知识图谱是将无结构的知识进行结构化表示和组织的有效途径, 已经成为支持众多智能应用的基础设施. 然而, 与商品相关的知识通常呈现出海量性、异质性和层次性的特点, 这对现有基于关系模型和图模型的知识查询处理方法提出了挑战. 针对商品知识的这些特点, 本文设计与实现了一种利用CPU和GPU协同计算的商品知识查询处理方法. 首先, 为了充分发挥GPU的并行计算能力, 提出了一种基于稀疏矩阵的商品知识存储策略, 并针对商品知识进行存储优化; 其次, 根据稀疏矩阵的存储结构设计了一种查询转换方式, 将SPARQL查询转化为对应的矩阵计算, 并将连接查询算法扩展到GPU上进行加速. 为了验证所提出方法的有效性, 我们在LUBM数据集和一个半合成的商品数据集上进行了一系列的实验. 结果表明, 本文提出的方法, 不仅在海量商品知识下相对于现有RDF查询引擎在检索效率上有较大提升, 而且在通用的RDF标准数据集上也能取得较好的检索性能, 并验证了GPU加速查询处理的有效性.

Knowledge graphs are an effective way to structurally represent and organize unstructured knowledgeare; in fact, these graphs are commonly used to support many intelligent applications. However, product-related knowledge is typically massive in scale, heterogeneous, and hierarchical; these characteristics present a challenge for traditional knowledge query processing methods based on relational and graph models. In this paper, we propose a solution to address these challenges by designing and implementing a product knowledge query processing method using CPU and GPU collaborative computing. Firstly, in order to leverage the full parallel computing capability of GPU, a product knowledge storage strategy based on a sparse matrix is proposed and optimized for the scale of the task. Secondly, based on the storage structure of the sparse matrix, a query conversion method is designed, which transforms the SPARQL query into a corresponding matrix calculation, and extends the join query algorithm to the GPU for acceleration. In order to verify the effectiveness of the proposed method, we conducted a series of experiments on an LUBM dataset and a semisynthetic dataset of products. The experimental results showed that the proposed method not only improves retrieval efficiency for large-scale product knowledge datasets compared with existing RDF query engines, but also achieves better retrieval performance on a general RDF standard dataset.