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Link inference, i.e., inferring links between vertices in a heterogeneous information network with heterogeneous vertices and edges, has been extensively studied in recent years. So far, many machine learning-based methods have been proposed for link inference, which can be classified into two categories, namely, supervised and unsupervised. Supervised methods perform well but highly rely on feature...
Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e.g., attribute comparison based methods, clustering based methods, and active learning methods) are usually supervised by labelled data as prior knowledge. Since it is not trivial to label data for training, researchers have recently turned to unsupervised methods, and have thus developed...
With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open...
In social networks, predicting a user's locations through those of his or her friends mainly relies on the selection method of the most influential friends of the user, which most of the existing location prediction methods fail to attach importance to. In this paper, we firstly present an analytical procedure in regard to the calculation of the theoretical maximum accuracy for location prediction...
Populating a knowledge base with new entity mentions extracted from unstructured text can help enhance its coverage and freshness. It naturally consists of two subtasks, namely, fine-grained entity classification and entity linking. Existing studies often focus on one of these two subtasks and they usually populate entity mentions in the same text by implicitly assuming that they are independent....
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