This presentation is about Epiphany, a system that automatically generates RDFa annotated versions of web pages based on information from Linked Data models.
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Epiphany: Adaptable RDFa Generation Linking the Web of Documents to the Web of Data
1. EpiphanyAdaptable RDFa Generation Linking the Web of Documents to the Web of Data Benjamin Adrian, JörnHeesIvan Herman, Michael Sintek,Andreas Dengel
2. Outline Web of Document vs. Web of Data RDFa, the glue combining both worlds Use Linked Data for RDFa generation Extract RDF from web pages Visualize RDFa via Linked Data Evaluation and Comparison with Open Calais 2
3. Web of Documents Features Distributed textual content Addressed by URLs Layout in HTML, CSS Connected with hyperlinks Access via HTTP Made for human readers! 3 World WideWeb WWW
4. Web of Data Features Distributed data sets Addressed by URIs Format is RDF Connected with RDF Links Access via HTTP Made for machine readers! 4 Linked OpenData
5. Plain HTML <BODY> <H1>Epiphany</H1><UL> <LI>Ben</LI> <LI>Jörn</LI> <LI>Ivan</LI> <LI>Michael</LI> <LI>Andreas</LI></UL> </BODY> 5 RDFa – Adding machine-readable data to web documents RDF in HTMLattributes
9. A bridge from document to data 9 RDFa Benefits RDFa is easy to generate by CMS ( i.e., Drupal), and other dynamic content providers. It is easy to annotate well-structured data with RDFa RDFa WWW Open Problems How to annotate unstructured plain text content? How to annotate the same documentsdifferently based on different data bases?
10. Epiphany 10 How to consumeLinked Data forRDFa generation Generate RDFa-annotated versions of web pages on-the-fly. For different Linked Data Models used generate different RDFa annotations. Create interactive boxes filled with additional information about annotated resources. Linked Data Model EpiphanyRDFa Generation Linked Data Model HTML+RDFa HTML
11. Epiphany - Example 11 How to consumeLinked Data forRDFa generation Tim Burton is a movie maker. <span about=“http://dbpedia.org/resource/Tim_Burton”property=“foaf:name”>Tim Burton</span>is a movie maker.
12. Epiphany 12 How to extractRDF from web pages Ontology-based Information Extraction RDFgraph Preprocessing ExtractionPipeline RDFgraphstore Cache HTML RDFaGenerator Linked Data Model HTML+RDFa
13. Ontology-based Information Extraction 13 RDF-based Information Extraction Ontology-based Information Extraction Pipeline “Ben is member of RDFa WG.” Text Normalization “Ben”, “is”, “member”, “of”, “RDFa WG”, ”.” Text Segmentation [] foaf:name “Ben”. [] foaf:name “RDFa WG” . Symbol Recognition <#me> foaf:name “Ben”. <#RDFaWG> foaf:name ”RDFa WG.” Instance Recognition <#me> foaf:member of <#RDFaWG>. Contextual Fact Recognition <#me> foaf:name “Ben”. <#RDFaWG> foaf:name ”RDFa WG.” <#me> foaf:member <#RDFaWG>. RDF Generation
14. RDFa Generation 14 How to create semantic annotations requestHTML tidy toXHTML list of RDF triples with literal object values DOM node traversal am:Burtonfoaf:name “Tim Burton”. am:augustrdfs:label “August”. am:autorfoaf:name “Autor”. Am:filmfoaf:name “Film”.
15. RDFa Generation 15 How to create semantic annotations for each text node around matches create html:SPANelement with RDFa attributes requestHTML tidy toXHTML list of RDF triples with literal object values DOM node traversal am:Burtonfoaf:name “Tim Burton”. am:augustrdfs:label “August”. am:autorfoaf:name “Autor”. Am:filmfoaf:name “Film”.
16. RDFa Generation 16 How to create semantic annotations for each text node around matches create html:SPANelement with RDFa attributes requestHTML tidy toXHTML add link to RDF graph to header list of RDF triples with literal object values DOM node traversal add js:onclick listener to elements with RDFa:about attributes am:Burtonfoaf:name “Tim Burton”. am:augustrdfs:label “August”. am:autorfoaf:name “Autor”. Am:filmfoaf:name “Film”.
17. RDFa Visualization 17 Use RDF togenerateEpiphanies Browser Epiphany Linked Data AJAX call toInformation Provider onclick event on <SPAN/> elements GET /resource/Tim_Burton HTTP 1.1 HOST: dbpedia.org ACCEPT: RDF Render RDF data inHTML as lighting box
18. Evaluation 18 ComparedEpiphany and Open Calais Goal: Epiphany is at least as good as Open Calais But:
19. Evaluation 19 ComparedEpiphany and Open Calais Linked data model : 12,462 pages + RDF graphs by BBC Music Artists </music/artists/0383dadf-2a4e-4d10-a46a-e9e041da8eb3#artist> rdf:typemo:MusicGroup ; rdf:typemo:MusicArtist ; foaf:name „Queen“ . … http://www.bbc.co.uk/music/artists/0383dadf-2a4e-4d10-a46a-e9e041da8eb3 http://www.bbc.co.uk/music/artists/0383dadf-2a4e-4d10-a46a-e9e041da8eb3.rdf
20. Evaluation – BBC Corpus 20 ComparedEpiphany and Open Calais For comparing results generated by Open Calais and Epiphany, we had to align Open Calais’ results to BBC’s vocabulary. BBC / Epiphany: mo:SoloMusicArtistmo:MusicGroup foaf:name Open Calais: oc:Person oc:MusicGroup oc:match, oc:name
21. Evaluation 21 Named Entities How did Open Calais and Epiphany retrieve any instances with known foaf:name values? e.g., [] foaf:name “Queen”
22. Evaluation 22 Solo MusicArtists mo:SoloMusicArtistwith known foaf:name values? e.g., [] foaf:name “Brian May”; a mo:SoloMusicArtist.
23. Evaluation 23 MusicGroups mo:MusicGroupwith known foaf:name values? e.g., [] foaf:name “Queen”; a mo:MusicGroup .
29. Amazon (Good Relations)At a glance Epiphany is an RDFa Generator that enriches Web Pages with Information from Linked Data Models. Demo Version http://projects.dfki.uni-kl.de/epiphany/
35. Thank you for Listening! Contributors JörnHees University of Kaiserslautern Benjamin Adrian DFKI Andreas Dengel DFKI Michael Sintek DFKI Ivan Herman W3C 27