基于文献信息网络语义特征的相似性搜索

文献信息网络是典型的异构信息网络，基于其进行相似性搜索是图挖掘领域的一个研究热点。然而，现有的方法主要采用元路径或元结构的方式，并未考虑节点自身的语义特征，从而导致搜索结果出现偏差。对此，基于文献信息网络提出了一种基于向量的语义特征提取方法，并设计实现了基于向量的节点相似性计算方法VSim；此外，结合元路径设计了基于语义特征的相似性搜索算法VPSim；为提高算法的执行效率，针对文献网络数据的特点，设计了剪枝策略。通过在真实数据上的实验，验证了VSim对搜索语义特征相似实体的适用性，以及VPSim算法的有效性、高执行效率和高可扩展性。

Similarity search based on semantic features of bibliographic information network

Bibliography information network is a typical heterogeneous information network and the similarity search based on it is a hot topic of graph mining. However, current methods mainly adopt meta path or meta structure to search similar objects, do not consider semantic features of node itself which leads to a deviation in the search results. To fill this gap, a vector-based semantic feature extraction method was proposed, and a vector-based node similarity calculation method called VSim was designed and implemented. In addition, a similarity search algorithm VPSim (Similarity computation Based on Vector and meta Path) based on semantic features was designed by combining the meta-paths. In order to improve the execution efficiency of the algorithm, a pruning strategy based on the characteristics of bibliographic network data was designed. Experiments on real-world data sets demonstrate that VSim is applicative for searching entities with similar semantic features and VPSim is effective, efficient and extensible.