows end-users to annotate images– Creation of annotation threads– Supervised Annotations

• Primary customers?– end-users– Institutions with large

image collections

• Technology– Web Application


Application: Online Exhibition Authoring Tool• What does it demonstrate?

– Creating and publishing online exhibitionsusing contents that is available in the BRICKS network

• Primary customers?– expert end-users

(curators)

• Technology– Web Application

• Live demo athttp://livingmemory.researchstudio.at/


Application: Archeological Finds Identifier• What does it demonstrate?

– a web application for comparing found objects (e.g. ancient coins) with objects from reference collections

– Application of complexdomain ontology (CIDOC-CRM)

– Map visualization of GIS-Metadata

• Primary customers?– Museum curators, archaeologists,

students, amateurs

• Technology– Struts based interface

• Live Demo athttp://finds.brickscommunity.org:8091/findsidentifier/index.do


BRICKS Demo Store• What does it demonstrate?

– Purchasing digital goods– License maintenances

and proofing

• Primary customers– Content providers

• Technology– Based on OFBiz

• Live demo athttp://brstore.metaware.it:9080/ecommerce/control/main


References

• BRICKS Community Web Site (http://www.brickscommunity.org)

• BNode Release Downloads (http://foundation.bricksfactory.org)

• BRICKSforge (http://develop.bricksfactory.org)

• BRICKS Developer Community (http://dev.brickscommunity.org)

http://www.brickscommunity.org/

http://foundation.bricksfactory.org/

http://develop.bricksfactory.org/

http://dev.brickscommunity.org/


www.deri.ie

Irish Digital Libraries SummitIrish Digital Libraries SummitDigital Libraries at the eve of the Next Generation Internet

Digital Libraries in Ireland


107

Building the Future

• IVRLA - The Irish Virtual Research Library and Archive Project - an infrastructure for humanities research.

• OJAX – A Web 2.0 Search user Interface • S3B - With a Little Help from My Friends: Social

Semantic Search and Browsing

Irish Virtual Research Library and Archive Project.

UCD Humanities Institute of Ireland,

Belfield, Dublin 4.

[email protected]

The Irish Virtual Research Library and Archive Project - an infrastructure for humanities research.

109

Outline

• Quick Overview

• Digitisation Processes

• Repository Development

• Content Models

• IVRLA Deployment

• Observations

110

IVRLA Positioning

• PRTLI funded project

• Component of UCD Humanities Institute of Ireland and based in UCD Library

• Supporting research through offering access to digitised content from participating primary source repositories

• Direct research into digitisation and digital repositories

• Developing and promoting added value tools and services

111

IVRLA Deliverables

1. Body of digitised content

2. Functioning repository prototype with scaleable infrastructure

3. Comprehensive report including regulatory and financial issues

• Body of corporate knowledge & expertise

• Centre of excellence

• Proof of concept

112

The vision thing

113

• Support the creation and publication of new forms of “information units”

• Integrate with the processes (e.g., workflows) of research, collaboration, and scholarly communication

• Enable knowledge integration: capture semantic and factual relationships among information entities

• Promote information re-use and contextualization

• Facilitate collaborative activity and capture information that is created as a byproduct of it

• Capture and maintain the complex structural, semantic, provenance, and administrative relationships among digital resources*

*Sandy Payette, Sydney 2006.

Digital content repositories should…

114

Digitisation and Cataloguing Processes

115

Image based Digitisation Components

• Apple PowerMac G5 running Kodak oXYgen Scan

• Kodak IQSmart 2

• Adobe Photoshop CS2

116

Audio Digitisation Components

• Quadriga system

• Lake People ADC and DAC

• Revox 1/4 inch tape player

117

Files and Formats

I. Scanned Material (text and images)• TIFF (PM) • JPEG (CW) Djvu (CW) • JPEG (TN)

II. Time Based Material1. Audio• BWF (PM)• MP3 / MP4 (CW)2. Video• Linear Digital (PM)• mov,wmv? (CW)

118

Workflows

• TIFFs have metadata embedded

• TIFFs are backed up to LTO

• Photoshop macros used to watermark, create JPEG and TIFFs

• DVDs created and stored

• Additional derivatives created for resource discovery and access

119

Data Storage

• 3 high quality ‘Preservation Master’ copies– 2 DVD-ROM - working– 1 LTO - deep archive

• Copies stored in geographically disparate locations

• Estimate that IVRLA will require 6-8TB for all preservation master storage.– Scans ~ 80MB– Audio ~ 800MB/hr

• Online requirement is significantly less

120

Metadata and Database

• 2 stage cataloguing database

• MODS - descriptive metadata

• METS - structural and transmission metadata

• EAD - archival context and structure

• MIX - technical metadata for images

• MADS - descriptive metadata authority files

122

MADS files

123

Collection Model

• Library use OPAC for searching

• Archives use Finding Aid for browsing

• Hybrid model to enable searching and browsing of complex hierarchical digital collections

• Model facilitates top down and bottom up approaches

• EAD provides context and structure

• MODS provides precision and accuracy

• Create EAD template for each ‘collection’

• Catalogue to the appropriate level

124

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

125

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

126

Repository Architecture Articulation

127

Open Source Repository Systems

• Growing area of development

• Several options available; – Dspace – Eprints – Fedora

• IVRLA required a solution which offers; – Suitability for wide range of data types– Support for collection structures and complex

objects– Scalability - prototype into service– Future-proof architecture– Long term digital preservation

128

Fedora Service Framework (2005-07)

Fedora RepositoryServ ice

Fedora Serv ices

Apps

PreservationIntegrity

Exte rn alW o rkflo w

JHO VE

G DF R

FedoraWorkflow

Administrator

PRO AI

(O AI Pro v id e r)

DirectoryIngest

W e b -b ase dsu b missio n an db asic w o rkflo w

FederationPID

ResolutionPreservationM onitoring

EventNotification

FedoraSearch

O penURLAccessPoint

Oth erService

Pathways

I nterDisseminatorS ervice

aDO Re

arX iv

DS pace

Ope

nUR

LO

penU

RL

Ope

nUR

L

Dialog Box Name

O KTex t:

Tex t

Tex t

Tex t

Tex t

Tex t

Cancel

H elp

Sample Text Here Sample Text Here Sample TextHere Sample Text Here Sample Text Here SampleText Here Sample Text Here Sample Text HereSample Text Here Sample Text Here

S am ple Tex t Here S am ple Tex t Here S am ple Tex t Here S am ple Tex t HereS am ple Tex t Here S am ple Tex t Here S am ple Tex t Here S am ple Tex t HereS am ple Tex t Here S am ple Tex t Here S am ple Tex t Here S am ple Tex t Here

FIRE ClientDirIngest PolicyBuilderO penURL

client

© S.Payette

129

IVRLA Preservation Requirements

• Audit trails and datastream versioning

• Persistent Identifiers

• Checksum creation and validation

• Whole object versioning

• OAIS compliance

• TDR compliance

130

IVRLA interface requirements

Evidence• Provenance, authenticity, integrity, context, persistence,

sustainability

• Granularity - directed to page, clip, part ..

• Security, authentication and authorisation infrastructure

Conversation/Participation• Informal, collaborative

• Personalisation and customisation

• Recommendation Services (S/CSI)

• Social searching and annotation (S/CSI - S/ILS)

• Add value, links, connections…

131

Content Models

132

Fedora Content Models

• A definition for a “type” of object (e.g., article, book, image, learning object) that describes the internal composition of a group of similar Fedora objects– Data Type– Structure– Services

• Data Type defines payloads and metadata

• Structure defines relationships between objects

• Services define actions or disseminators for the content

133

Object Type DS Description Comment1 Still Image JPEG image

Thumbnail JPEG imageDJVU image DefaultMODS XML recordDublin Core recordMIX Tech MetadataPreview image Portrait

LandscapeAnnotation(s)

2 Printed Text JPEG imageThumbnail JPEG imageDJVU image DefaultMODS XML recordDublin Core recordPDF of OCRPreview image Portrait

LandscapeAnnotation(s)

3 Audio Object Thumbnail icon Type iconMODS XML recordDublin Core recordPreview icon Type iconMPEG 4 Default for StreamingMPEG 3 for DownloadAnnotation(s)

4 Video Object Thumbnail icon Type iconMODS XML recordDublin Core recordPreview icon Type iconMPEG 21 Default for StreamingAnnotation(s)

5 Dataset Object Thumbnail icon Type iconMODS XML recordDublin Core recordPreview icon Type iconCSV file DefaultAnnotation(s)

136

RoadMap

• Initial Research and Demo

• Develop utilities - sipMaker and mixMaker

• Articulate collection model

• Develop Virtual Library and Archive 1.0– Browse– Search– View– Cite– Tag

• Ingest Trial and deployment of subsets

• Develop Virtual Library and Archive 2.0– User management– Personalisation, customisation– Recommendation services– Annotation and tagging– Research space– Virtual collections

137

IVRLA 1.0?

138

Usership

• Research based

• Context heavy - accuracy, integrity and authenticity

• Technically literate with Internet age expectations - the Google effect

• Accurate citation and source acknowledgement using persistent identifiers

139

Repository Challenges

• Architecture is not an ‘out of box’ solution

• Resources required to articulate and develop interface layer(s)

• Metadata management is complex

• Tension between popular delivery formats and archival preservation formats

• Challenge of anticipating all user environments in content modeling

• Improved automation is necessary for ingest and validation. Digitisation is the main bottleneck

• Sustainability - prototype developed into a service

• Human resources are central to technology projects

• Developing and training data curators - multidisciplinary skill sets

140

Observations and Conclusion

• 5 year project timeline requires an iterative process

• New advances in computing science will influence developments - eScience, eHumanities, Web 2.0

• IVRLA positions the archival source with all context and structure as central to the digital deployment

• Define and build core sources which can be interrogated and integrated with dynamic services

• Standards based interoperability is key to ensure future accessibility and sustainability

• New repository models suggest and support user created metadata such as social bookmarking and annotating

141

Further Information

www.ucd.ie/ivrla

[email protected]

mailto:[email protected]

Scoil an Léinn Eolais agus

na Leabharlannaíochta UCD

UCD School of Information

and Library Studies

OJAX: Web 2.0

Federated search

Judith Wusteman

April 2007

143

Overview

• Introducing OJAX

• OJAX Demo

• Related research

144

Web 2.0 Technologies and Standardsused in OJAX

• AJAX

• REST

• JSON

• Atom

• OAI-PMH

• OpenSearch

• Open API

• StaX

• Apache Lucene

145

http://ojax.sourceforge.net/

146

OJAX

147

OJAX demo

148

Unifying the user interface

149

Auto-completionAuto-searchDynamic archive list

150

Dynamic scrolling

151

Auto-expansion of results

152

Sorting results

153

OpenSearch

154

OpenSearch

• Enables search engines to describe their search syntax to browsers

• Describes standards for search results syntax– Based on RSS and Atom

155

Atom feed support

160

Accessibility

161

Science Foundation Ireland:OJAX++: a next generation collaborative research tool

To investigate how concepts from the Social Web can be applied to the research environment in order to facilitate dynamic collaboration and the sharing of ideas among researchers.

162

PhD starting September 2007

• In collaboration with

UCD School of Computer Science and Informatics

• Requirements – Honours degree (preferably first class or 2.1)

• in Computer Science or a related field• or equivalent technical expertise

• Preferred Experience: – Web technology – JavaScript – AJAX – one of Java, Ruby or Python.

• http://www.ucd.ie/wusteman

• [email protected].

163

Advantages of OJAX

• Developed in Ireland. Can be adapted to suit.

• Already in Beta version. Available for download.

• Well received

• Responds to new user expectations generated by Web 2.0– Rich, dynamic user experience. – Intuitive interface.

• Integration, interoperability and reuse.

• Open source standards-compliance.– including OpenSearch, OAI-PMH, StAX and Apache Lucene.

164

http://ojax.sourceforge.net/


www.deri.ie

With a Little Help from My FriendsSocial Semantic Search and Browsing

Sebastian Ryszard Kruk, Adam GzellaDigital Enterprise Research Institute

National University of Ireland, Galway

[email protected], [email protected]://s3b.corrib.org/

166

Take away message

• We search in different way for different things• Keyword search is not enough• We create the knowledge by sharing our (search)

experience

167

Outline

• Motivation• How do people search• Search and Browsing lifecycle• Applying semantics and making use of social networks:

– Keyword-based search– Collaborative Faceted Navigation– Collaborative Filtering

• Conclusions - Putting it all together

168

How do people search?

• Different user goals:– Resource Seeking - the user wants to find a specific resource

(e.g. lyrics of a song, a program to download, a map service etc.)– Navigational - the user is searching for a specific web site

whose URL s/he forgot– Informational - the user is looking for information about a topic

s/he is interested in

Rose and Levinson: Understanding user goals in web search (2004)

169

Search and browsing lifecycle

• Why?– Information can be useful– Information can be a garbage

• How? (Search and browsing actions)– [REUSE] keyword-based search (resource seeking) – [REDUCE] faceted navigation (navigational) – [RECYCLE] collaborative filtering (informational)

• Can this process be improved with Semantic Web and Social Networking technologies?

170

Query refinement in keyword-based search

• Why simple full-text search is not enough?– Too many results (low precision)– One needs to specify the exact keyword (low recall)– How to distinguish between: Python and python? (high fall-out)

• How? – Disambiguation through a context

• Query context• Short-term context:

– User’s goal– Location– Time

• Long-term context:– User’s interest– Search engine specific

171

Query refinement in keyword-based search

• How? – Query refinement

• Spread activation

• Types mapping

• Pruning

– Acquiring the context information:• Previous searches of the user

• Semantically annotated user’s bookmarks

• Community profile

• And? (Manual query refinement)– “Tell me why” button and

the transcript of refinement process– Continue to faceted navigation

172

Collaborative Faceted Navigation

• Why?– The search does not end on a (long) list of results

– The results are not a list (!) but a graph

– We loose context with linear navigation

– A need for unified notion (UI, Services) of filter/narrow and browse/expand services

– Share browsing experience – navigate collaboratively

• How (Services)?– Defines REST access to services and their composition

– Basic services: access, search, filter, similar, browse, combine

– Meta services: RDF serialization, subscription channels, service ID generation

– Context services: manage contexts, manage service calls/compositions in the context, lists contexts

– Statistics services: properties, values, tokens

173

Collaborative Faceted Navigation

• How (User interface)?– Hexagons to capture the notion of non-linear history of browsing– Selecting values from list, tag cloud or TagsTreeMapTM

– Context zoomable interface:• List (graph) of results• Browse from current results• Navigate between service call• Navigate between contexts (with given call)

174

Social Semantic Collaborative Filtering

• Why?– The bottom-line of acquiring knowledge: informal communication

(“word of mouth”)

• How?– Everyone classifies (filters) the information in bookmark folders

(user-oriented taxonomy)– Peers share (collaborate over) the information (community-

driven taxonomy)

• Result?– Knowledge “flows“ from the expert

through the social network to the user– System amass a lot of information

on user/community profile (context)

175

Social Semantic Collaborative Filtering

• Problems?– The horizon of a social network (2-3 degrees of separation)– How to handle fine-grained information (blogs, wikis, etc.)

• Solutions? – Inference engine to suggest knowledge from the outskirts of the

social network– Support for SIOC metadata:

• Semantically Interlinked Online Communities: blogs, wikis, fora, …

• SIOC browser in SSCF

• Annotations and evaluations of “local” resources

176

Putting it all together

user profile:recent actions

refinesearch results

filter, record, annotate, and share results and actions

re-call shared actions

user profile:user’s interests

filter, record, annotate, and share results


www.deri.ie

Do we need Semantic Web and Web 2.0 technologies in Digital

Libraries? Irish Digital Libraries SummitIrish Digital Libraries Summit

Digital Libraries at the eve of the Next Generation Internet



www.deri.ie

Irish Digital Libraries SummitIrish Digital Libraries SummitDigital Libraries at the eve of the Next Generation

Internet

Conclusions


irish digital libraries summit

Technology

answering

ontological

ucd humanities

jerome digital

irish digital

metadata sharing

fundamental

community