Trending topics on science, a tensor memory hypothesis approach

Abstract

The current human knowledge is written. Documenting is the most used manner to preserve memories and to store fantastic stories. Thus, to distinguish the reality from fiction, the scientific writing cites previous works moreover than become form experimental setups. Books and scientific papers are only a small part of the existent literature but are considered more thrust as information sources. It is useful to find more relations and to know where to focus the lookup of a topic using the information about the authors and the keywords on the titles and abstracts. This is possible using relational databases or knowledge graphs, a semantic approach, but with the tensor memory hypothesis, that adds a temporal dimension, is possible to process the information with an episodic memory approach. If well, knowledge graphs are of extended use on question answering and chatbots, they need a previous relational schema generated automatically or by-hand and stored in an easy-to-query file format. I use JATS, a standard format that allows integrating scientific papers in semantic searches but is not spread on all scientific publishers, to extract the markup tags from PDF files, current year journal articles of one particular topic, and then construct the tensors memory with their references to extract relations and predictions with statistical relational learning techniques.