Annotation-Based Document Retrieval with Probabilistic Logics

Annotations are an important part in today’s digital libraries and Web information systems as an instrument for interactive knowledge creation. Annotation-based document retrieval aims at exploiting annotations as a rich source of evidence for document search. The POLAR framework supports annotation-based document search by translating POLAR programs into four-valued probabilistic datalog and applying a retrieval strategy called knowledge augmentation, where the content of a document is augmented with the content of its attached annotations. In order to evaluate this approach and POLAR’s performance in document search, we set up a test collection based on a snapshot of ZDNet News, containing IT-related articles and attached discussion threads. Our evaluation shows that knowledge augmentation has the potential to increase retrieval effectiveness when applied in a moderate way.

• Type: Conference
• Year: 2007
• Where: ERCIMDL
• Authors: Ingo Frommholz
• Keywords: Annotation-based Document, Annotation-based Document Retrieval, Document Search, ERCIMDL 2007