 Copyright 2006 Digital Enterprise Research Institute. All rights reserved. Semantic Web 2.0 Stefan Decker Digital Enterprise Research Institute National University of Ireland, Galway

2 DERI Galway – Mission DERI Galway’s Mission is “to exploit semantics for helping People Organisations Systems to better collaborate and interoperate”

3 What is Web 2.0? The term Web 2.0 was made popular by Tim O’Reilly: – -is-web-20.html –“Web 2.0 … has … come to refer to what some people describe as a second phase of architecture and application development for the World Wide Web.” The Web where “ordinary” users can meet, collaborate, and share using whatever is newly popular on the Web (tagged content, social bookmarking, AJAX, etc.) Popular examples include: –Bebo, del.icio.us, digg, Flickr, Google Maps, Skype, Technorati, Wikipedia…

4 Web 2.0 and social software Web 2.0 focuses include: –The Web as a platform for social and collaborative exchange –Reusable community contributions –Subscriptions to information, news, data flows, services –Mass-publishing using web-based social software –“Social Software lets people rendezvous, connect or collaborate by use of a computer network. It results in the creation of shared, interactive spaces…” Social software for communication and collaboration: –IM, IRC, Forums, Blogs, Wikis, Social Network Services, Social Bookmarks, MMOGs…

5 –“An extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Sir Tim Berners-Lee et al., Scientific American, 2001: tinyurl.com/i59p –“…allowing the Web to reach its full potential…” with far-reaching consequences –“The next generation of the Web” What is the Semantic Web? (beware the hype)

6 The Semantic Web (so far)

7 How to create the Semantic Web? Commonly quoted problems Ontologies are difficult to create and are not used –Not worth the effort Annotation is expensive –Regular user won’t bother Metadata provides no benefits –No consumers Standards are too complicated –Developers don’t understand Description Logics

8 Social semantic information spaces Web 2.0 and social software

9 Information Foodchain Metadata browsing Metadata search End User Metadata creation (eg., SIOC) Metadata sharing/ depoyment

10 (Semantic) Blogging: A phenomenon for a new generation? Cincinnati Enquirer, October 2004

11 Traditional blogging vs. semantic blogging Traditional blogging: –Publishing for the “eyeball Web” –Content is text, images, video (i.e. data targeted at people) Semantic blogging: –Enrich traditional blogs with semantic metadata –Structural: what relates to what and how? –Content related: what is this post about (e.g. a person, an event, etc.)? –Blogging targeted at machines as well as people

12 Why semantic blogging? Users collect and create large amounts of structured data on their desktops This data is often tied to specific applications and locked within the user's computer Semantic blogging can lift this data into the Web

13 Releasing your data to the Web scenario Ina John Ina‘s Computer John‘s Computer Blog Post Metadata writes Post annotates Post publishes Post reads Post imports metadata Web

14 Creating a semantic blog post with semiBlog Annotating a blog entry with an address book entry. Harth Andreas Andreas Harth

15 Using the metadata Once a blog has semantic metadata, it can be... Used to query: “Which blog posts talk about papers by Stefan Decker?” Used to browse across blogs and other kinds of discussion methods: Imported into desktop applications of blog readers (AKA “The Web as a Clipboard“)

16 The Web as a clipboard using a semiBlog reader A user can import metadata from here into his/her own applications

 Copyright 2006 Digital Enterprise Research Institute. All rights reserved. Browsing Metadata Extending faceted navigation for RDF data

18 Motiviation Social semantic information space –semantic web: structured heterogeneous data without fixed schema faceted browsing: navigation of structured information –advantages: intuitive exploration of large datasets –disadvantage: manual configuration of interface unclear what faceted navigation really is Only surface browsing (flat set of objects) Introduction

19 Faceted browsing example: iTunes Introduction

20 Faceted browsing: limitations facets selection manually configured for a fixed domain –music: genre, artist, album, title –books: title, author, publisher –recipes: ingredients, cuisine, calories, preparation time facets do not (fully) exploit interconnected data –assume data homogeneity –focus on one resource type (e.g. publications) –only facets of publications, not of people Introduction 

21 Faceted browsing on the Semantic Web faceted browsing is query construction facets in semantic web –information space: set of triples –elements of interest: set of subjects –facets: set of properties identify operators... SELECT ?x WHERE ?x author ?a and ?a age “30”. Understanding

22 Selection operators (example) all thirty-year-olds all single people all people who know somebody with a friend called Stefan Understanding

23 Facet browsing: decision tree faceted browsing: constructing & traversing decision tree... Constructing the interface

24 Facet ranking: optimising decision tree traversal structure of decision tree affects navigation efficiency cardinality –author: 300 values (all names unique) –publisher: 5 values (only few topics) balance –publisher: Springer (82%), rest (18%) –topic: logic (30%), p2p (30%), web (40%) frequency –location 5%(almost never given) –author 100% (no anonymous publications) Constructing the interface

25 Prototype: choose FBI dataset Prototype

26 Prototype: interested in terrorists Prototype

27 Prototype: filter by facets Prototype

28 Prototype: see matching results Prototype

29 Evaluation compared to zero-effort interfaces (keyword search) gave simple tasks (e.g. find all blue-eyed terrorists) results: –higher solution rate, preferred interface –complex queries difficult for people –ranking not intuitive Prototype

30 Summary faceted browsing good for exploration of large datasets existing interfaces manually configured to fixed domains existing interfaces do not exploit interconnected data we provide formal model of faceted navigation we extend to interconnected Semantic Web data we rank facets automatically demo online: Conclusion

 Copyright 2006 Digital Enterprise Research Institute. All rights reserved. Sharing Metadata Social Semantic Digital Libraries /

32 Different User Roles – Different Requirements Cross referrencing Ease of use Semantic Web Web 2.0 Digital Libraries Legacy KOS Social Semantic Digital Libraries

What is a Social Semantic Digital Library? Social semantic digital libraries –integrate information from various sources –provide interoperability with other systems (not only libraries) –deliver more robust, user friendly and adaptable search and browsing interfaces empowered by semantics and social networking

JeromeDL Semantic Digital Library Resources and annotations repository Middleware: –query processing –community space –resources management User interface agents: Communication to the outside world Administrative interface

Ontologies in JeromeDL

36 Semantic Components Framework JeromeDL TM semantic digital library knowledge sharing SSCF TM FOAFRealm TM social networks based DRM scalable P2P infrastructure TM on-demand e-learning mediation ontology & services MarcOnt TM

37 Social Semantic Collaborative Filtering? Goal: –enhance knowledge sharing through communities SSCF delivers: –Community-oriented, semantically-rich taxonomies –Information about a user's interest –Flows of expertise from the domain expert

38 Example of social semantic collaborative filtering foaf:knows xfoaf:include xfoaf:bookmark Tag (keywords, topics) DRM (FOAFRealm)

39 Going back…. Memex (Vannevar Bush) A memex is “a device in which an individual stores all his books, records, and communications.” Open Hypertext System (Doug Engelbart) “The open hyperdocument system (OHS) is a standards-based, open source framework for developing collaborative, knowledge management applications.” WWW (Tim Berners-Lee) “There was a second part of the dream […] we could then use computers to help us analyse it, make sense of what we re doing, where we individually fit in, and how we can better work together.”

40 It wasn’t the time…

41 But now it is… Today necessary technologies & communities exist: Standardised metadata: Semantic Web Scalable distributed infrastructure: P2P Computing Knowledge articulation and interaction: Desktop/Wiki Technology Processing of unstructured and legacy information: NLP Human centric information exchange: Online Social Networks

42 Realising the Social Semantic Desktop Desktop: Help individuals in managing information on the Web/their PC Semantic: Make content available to automated processing Social: Enable exchange across individual boundaries colleague friend acquaintance Social semantic peers peers Personal Semantic Web:a semantically enlarged intimate supplement to memory Social protocols and distributed search Person Topic Website Document Image Event Person

43 Co-evolving technology streams Phase 1Phase 2Phase 3 Ontology-Driven Distributed Social Networking Ontology-Driven Social Networking Semantic Desktop Social Semantic Desktop P2P Networks Semantic Web Desktop / Web Semantic P2P Social Networking NLP

44 Next steps… Definition of a reference model for next generation collaborative environments Standardisation of ontologies and APIs Ireland has a leading role!

45 Conclusions Web 2.0 makes the Semantic Web practical –Web 2.0 provides the user interaction, Semantic Web the standards for information interchange –From Semantic (Web 2.0) towards (Semantic Web) 2.0 DERI supports the whole Semantic Web 2.0 foodchain Making humans more efficient is the next challenge

46 More information Digital Enterprise Research Institute – SIOC – Jerome Semantic Digital Library – Semantic Desktop Community Site: – NEPOMUK project: –