Michael
Färber, Achim Rettinger and Andreas Harth
Towards Monitoring of Novel Statements in the News
Abstract:
In media monitoring users have a clearly defined information need to
find so far unknown statements regarding certain entities or relations
mentioned in natural-language text. However, commonly used
keyword-based search technologies are focused on finding relevant
documents and cannot judge the novelty of statements contained in the
text. In this work, we propose a new semantic novelty measure that
allows to retrieve statements, which are both novel and relevant, from
natural-language sentences in news articles. Relevance is defined by a
semantic query of the user, while novelty is ensured by checking
whether the extracted statements are related, but non-existing in a
knowledge base containing the currently known facts. Our evaluation
performed on English news texts and on CrunchBase as the knowledge base
demonstrates the effectiveness, unique capabilities and future
challenges of this novel approach to novelty. Data sets used in the evaluation:
See also our evaluation on DBpedia here.
