A curation pipeline for bio-derived chemical feedstocks
Creators: name: Demetriou, George
name: Read, Warren
name: Fletcher, Martyn
name: Ruddock, Noel
name: Nenadic, Goran
name: Jackson, Tom
name: Stevens, Robert
name: Winter, Jerry
Publisher: name: F1000Research
Year: 2016

description: The BioHub Information and Knowledge Management System (IKMS) aims to support the process of identifying chemical ingredients that can be sourced from  sustainable biomass as alternatives to those from non-renewable resources such as fossil oils or earth minerals.<br><br>The curation of chemical data in BioHub is performed in three stages:<br><br>(i)  A text mining stage whose aim is to mine facts about chemicals directly from the scientific literature (journal papers, patents, laboratory reports).<br><br>(ii) An 'assertion generation' stage where 'assertions'  (i.e. factual statements about feedstocks and chemicals) are selected as candidates for curation by querying the text mining results.<br><br>(iii) A curation stage  that allows curators to browse, edit, validate and store the assertions to the system's final data store.<br><br>The text analytics system of BioHub is developed within the GATE platform. It processes documents in order to extract relevant information about chemicals such as the feedstock streams from which they are derived, their physical and chemical properties, possible transformations applied to them etc. This information is exported to OWL data stores enriched with links to the parts of the texts from which it comes.<br><br>Curation is not performed directly on the text mining results but on the output of semantic queries applied to them. Assertions are generated on-the-fly via  SPARQL-ing the OWL output and are transformed to JSON objects ready to be parsed and presented in tabular format on the BioHub curation User Interface (UI). The conceptual structure of an assertion is pre-specified. An example of an assertion type is "feedstock-has-chemical-with-proportion". This is derived from two binary  relations (triples) identified in the text mining stage i.e. "feedstock-has-chemical" and "chemical-has proportion", both of which may have been extracted from the same part of the text (usually a sentence) and have been linked together by querying the text mining data.<br><br>The main goal of the curation UI is to populate the BioHub data repository with validated assertions. Secondary goals are to aid performance evaluation and provide curated data for system training and refinement. Its design has been influenced by considerations of an intuitive interface aiming at rapid curation rather than considerations of a full-fledged annotation editor which might require much more user intervention and time resources for curating full text documents. The UI is employable by a Web-based client with client-side services for input data and server-side facilities for storing the final results. Its editing engine includes capabilities such as context-enabled curation (i.e. allows access to the text sentences, paragraphs or full text), content-editable fields for entities, grouping/sorting of assertions per various facets, logging, etc.<br><br>The results of the curation stage are stored to the BioHub data repository and are used to support subsequent stages in the IKMS, such as the selection of  ingredients based on functional characteristics and the computational optimisation of chemical pathways.<br><br>The curation architecture of the BioHub IKMS demonstrates how text mining and semantic web technologies can be integrated within a distributed, goal-oriented curation infrastructure to facilitate the semi-automated development of knowledge bases in the chemical domain.<br>