Published: 2013 Dezember
Institution: Institut AIFB, KIT
Erscheinungsort / Ort: Karlsruhe
To allow effective search on the Web of data, systems frequently rely on data from multiple sources for answering queries. However, in order to enable an interactive result exploration, users should be able to choose data sources contributing to search results, thereby refining/expanding their current findings. For this, one needs effective recommendations for data sources to be picked: data source contextualization. Previous work, however, solely aims at source contextualization for “Web tables”, while relying on schema information, and fixed table structures. Addressing these shortcomings, we exploit work from the field of data mining, and show how to enable effective Web data source contextualization. Based on a real-world use case, we built a prototype contextualization engine, which we integrated in a Web search system. We empirically validated the effectiveness of our approach – achieving performance gains of up to 29% over the state-of-the-art.