Paper Info
Title
Life-Inet: A Structured Network-Based Knowledge Exploration And Analytics System For Life Sciences
Abstract
Search engines running on scientific literature have been widely used by life scientists to find publications related to their research. However, existing search engines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowledge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that factual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively imply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in existing search engines on facilitating life sciences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of background documents, to support efficient exploration of structured factual knowledge in the unstructured literature. It also provides functionalities for finding distinctive entities for given entity types, and generating hypothetical facts to assist literature-based knowledge discovery (e.g., drug target prediction).
Year
DOI
Venue
2017
10.18653/v1/P17-4010
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017): SYSTEM DEMONSTRATIONS
Field
DocType
Volume
Data science,Software analytics,Computer science,Inet,Analytics
Conference
P17-4
Citations 
PageRank 
References 
1
0.35
10
Authors
13
Name
Order
Citations
PageRank
Xiang Ren188560.08
Jiaming Shen2569.05
Meng Qu3116337.34
Xuan Wang4285.96
Zeqiu Wu5594.11
Qi Zhu6273.78
Meng Jiang770647.57
Fangbo Tao8967.53
Saurabh Sinha952948.96
David Liem1011.02
Peipei Ping11414.13
Richard M. Weinshilboum12153.10
Jiawei Han13430853824.48