Dr. Kun Lu studied electronic engineering in TU München and continued his doctor work there from 2010 to 2014. He started his first industrial job in 2014 by Siemens in Erlangen, working as a developer for electrical automobiles. Upon deciding that he is rather interested in machine learning, he returned to Munich in 2015 for a new job at SHS Viveon, where he was a consultant in the Business Intelligence team. There he was involved in Data-Warehousing and Big-Data projects. Now he is with metaFinanz, specializing on Data-Mining especially Web-Mining.
This talk is about applying text-mining on academic publications to extract information such as knowledge graphs of related concepts. The overall goal is to help researchers to better and faster explore a large amount of text documents which in this case are academic publications. Given the text of thousands or millions of publications, I will show 1) how to use natural language processing techniques to extract concepts 2) how to use statistics and bayersian theory to identify related concepts 3) how to use a short-long-term memory mechanism to learn related concepts in a sequential manner. In the end, we will be able to obtain a „knowledge graph“ among the concepts through this text-mining process. Part of the results could be found at: www.neuronbit.io. The result of this work can also be useful for semantic search, document classification, information retrieval, etc.