Knowledge Graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results. These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations. Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory. In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment. Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entity-pairs and edges correspond to relation-pairs; it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities. To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes; and a graph neural network is employed to propagate the similarity features and get the final embeddings of entity-pairs. Experiments on five real-world datasets show that our approach can achieve the state-of-the-art KG alignment results.

Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners. In this work, we present MAssistant system, a personal knowledge assistant for MOOC learners. MAssistant helps users to trace the concepts they have learned in MOOCs, and to build their own concept graphs. There are three key components in MAssistant: (i) a large-scale concept graph built from open data sources, which contains concepts in various domains and relations among them; (ii) a browser extension which interacts with learners when they are watching video lectures, and presents important concepts to them; (iii) a web application allowing users to explore their personal concept graphs, which are built based on their learning activities on MOOCs. MAssistant will facilitate the knowledge management task for MOOC learners, and make the learning on MOOCs easier.