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.

引用

页数：6

共 30 条

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