大规模演化知识网络中的关联推理

Link Inference in Large Scale Evolutionable Knowledge Network

网络大数据时代的到来使得知识网络中时空信息越来越丰富.现有的知识网络描述模型对知识的时空信息刻画不足.研究证明,利用网络中知识的时空信息以及相关性,能够提高网络中知识间的关联推理的准确率.针对以上问题,首先提出了一种包含时空信息的演化知识网络表示模型,然后研究在该网络模型上的关联推理问题,提出了一种基于背包问题的知识间关联推理方法.在多个数据集上的实验证明了所提出的关联推理方法的有效性以及对大规模知识网络的适应性.

In the era of network big data, the spatiotemporal information of knowledge is richly stored in knowledge networks, such as the building time of links, the lifetime of vertices, etc. Traditional knowledge network representation models are mostly blind to either the spatial or the temporal information of vertices and links in the network. It has been verified in the literature that considering the spatial or the temporal information of vertices and links can promote the performance of link inference in knowledge networks. In this paper, we propose an evolutionable knowledge network model, i.e., a heterogeneous knowledge network, in which vertices and edges are anchored in both time and space dimensions. Then based on the model, we further study the link inference problem on evolutionable knowledge networks. Specifically, we firstly define the link extendable patterns to characterize the link formation process, and then propose a knapsack constrained link inference method to turn the link inference problem into a combinatorial optimization problem with the knapsack-like constrains. The dynamic programming technique is used to solve the optimization problem in pseudo-polynomial time complexity. Experiments on real data sets suggest the better effectiveness and scalability of our proposed method over large-scale networks than the state-of-the-art methods.