There is an increasing adoption of machine learning for encoding data into
vectors to serve online recommendation and search use cases. As a result,
recent data management systems propose augmenting query processing with online
vector similarity search. In this work, we explore vector similarity search in
the context of Knowledge Graphs (KGs). Motivated by the tasks of finding
related KG queries and entities for past KG query workloads, we focus on hybrid
vector similarity search (hybrid queries for short) where part of the query
corresponds to vector similarity search and part of the query corresponds to
predicates over relational attributes associated with the underlying data
vectors. For example, given past KG queries for a song entity, we want to
construct new queries for new song entities whose vector representations are
close to the vector representation of the entity in the past KG query. But
entities in a KG also have non-vector attributes such as a song associated with
an artist, a genre, and a release date. Therefore, suggested entities must also
satisfy query predicates over non-vector attributes beyond a vector-based
similarity predicate. While these tasks are central to KGs, our contributions
are generally applicable to hybrid queries. In contrast to prior works that
optimize online queries, we focus on enabling efficient batch processing of
past hybrid query workloads. We present our system, HQI, for high-throughput
batch processing of hybrid queries. We introduce a workload-aware vector data
partitioning scheme to tailor the vector index layout to the given workload and
describe a multi-query optimization technique to reduce the overhead of vector
similarity computations. We evaluate our methods on industrial workloads and
demonstrate that HQI yields a 31x improvement in throughput for finding related
KG queries compared to existing hybrid query processing approaches.

本文介绍了一种名为 HQI 的系统，用于高吞吐量批处理混合查询，特别是在知识图谱中的向量相似度搜索。该系统使用一种基于负载的向量数据分区方案来量身定制向量索引布局，同时描述了一种多查询优化技术以减少向量相似度计算的开销。我们基于工业工作负载评估了 HQI 的方法，并证明与现有的混合查询处理方法相比，HQI 在查找相关 KG 查询方面的吞吐量提高了 31 倍。