Deep learning for Knowledge-Graph
A curated Chinese-language collection of research summaries, papers and resource links on knowledge graphs, natural language processing and dialogue systems, including a survey of open-source question-answering systems.
Deep learning for Knowledge-Graph is a curated collection of research summaries and resource links on knowledge graph construction, question answering and dialogue systems, maintained since 2019 and written mainly in Chinese. It links out to external resources tracking the state of the art in NLP tasks and to a papers-with-code index, and organizes its own research summaries into topical sections, including the history of knowledge graphs, theoretical explainers on knowledge graphs and question answering, analysis of NLP papers such as the Transformer, BERT, ERNIE and T5, and research on structuring Chinese financial documents.
It also lists major academic conferences and journals relevant to knowledge graphs and natural language processing with their ranking and field, catalogs mainstream open-source question-answering and dialogue systems and semantic platforms, and separately lists Chinese and English text-preprocessing tools, graph storage and query tools, visualization tools, and knowledge-graph datasets.
The project accepts contributions from others interested in the topic.