A bidirectional attention mechanism is applied between the question sequence and the paths that connect entities, which provides us with transparent interpretability.
Recently, increasing attention has been devoted to the graph few-shot learning problem, where the target novel classes only contain a few labeled nodes.
Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.
Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus.
Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus.