Epiphany: Adaptable RDFa Generation Linking the Web of Documents to the Web of Data

Benjamin Adrian, Jörn Hees, Ivan Herman, Michael Sintek, Andreas Dengel

In: Philipp Cimiano , Helena Sofia Pinto (editor). Knowledge Engineering and Knowledge Management by the Masses. International Conference on Knowledge Engineering and Knowledge Management (EKAW-2010) 17th October 11-15 Lisbon Portugal Pages 178-192 Lecture Notes in Computer Science (LNCS) 6317 ISBN 978-3-642-16437-8 Springer 2010.


The appearance of Linked Open Data (LOD) was an important milestone for reaching a Web of Data. More and more RDF data sets get published to be consumed and integrated into a variety of applications. Pointing out one application, Linked Data can be used to enrich web pages with semantic annotations. This gives readers the chance to recall Semantic Web’s knowledge about text passages. RDFa provides a well-defined base, as it extends HTML tags in web pages to a form that contains RDF data. Nevertheless, asking web authors to manually annotate their web pages with semantic annotations is illusive. We present Epiphany, a service that annotates Linked Data to web pages automatically by creating RDFa enhanced versions of the input HTML pages. In Epiphany, Linked Data can be any RDF dataset or mashup (e.g., DBpedia, BBC programs, etc.). Based on ontology-based information extraction and the dataset, Epiphany generates an RDF graph about a web page’s content. Based on this RDF graph, RDFa annotations are generated and integrated in an RDFa enhanced version of the web page. Authors can use Epiphany to get RDFa enhanced versions of their articles that link to Linked Data models. Readers may use Epiphany to receive RDFa enhanced versions of web pages while surfing. We analysed results of Epiphany with Linked Data from BBC about music biographies and show a similar quality compared to results of Open Calais. Epiphany provides annotations from a couple of Linked Data sets.


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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz