We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org/.

Employing ethyl 2-oxocyclohexanecarboxylate as a novel, efficient, and versatile ligand, the copper-catalyzed coupling reactions of various N/O/S nucleophilic reagents with aryl halides could be successfully carried out under mild conditions. A variety of products including N-arylamides, N-arylimidazoles, aryl ethers, and aryl thioethers were synthesized in good to excellent yields.

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Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.

An efficient and versatile method for the assembly of novel polycyclic benzimidazole derivatives has been developed by Cu-catalyzed domino addition/double cyclization reactions. A wide variety of polycyclic benzimidazole derivatives, which might be used as synthetic medicines and functional materials, were successfully assembled from bis-(o-haloaryl)carbodiimides. Unexpected N-methylated benzo[4,5]imidazo[1,2-a]indoles can also be selectively assembled. Multibonds and polycyclic moieties were conveniently formed in one pot during these domino processes.