TY - GEN
T1 - Life-iNet
T2 - 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
AU - Ren, Xiang
AU - Shen, Jiaming
AU - Qu, Meng
AU - Wang, Xuan
AU - Wu, Zeqiu
AU - Zhu, Qi
AU - Jiang, Meng
AU - Tao, Fangbo
AU - Sinha, Saurabh
AU - Liem, David
AU - Ping, Peipei
AU - Weinshilboum, Richard
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics
PY - 2017
Y1 - 2017
N2 - Search engines running on scientific liter ature have been widely used by life sci entists to find publications related to their research. However, existing search en gines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowl edge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that fac tual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively im ply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in exist ing search engines on facilitating life sci ences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of back ground documents, to support efficient ex ploration of structured factual knowledge in the unstructured literature. It also pro vides functionalities for finding distinctive entities for given entity types, and gener ating hypothetical facts to assist literature-based knowledge discovery (e.g., drug tar get prediction).
AB - Search engines running on scientific liter ature have been widely used by life sci entists to find publications related to their research. However, existing search en gines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowl edge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that fac tual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively im ply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in exist ing search engines on facilitating life sci ences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of back ground documents, to support efficient ex ploration of structured factual knowledge in the unstructured literature. It also pro vides functionalities for finding distinctive entities for given entity types, and gener ating hypothetical facts to assist literature-based knowledge discovery (e.g., drug tar get prediction).
UR - http://www.scopus.com/inward/record.url?scp=85051463483&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051463483&partnerID=8YFLogxK
U2 - 10.18653/v1/P17-4010
DO - 10.18653/v1/P17-4010
M3 - Conference contribution
AN - SCOPUS:85051463483
SN - 9781945626715
T3 - ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations
SP - 55
EP - 60
BT - ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations
PB - Association for Computational Linguistics (ACL)
Y2 - 30 July 2017 through 4 August 2017
ER -