Tensor-based Document Retrieval over Neo4j with an Application to PubMed Mining

被引：0
|
作者：
Drakopoulos, Georgios [1 ]
Kanavos, Andreas [1 ]
机构：
[1] Univ Patras, Comp Engn & Informat Dept, Patras 26500, Hellas, Greece
来源：
2016 7TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS & APPLICATIONS (IISA) | 2016年
关键词：
PubMed; Entrez; Tensor algebra; Medical information retrieval; Topic clustering; MeSH ontology; Text mining; Neo4j; ONTOLOGY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：6
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