Relevance is a key notion in information retrieval. It measures the relation between query and document which contains several different dimensions, e.g., semantic similarity, topical relatedness, cognitive relevance (the relations in the aspect of knowledge), usefulness, timeliness, utility and so on. However, existing retrieval models mainly focus on semantic similarity and cognitive relevance while ignore other possible dimensions to model relevance. Topical relatedness, as an important dimension to measure relevance, is not well studied in existing neural information retrieval. In this paper, we propose a Topic Enhanced Knowledge-aware retrieval Model (TEKM) that jointly learns semantic similarity, knowledge relevance and topical relatedness to estimate relevance between query and document. We first construct a neural topic model to learn topical information and generate topic embeddings of a query. Then we combine the topic embeddings with a knowledge-aware retrieval model to estimate different dimensions of relevance. Specifically, we exploit kernel pooling to soft match topic embeddings with word and entity in a unified embedding space to generate fine-grained topical relatedness. The whole model is trained in an end-to-end manner. Experiments on a large-scale publicly available benchmark dataset show that TEKM outperforms existing retrieval models. Further analysis also shows how topic relatedness is modeled to improve traditional retrieval model with semantic similarity and knowledge relevance

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