Feature-based reformulation of entities in triple pattern queries
  Knowledge graphs encode uniquely identifiable entities to other entities or
literal values by means of relationships, thus enabling semantically rich
querying over the stored data. Typically, the semantics of such queries are
often crisp thereby resulting in crisp answers. Query log statistics show that
a majority of the queries issued to knowledge graphs are often entity centric
queries. When a user needs additional answers the state-of-the-art in assisting
users is to rewrite the original query resulting in a set of approximations.
Several strategies have been proposed in past to address this. They typically
move up the taxonomy to relax a specific element to a more generic element.
Entities don't have a taxonomy and they end up being generalized. To address
this issue, in this paper, we propose an entity centric reformulation strategy
that utilizes schema information and entity features present in the graph to
suggest rewrites. Once the features are identified, the entity in concern is
reformulated as a set of features. Since entities can have a large number of
features, we introduce strategies that select the top-k most relevant and
{informative ranked features and augment them to the original query to create a
valid reformulation. We then evaluate our approach by showing that our
reformulation strategy produces results that are more informative when compared
with state-of-the-art
