Affordable Access

Access to the full text

LIVIVO – the Vertical Search Engine for Life Sciences

Authors
  • Müller, Bernd1
  • Poley, Christoph1
  • Pössel, Jana1
  • Hagelstein, Alexandra1
  • Gübitz, Thomas1
  • 1 German National Library of Medicine ZB MED – Information Centre for Life Sciences, Applied Research and Innovation, Gleueler Str. 60, Cologne, North Rhine-Westphalia, 50931, Germany , Cologne (Germany)
Type
Published Article
Journal
Datenbank-Spektrum
Publisher
Springer Berlin Heidelberg
Publication Date
Jan 18, 2017
Volume
17
Issue
1
Pages
29–34
Identifiers
DOI: 10.1007/s13222-016-0245-2
Source
Springer Nature
Keywords
License
Green

Abstract

The explosive growth of literature and data in the life sciences challenges researchers to keep track of current advancements in their disciplines. Novel approaches in the life science like the One Health paradigm require integrated methodologies in order to link and connect heterogeneous information from databases and literature resources. Current publications in the life sciences are increasingly characterized by the employment of trans-disciplinary methodologies comprising molecular and cell biology, genetics, genomic, epigenomic, transcriptional and proteomic high throughput technologies with data from humans, plants, and animals. The literature search engine LIVIVO empowers retrieval functionality by incorporating various literature resources from medicine, health, environment, agriculture and nutrition. LIVIVO is developed in-house by ZB MED – Information Centre for Life Sciences. It provides a user-friendly and usability-tested search interface with a corpus of 55 Million citations derived from 50 databases. Standardized application programming interfaces are available for data export and high throughput retrieval. The search functions allow for semantic retrieval with filtering options based on life science entities. The service oriented architecture of LIVIVO uses four different implementation layers to deliver search services. A Knowledge Environment is developed by ZB MED to deal with the heterogeneity of data as an integrative approach to model, store, and link semantic concepts within literature resources and databases. Future work will focus on the exploitation of life science ontologies and on the employment of NLP technologies in order to improve query expansion, filters in faceted search, and concept based relevancy rankings in LIVIVO.

Report this publication

Statistics

Seen <100 times