A Semantic Web Browser for Supporting Open-Corpus Linking and Adaptive Hypermedia

1. SemWeB – A Semantic Web Browser for Supporting Open-Corpus Linking and Adaptive Hypermedia Melike Şah Intelligence, Agents and Multimedia Group School of Electronics and Computer Science University of Southampton ms305r@ecs.soton.ac.uk Supervisors: Prof Wendy Hall, Prof David C De Roure

2. Outline • Background • Semantic Web technologies • Adaptive Hypermedia • SemWeB – Semantic Web Browser • Conclusions

3. Semantic Web • “an extension of the current Web, in which information is given well-defined meaning” • “is an extension of Web principles from documents to data” – technology for creating and sharing data • Powerful knowledge representation formalisms • Inferencing mechanisms • Interoperability • Is a global information space for inter-linked data (linked data) • Semantic metadata is on the Web now!

4. Semantic Web is a reality (Linked Data) • Linked data - for exposing, sharing and connecting pieces of data on the Semantic Web (available in RDF) • Linking Open Data Community Project - extends Web by publishing various open datasets as RDF on the Web and setting RDF links between data items from different data sources.

5. Adaptive Hypermedia • One page fits all! No! Different users have different browsing needs. Page content and hyperlinks should be adapted accordingly. • Adaptive Hypermedia is a solution. Most of apps are in educational hypermedia domain (AHA, InterBook, …) • Early adaptive hypermedia systems use controlled vocabularies. Semantic Web is a solution.

6. Adaptive Hypermedia Standards • IEEE PAPI and IMS LIP are well known user modelling standards. • They mainly developed for learners in educational hypermedia. • Ordinary users will not enter such information to a Web site or may not need that kind of personalization. • How about browsing interests, goals, strategies of a user?

7. Personalization Mechanisms • Existing approaches are obstructive • Users need to log in to multiple websites • Users have to enter personal information and preferences many times • Profiles are different for each site • There is a need for generic user profiles and personalization architectures, which can achieve adaptive hypermedia on diverse websites

8. SemWeb: a Personalized Semantic Web Browser • Şah, M., Hall, W., De Roure, D. C.: Designing a Personalized Semantic Web Browser. Accepted to Adaptive Hypermedia and Adaptive Web-Based Systems, 2008. • Şah, M., Hall, W., De Roure, D. C.: SemWeB: A Semantic Web Browser for Supporting the Browsing of Users using Semantic and Adaptive Links. Accepted to Doctoral Consortium of Adaptive Hypermedia and Adaptive Web-Based Systems, 2008.

9. Background • There are linked data browsers • Tabulator, Disco, OpenLink RDF Browser, …. • Separation between metadata and Web content • Our intention is not to create a linked data browser, but to create a semantic layer to a browser. • Interfaces for supporting browsing of users • Magpie, COHSE (No Adaptive Hypermedia support and they use databases for linking) • Our aim is to adapt information to the needs of the users. Besides, we will use Web as source for linking (open-corpus linking).

10. System Design • SemWeB is a browser extension of the Mozilla Firefox browser. • Javascript and AJAX Web technologies are used at the browser. • Java Servlet and Jena are used at the server-side.

11. SemWeB Browser Extension

13. Information Extraction and Semantic Annotation • Information extraction using ontologies and ontology-driven lexicon based on the modified GATE framework. • For demonstration ECS ontology is used. • We crawl RDF files from ECS domain and created gazetteers and their URI mappings for semantic annotation.

14. Information Extraction (Cont.) • Extend GATE with a mapping service, which matches named entity URIs to lexicons or lexicons to named entity URIs. • i.e. “Wendy Hall” lexicon is matched to “http://id.ecs.soton.ac.uk/person/1650” • Semantic annotations are created using JAPEC pattern matching rules. • Annotation storage unit stores the created annotations as XML files at the server-side.

15. Semantic annotation by request

16. Created Annotations <?xml version=\"1.0\"?> <message> <Person> <value>Hall, W.</value> <mapping>http://id.ecs.soton.ac.uk/person/1650</mapping> </Person> <Publication> <value>Building and Managing Personalized Semantic Portals</value> <mapping>http://id.ecs.soton.ac.uk/publication/13715</mapping> </Publication> …. </message>

17. <span><a href="javascript:(function(){ var r=window.open(\'http://localhost:7070/user_db/linking.htm?instance= http://id.ecs.soton.ac.uk/person/1650&userid=testperson&goal=peopleworkswith&title=Melike Sah, \'chrome,menubar=yes,toolbar=yes,location=yes,scrollbars=yes, resizeable=yes\' ); r.focus(); })()"><img src="chrome://emptysidebar/skin/explore_icon.PNG"></a></span>'; <span class='+classname+'><a href="http://id.ecs.soton.ac.uk/person/1650"> <img src="chrome://emptysidebar/skin/browse_icon.PNG"></a>&nbsp;

18. Dereferencing URIs

19. Semantic Hyperlink Creation • Semantic Hyperlinks are requested asynchronously using AJAX request to server with resource URI, goal(s), title userid. • Possible link anchors and targets can be found by analyzing RDF description of the resource. • In addition, more useful information shown to the users according to their goals • For example, person’s recent DBLP publications • Wikipedia definition and links to broader or special topics

20. Algorithm 1) Dereference URI x of resource 2) Match triple patterns (x any any) and (any any x) 3) Match triple patterns (x rdfs:seeAlso y) 4) Match triple patterns (x owl:sameAs y) 5) If the user has a goal, use Goal Services for finding related information 6) If the user is logged in annotate links with visual cues 6) Create a response XML file using a presentation vocabulary and return this to the client’s browser

21. Browsing Goals ECS DBLP DBPedia

22. DBLP Recent Publications Goal Service (Queries DBLP SPARQL Endpoint) • Finding recent publications of a person http://dblp.L3S.de/d2r/sparql?query=Construct {<DBLP_Author_URI> foaf:made ?paper. ?paper foaf:homepage?page. ?page rdfs:label ?label.} WHERE { ?paper rdf:type foaf:Document. ?paper rdfs:label ?label. ?paper dc:creator <DBLP_Author_URI>. ?paper foaf:homepage ?page. ?paper dc:issued ?year. FILTER regex (str(?year), "2008")}

23. Semantic Hyperlinks return as XML <?xml version="1.0" encoding="UTF-8" ?> <message> <PageTitle> <value>Southampton ECS People: Melike Sah</value> <target>http://id.ecs.soton.ac.uk/person/9677</target> </PageTitle> <RDFLinks> <target>http://id.ecs.soton.ac.uk/interest/artificial_intelligence</target> <value>artificial intelligence</value> <property>ecs:hasInterest</property> </RDFLinks> <DBLPPublications> <target>http://doi.acm.org/10.1145/1286240.1286248</target> <value>Semport: a personalized semantic portal.</value> </DBLPPublications> ….

25. Personalization • We want to personalize browsing of users using metadata obtained from Web page and user profile. • First we need a generic user model which also expresses browsing needs of the users.

26. Browsing • Browsing can be categorized in three groups (Bawden, D.: Information Systems and the Stimulation of Creativity (1986) and Cove, J., Walsh, B.: Online Text Retrieval via Browsing (1988)): • Purposive / search browsing (looking for a definite piece of information) – directed • exploratory / general purpose browsing (deliberately searching for inspiration) – semi-directed • Capricious / serendipity browsing (randomly examining material) – undirected

27. Proposed User Model • In the user model, at present we use seven categories: Identification, Preference, Security, Browsing Goal, Interest, Expertise and Browsing Behavior. • Later the user model can be extended with more information (i.e. portfolio) from existing standards. • In addition, our model can applied to existing standards.

28. A Part of Proposed User Model

29. A Part of Proposed User Model (Cont.) • Interest: Low, Medium, High • Expertise: Novice, Intermediate, Expertise • Goal: Will be automatically provided by browser based on semantic context. • Browsing Level: Inactive, Passive, Active, Very Active • Browsing Type: Conditional • If the user has a browsing goal, then directed • If the user has a browsing interest, then semi-directed • If the user does not have a browsing goal or interest, then undirected

30. An Example User Profile

31. User Modeling • To start personalization, users need to register and log in from their browsers. • Profiles are kept at server-side triple store. • Additionally, a profile editor is developed, where users can update profiles from their browsers. • Users can be explicitly assigned to expertise, interests and goals from SemWeB. • Browsing level and browsing type are automatically updated by SemWeB.

32. Personalization Tab

33. Adaptive Navigation Support and Adaptive Presentation • Based on different browsing types • Directed browsing, show related links according to short-term browsing goals. • Semi-directed browsing or un-directed browsing, use interests to recommend links that are relevant to the user’s interests.

36. Creating Adaptive Links (Cont.) • Adaptation based on expertise • When a link is requested by a novice user, provide links to Wikipedia pages. • When the user is an expert, provide detailed semantic links. • Personalized Homepages • Also, link sorting and link annotation can be done based on interest ratings, goal priorities, expertise and browsing levels.

39. Experimenting SemWeB on Different Linked Data Domains • Modifications required on • IE and Semantic Annotation • SemWeB Sidebar • No Updates on Semantic Linking and Adaptation Module

42. Conclusions • We presented a personalized Semantic Web browser architecture, which uses a novel behavior-based user model for adaptation. • In our approach, AH and context-based linking can be achieved on different Web sites. • SemWeB is not an application specific software and tested on ECS, DBpedia and DBLP linked data domains.

43. Thank you, Questions?