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 (eg, 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 literaturebased knowledge discovery (eg, drug target prediction).

Date

2017

Authors

Xiang Ren, Jiaming Shen, Meng Qu, Xuan Wang, Zeqiu Wu, Qi Zhu, Meng Jiang, Fangbo Tao, Saurabh Sinha, David Liem, Peipei Ping, Richard Weinshilboum, Jiawei Han

Conference

Proceedings of ACL 2017, System Demonstrations

Pages

55-60