Ad hoc retrieval via entity linking and semantic similarity

Semantic search has emerged as a possible way for addressing the challenges of traditional keyword-based retrieval systems such as the vocabulary gap between the query and document spaces. In this paper, we propose a novel semantic retrieval framework that uses semantic entity linking systems for forming a graph representation of documents and queries, where nodes represent concepts extracted from documents and edges represent semantic relatedness between those concepts. The core of our proposed work is a semantic-enabled language model that estimates the probability of generating query concepts given values assigned to document concepts. The semantic retrieval framework also provides basis for interpolating keyword-based retrieval systems with the semantic-enabled language model. We conduct comprehensive experiments over several Trec document collections and analyze the performance of different configurations of the framework across multiple retrieval measures. Our experimental results show that the proposed semantic retrieval model has a synergistic impact on the results obtained through the state-of-the-art keyword-based systems, and the consideration of semantic information can complement and enhance the performance of such retrieval models.