The Web of Data describes objects, entities, or “things” in terms of their attributes and their relationships, using RDF statements. There is a need to make this wealth of knowledge easily accessible by means of keyword search. Despite recent research efforts in this direction, there is a lack of understanding of how structured semantic data is best represented for text-based entity retrieval. The task we are addressing in this paper is ad-hoc entity search: the retrieval of RDF resources that represent an entity described in the keyword query. We build upon and formalise existing entity modeling approaches within a generative language modeling framework, and compare them experimentally using a standard test collection, provided by the Semantic Search Challenge evaluation series. We show that these models outperform the current state-of-the-art in terms of retrieval effectiveness, however, this is done at the cost of abandoning a large part of the semantics behind the data. We propose a novel entity model capable of preserving the semantics associated with entities, without sacrificing retrieval effectiveness.