Paper Info
Title
KnowLife: A knowledge graph for health and life sciences.
Abstract
Knowledge bases (KB's) contribute to advances in semantic search, Web analytics, and smart recommendations. Their coverage of domain-specific knowledge is limited, though. This demo presents the KnowLife portal, a large KB for health and life sciences, automatically constructed from Web sources. Prior work on biomedical ontologies has focused on molecular biology: genes, proteins, and pathways. In contrast, KnowLife is a one-stop portal for a much wider range of relations about diseases, symptoms, causes, risk factors, drugs, side effects, and more. Moreover, while most prior work relies on manually curated sources as input, the KnowLife system taps into scientific literature as well as online communities. KnowLife uses advanced information extraction methods to populate the relations in the KB. This way, it learns patterns for relations, which are in turn used to semantically annotate newly seen documents, thus aiding users in "speed-reading". We demonstrate the value of the KnowLife KB by various use-cases, supporting both layman and professional users.
Year
DOI
Venue
2014
10.1109/ICDE.2014.6816754
ICDE
Keywords
Field
DocType
knowledge based systems,medical computing,portals,KnowLife portal,KnowLife system,Web analytics,Web sources,biomedical ontologies,documents annotation,domain-specific knowledge,genes,health science,knowledge base,knowledge graph,life science,molecular biology,online communities,pathways,proteins,scientific literature,semantic search,smart recommendations,speed-reading
Data mining,Scientific literature,Knowledge graph,World Wide Web,Unified Modeling Language,Semantic search,Open Biomedical Ontologies,Web analytics,Computer science,Information extraction,Semantics,Database
Conference
ISSN
Citations 
PageRank 
1084-4627
10
0.50
References 
Authors
9
4
Name
Order
Citations
PageRank
Patrick Ernst1706.51
Meng, C.2100.50
Siu, A.3100.50
Gerhard Weikum4127102146.01