Calling on a million minds for community annotation in WikiProteins

Abstract

WikiProteins enables community annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment that links out to the original sources. Data from community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at http://www.wikiprofessional.org.A preview of the version highlighted by WikiProfessional is available at:

How the New Web Generations are Changing Library and Information Services

“…Starting from frequent meta-analysis of biomedical ontologies, published databases, the wiki itself, and new sentences collected by the community from the broad scientific literature, the system will actively push new or suggested associations to experts for review and annotation in a Wiki-environment. 25 The U.S. National Library of Medicine has developed the Unified Medical Language System (UMLS), whose purpose is to aid in the development of systems that help health professionals and researchers retrieve and integrate electronic biomedical information from a variety of sources. The UMLS Semantic Network consists of a set of broad subject categories that provide a consistent classification of all concepts present in the UMLS Metathesaurus.…”

The value of data

“…We have all heard lamentations about datasets being difficult to find and the painfully slow increase in annotation and curation by the scientific community 1 . One bottleneck is the lack of a scientific reward system for depositing and curating data outside the mainstream of publishing conventional articles.…”

Ceci n'est pas un hamburger: modelling and representing the scholarly article

“…This is much more tractable than attempting to write formal machine-readable language for entire articles, and gives more reliable results than automated text-mining. Schemes for structuring and attributing these annotations as 'nanopublications', 13,14 15 is a viable way forward remains largely untested. What is clear is that whichever group of players ultimately takes on the task of manual annotation, the process will require user-friendly tools for creating annotations and managing the associated ontologies; it will also necessitate confidence from authors and publishers that such article mark-up adds sufficient value to warrant both the extra effort involved and its associated heavy price-tag.…”