  Similarity query is the family of queries based on some similarity metrics.
Unlike the traditional database queries which are mostly based on value
equality, similarity queries aim to find targets "similar enough to" the given
data objects, depending on some similarity metric, e.g., Euclidean distance,
cosine similarity and so on. To measure the similarity between data objects,
traditional methods normally work on low level or syntax features(e.g., basic
visual features on images or bag-of-word features of text), which makes them
weak to compute the semantic similarities between objects. So for measuring
data similarities semantically, neural embedding is applied. Embedding
techniques work by representing the raw data objects as vectors (so called
"embeddings" or "neural embeddings" since they are mostly generated by neural
network models) that expose the hidden semantics of the raw data, based on
which embeddings do show outstanding effectiveness on capturing data
similarities, making it one of the most widely used and studied techniques in
the state-of-the-art similarity query processing research. But there are still
many open challenges on the efficiency of embedding based similarity query
processing, which are not so well-studied as the effectiveness. In this survey,
we first provide an overview of the "similarity query" and "similarity query
processing" problems. Then we talk about recent approaches on designing the
indexes and operators for highly efficient similarity query processing on top
of embeddings (or more generally, high dimensional data). Finally, we
investigate the specific solutions with and without using embeddings in
selected application domains of similarity queries, including entity resolution
and information retrieval. By comparing the solutions, we show how neural
embeddings benefit those applications.
