  Novel research in the field of Linked Data focuses on the problem of entity
summarization. This field addresses the problem of ranking features according
to their importance for the task of identifying a particular entity. Next to a
more human friendly presentation, these summarizations can play a central role
for semantic search engines and semantic recommender systems. In current
approaches, it has been tried to apply entity summarization based on patterns
that are inherent to the regarded data.
  The proposed approach of this paper focuses on the movie domain. It utilizes
usage data in order to support measuring the similarity between movie entities.
Using this similarity it is possible to determine the k-nearest neighbors of an
entity. This leads to the idea that features that entities share with their
nearest neighbors can be considered as significant or important for these
entities. Additionally, we introduce a downgrading factor (similar to TF-IDF)
in order to overcome the high number of commonly occurring features. We
exemplify the approach based on a movie-ratings dataset that has been linked to
Freebase entities.

Artificial Intelligence;Human-Computer Interaction;Information Retrieval