Publication type: Conference paper
Type of review: Peer review (publication)
Title: Towards creating a new triple store for literature-based discovery
Authors: Koroleva, Anna
Anisimova, Maria
Gil, Manuel
et. al: No
DOI: 10.1007/978-3-030-60470-7_5
Proceedings: Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2020
Editors of the parent work: Lu, Wei
Zhu, Kenny Q.
Pages: 41
Pages to: 50
Conference details: PAKDD 2020 Workshops, DSFN, GII, BDM, LDRC and LBD, Singapore, 11-14 May 2020
Issue Date: 15-Oct-2020
Series: Lecture Notes in Computer Science
Series volume: 12237
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-60469-1
978-3-030-60470-7
ISSN: 0302-9743
1611-3349
Language: English
Subjects: Literature-based discovery; Triple store; Semantic web; Information extraction
Subject (DDC): 006: Special computer methods
Abstract: Literature-based discovery (LBD) is a field of research aiming at discovering new knowledge by mining scientific literature. Knowledge bases are commonly used by LBD systems. SemMedDB, created with the use of SemRep information extraction system, is the most frequently used database in LBD. However, new applications of LBD are emerging that go beyond the scope of SemMedDB. In this work, we propose some new discovery patterns that lie in the domain of Natural Products and that are not covered by the existing databases and tools. Our goal thus is to create a new, extended knowledge base, addressing limitations of SemMedDB. Our proposed contribution is three-fold: 1) we add types of entities and relations that are of interest for LBD but are not covered by SemMedDB; 2) we plan to leverage full texts of scientific publications, instead of titles and abstracts only; 3) we envisage using the RDF model for our database, in accordance with Semantic Web standards. To create a new database, we plan to build a distantly supervised entity and relation extraction system, employing a neural networks/deep learning architecture. We describe the methods and tools we plan to employ.
URI: https://digitalcollection.zhaw.ch/handle/11475/21572
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Published as part of the ZHAW project: Computational literature-based natural product drug discovery
Appears in collections:Publikationen Life Sciences und Facility Management

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