  Entity suggestion by example (ESbE) refers to a type of entity acquisition
query in which a user provides a set of example entities as the query and
obtains in return some entities that best complete the concept underlying the
given query. Such entity acquisition queries can be useful in many applications
such as related-entity recommendation and query expansion. A number of ESbE
query processing solutions exist in the literature. However, they mostly build
only on the idea of entity co-occurrences either in text or web lists, without
taking advantage of the existence of many web-scale conceptual taxonomies that
consist of hierarchical isA relationships between entity-concept pairs. This
paper provides a query processing method based on the relevance models between
entity sets and concepts. These relevance models can be used to obtain the
fine-grained concepts implied by the query entity set, and the entities that
belong to a given concept, thereby providing the entity suggestions. Extensive
evaluations with real data sets show that the accuracy of the queries processed
with this new method is significantly higher than that of existing solutions.
