Semantic text mining support for lignocellulose research

Abstract

BackgroundBiofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties.ResultsSemantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources.ConclusionsWorking closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information.

IntelliGenWiki: An Intelligent Semantic Wiki for Life Sciences

“…The task we aimed to support in the project is biomedical literature curation for lignocellulose research. For this experiment, we deployed the mycoMINE NLP pipeline (Meurs et al, 2012), which automatically extracts knowledge from the literature on fungal enzymes by using semantic text mining approaches combined with ontological resources. We manually pre-filled the wiki with a corpus of 30 documents composed of PubMed abstracts and their corresponding full-text papers, selected by two expert biocurators.…”

Empowering web portal users with personalized text mining services

“…To support these researchers, we automatically import new articles appearing on PubMed into a portal (Figure 1), processing them with the mycoMINE NLP pipeline (Meurs et al, 2012), which extracts entities and facts related to fungal enzymes. The Query portlet displays user's search queries.…”

Personalized semantic assistance for the curation of biochemical literature

“…Depending on the type of assistant, its results can be displayed in the source text, as an index, a map, or a text in a side portlet. For instance, Figure 3 displays results of the mycoMINE assistant [14], which extracts entities and facts related to fungal enzymes involved in lignocellulose degradation, such as enzymes, organisms, genes, substrates, pH, temperature or activity assay conditions. The entities extracted by the assistant, as selected in Figure 2, are underlined in the text of publications listed in the origin portlet and displayed as an index in a side portlet.…”

“…For annotation, it uses NLP pipelines provided by the Semantic Assistants framework [13]. For instance, for annotation of biochemical literature related to lignocellulosic degradation, it uses the mycoMINE pipeline [14]. For every extracted entity type, the service identifies an appropriate concept of the domain ontology based on semantic type mapping, which maps the ontology scheme of the NLP tools to the scheme of the domain ontology.…”