Toward a Deep Neural Approach for Knowledge-Based IR
This work tackles the problem of improving semantic matching in information retrieval for users, but it is incremental as it builds on existing neural and knowledge-based approaches.
This paper addresses the semantic gap in information retrieval by proposing to integrate knowledge bases into deep neural architectures for document ranking, aiming to enhance latent representations of queries and documents through both distributional and relational semantics.
This paper tackles the problem of the semantic gap between a document and a query within an ad-hoc information retrieval task. In this context, knowledge bases (KBs) have already been acknowledged as valuable means since they allow the representation of explicit relations between entities. However, they do not necessarily represent implicit relations that could be hidden in a corpora. This latter issue is tackled by recent works dealing with deep representation learn ing of texts. With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations. In this paper, we review the main approaches of neural-based document ranking as well as those approaches for latent representation of entities and relations via KBs. We then propose some avenues to incorporate KBs in deep neural approaches for document ranking. More particularly, this paper advocates that KBs can be used either to support enhanced latent representations of queries and documents based on both distributional and relational semantics or to serve as a semantic translator between their latent distributional representations.