Tensor based document retrieval over Neo4j with an application to PubMed mining
PubMed mining is currently at the epicenter of intense interdisciplinary research. Text mining methodologies provide a way to retrieve and analyze emotionally charged words, punctuation, and syntax. Moreover, they can analyze scientific literature and process document collections. Moving beyond traditional document term matrix representation, an architecture for content based retrieval from PubMed is proposed whose core is a document term author third order tensor. This methodology has been implemented in Python over Neo4j and has been applied to a PubMed document article collection.
2016 7th International Conference on Information, Intelligence, Systems Applications (IISA)