Towards ontology-driven navigation of the lipid bibliosphere.
BACKGROUND
The indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. The emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimer's syndrome, Mycobacterium infections and cancer.


RESULTS
We present a literature-driven workflow involving document delivery and natural language processing steps generating tagged sentences containing lipid, protein and disease names, which are instantiated to custom designed lipid ontology. We describe the design challenges in capturing lipid nomenclature, the mandate of the ontology and its role as query model in the navigation of the lipid bibliosphere. We illustrate the extent of the description logic-based A-box query capability provided by the instantiated ontology using a graphical query composer to query sentences describing lipid-protein and lipid-disease correlations.


CONCLUSIONS
As scientists accept the need to readjust the manner in which we search for information and derive knowledge we illustrate a system that can constrain the literature explosion and knowledge navigation problems. Specifically we have focussed on solving this challenge for lipidomics researchers who have to deal with the lack of standardized vocabulary, differing classification schemes, and a wide array of synonyms before being able to derive scientific insights. The use of the OWL-DL variant of the Web Ontology Language (OWL) and description logic reasoning is pivotal in this regard, providing the lipid scientist with advanced query access to the results of text mining algorithms instantiated into the ontology. The visual query paradigm assists in the adoption of this technology.
Baker
Christopher Jo
CJ
Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg
Kanagasabai
Rajaraman
R
Ang
Wee Tiong
WT
Veeramani
Anitha
A
Low
Hong-Sang
HS
Wenk
Markus R
MR
D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D015706:Bibliometrics; D003628:Database Management Systems; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D050356:Lipid Metabolism; D008055:Lipids / Q000145:classification*; D008659:Metabolic Diseases / Q000145:classification* / Q000378:metabolism*; D009323:Natural Language Processing; D010506:Periodicals as Topic
D008055:Lipids