Program

9:30 – 10:30 Session 1: Ontology Alignment and Enrichment

• Rahul Parundekar, Craig Knoblock and José Luis Ambite: Finding Concept Coverings in Aligning Ontologies of Linked Data (Full Paper)
• Frederik Janssen, Faraz Fallahi, Jan Noessner and Heiko Paulheim: Towards
  Rule Learning Approaches to Instance-based Ontology Matching (Short Paper)
• Mathieu D’aquin, Gabriel Kronberger and Mari Carmen Suárez-Figueroa: Combining Data Mining and Ontology Engineering to enrich Ontologies and Linked Data (Short Paper)

10:30 – 11:00 Coffee Break

11:00 – 12:00 Invited Talk by Volker Tresp: Machine Learning with Linked Open Data

Linked Open Data (LOD) represents a great source of useful information for machine learning applications. Statistical machine learning in particular is well suited to handle the high dimensionality, sparsity and incompleteness of LOD.
We will present a number of concrete examples. In Traffic LarKC we have integrated LOD to improve traffic forecasting and in Bottari we have used opinion mining on Twitter to improve restaurant recommendations. Life science data in LOD was exploited to predict the relationship between genes and diseases. Finally, we have used machine learning to predict triples on the complete YAGO2 ontology. The predictions can then be integrated into an extended SPARQL query.

12:00 – 13:00 Session 2: Information Extraction for Linked Open Data

• Daniel Hienert and Francesco Luciano: Extraction of Historical Events from Wikipedia (Full Paper)
• Antonis Koukourikos, Vangelis Karkaletsis and George Vouros: Towards Enriching Linked Open Data via Open Information Extraction (Short Paper)
• Andias Wira-Alam and Brigitte Mathiak: Mining Wikipedia’s Snippets Graph – First Step to Build a New Knowledge Base (Short Paper)