Information networks provide a powerful representation of entities and the relationships between them. Information networks fusion is a technique for information fusion that jointly reasons about entities, links and relations in the presence of various sources. However, existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for reasoning. In order to solve this issue, in this paper, we present a novel model called MC-INFM (information networks fusion model based on multi-task coordination). Different from traditional models, MC-INFM casts the fusion problem as a probabilistic inference problem, and collectively performs multiple tasks (including entity resolution, link prediction and relation matching) to infer the final result of fusion. First, we define the intra-features and the inter-features respectively and model them as factor graphs, which can provide abundant evidence to infer. Then, we use conditional random field (CRF) to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic inference. Experiments demonstrate the effectiveness of our proposed model.

信息网络提供了实体及其之间关系的有力表示。信息网络融合是一种用于信息融合的技术，该技术在存在各种来源的情况下共同对实体，链接和关系进行推理。但是，现有的信息网络融合方法往往依赖于单个任务，这可能无法获得足够的推理依据。为了解决这个问题，在本文中，我们提出了一种称为MC-INFM的新型模型（基于多任务协调的信息网络融合模型）。与传统模型不同，MC-INFM将融合问题转换为概率推理问题，并共同执行多项任务（包括实体解析，链接预测和关系匹配）以推断融合的最终结果。第一的，我们分别定义了内部特征和内部特征，并将它们建模为因子图，这可以提供丰富的推断依据。然后，我们使用条件随机场（CRF）来学习每个特征的权重，并通过执行最大概率推断来同时推断这些任务的结果。实验证明了我们提出的模型的有效性。