Slawek Korea

Slide 1: Semantic Infrastructure Lab (Corrib) Digital Enterprise Research Institute National University of Ireland, Galway  Copyright 2006 Digital Enterprise Research www.deri.ie Institute. All rights reserved.

Slide 2: Outline • Motivation • JeromeDL • FOAFRealm • S3B • MarcOnt • Didaskon • Conclusion 2

Slide 3: Motivation • Semantic Web (2.0?) will not emerge by its own • We need to build an infrastructure first • Open source – fast research dissemination channel • JeromeDL spin-off projects (divide and conquer approach) 3

Slide 4: About us • Group of researchers from DERI Galway and students from Gdansk University of Technology • One goal – make semantic web 2.0 reality • Supervisors: prof. Stefan Decker (DERI), prof. Henryk Krawczyk (GUT), Sebastian Ryszard Kruk • PhD Students: Maciej Dąbrowski, Adam Gzella, Sławomir Grzonkowski, Jacek Jankowski, Krystian Samp, Tomasz Woroniecki • Interns (March-June 2007): Filip Czaja, Jarek Dobrzanski, Wladek Bultrowicz • 9 Master students from GUT 4

Slide 5: Social Semantic Digital Library • A library stores and provides access to resources (books) • Qualified staff updates catalogues and helps users

Slide 6: Social Semantic Digital Library • Machine-readable resources • Full-text index improves searching • Easy access • Availability

Slide 7: Social Semantic Digital Library • 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

Slide 8: Social Semantic Digital Library • Involves the community into sharing knowledge • Utilizes social network in searching • Allows for comments, blogs, shared bookmarks • Easy tagging

Slide 9: Social 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

Slide 10: 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

Slide 11: 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

Slide 12: Using JeromeDL

Slide 13: Using JeromeDL • An administrator either approves or rejects a published resource

Slide 14: 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

Slide 15: JeromeDL for a regular user

Slide 16: Search and browsing lifecycle • Why? – Information can be useful or a garbage – Different user goals (Rose and Levinson: Understanding user goals in web search (2004)) • 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 • 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? 16

Slide 17: 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 17

Slide 18: 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 18

Slide 19: Faceted navigation on arbitrary graph • 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, SOA) of filter/narrow and browse/expand services 19

Slide 20: Faceted navigation on arbitrary graph • How (SOA)? – 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 • How (User interface)? – Hexagons to capture the notion of non-linear 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) 20

Slide 21: 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) 21

Slide 22: 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? (under testing) – Inference engine to suggest knowledge from the outskirts of the social network – Support for SIOC metadata: • SIOC browser in SSCF • Annotations and evaluations of “local” resources 22

Slide 23: Putting it all together user profile: user’s interests refine search results user profile: recent actions filter, record, annotate, and share results re-call shared actions filter, record, annotate, and share results and actions 23

Slide 24: Introduction to MarcOnt Motivation: • Provide set of tools for collaborative ontology development MarcOnt Initiative goals: • Collaboration • Tools for domain experts • Mediation services 24

Slide 25: MarcOnt Mediation Services 1. Format co-operation 2. Format translation MarcOnt Ontology MarcOnt RDF MARC21 RDF Dublin Core RDF New format RDF MARC21 XML Dublin Core XML New format XML MARC21 Dublin Core New format MarcOnt Mediation Services RDF Translator 25

Slide 26: MarcOnt Ontology • Central point of MarcOnt Initiative • Translation and mediation format • Continuos collaborative ontology improvement • Knowledge from the domain experts • Community influence and evaluation 26

Slide 27: MarcOnt Portal 3. Source of knowledge Portal provides: Initial Ontology • Suggestions Sugested Poposals • Annotations Versioning Proposal discussion • Versioning Proposal anotations • Ontology editor Proposal autopromoting Proposal voting Next Revision MarcOnt Portal 27

Slide 28: MarcOnt Initiative summary MarcOnt Initiative goals: • Create a framework for collaborative ontology improvement (E-learning) • Provide domain experts with tools to share their knowledge • Offer tools for data mediation between different data formats 28

Slide 29: Didaskon Didaskon - Automated Curriculum Composition based on the Work-flow Scheduling of Semantically Annotated Learning Object Services Architecture of the future e-Learning system (our idea presented on LACLO 2006): • Ontology for user model – delivering personalised content • Ontology for content - ensuring cooperation of heterogeneous environments which use different formats 29

Slide 30: Didaskon - Architecture Didaskon – e-Learning framework, that will be based on existing solutions: • FOAFRealm - users management, • JerlomeDL – learning object’s repository, • MBB – improved browsing, • MarcOnt – handling different data formats, • SSIS – tracking informal learning 30

Slide 31: Conclusion • Together with smaller projects (JOnto, TagsTreeMaps, HexBrowser) these are our building blocks for the Semantic Web (2.0) • The initial infrastructure has been delivered - time to start researching again • Please visit: http://www.corrib.org/ for more information 31