Visualizing semantic table annotations with TableMiner+

This paper describes an extension of the TableMiner+ sys- tem, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and e�fficient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web

A tool for creating and visualizing semantic annotations on relational tables

Semantically annotating content from relational tables on the Web is a crucial task towards realizing the vision of the Semantic Web. However, there is a lack of open source, user-friendly tools to facilitate this. This paper describes an extension of the TableMiner+ system, an open source Semantic Table Interpretation system that automatically annotates Web tables using Linked Data in an effective and effi�cient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on relational tables, which facilitates the publication of Linked Data on the Web

Genomics data integration for knowledge discovery using genome annotations from molecular databases and scientific literature

One of the major global challenges of today is to meet the food demands of an ever increasing population (food demand will increase by 50% in 2030). One approach to address this challenge is to breed new crop varieties that yield more even under unfavorable conditions e.g. have improved tolerance to drought and/or resistance to pathogens. However, designing a breeding program is a laborious and time consuming effort that often lacks the capacity to generate new cultivars quickly in response to the required traits. Recent advances in biotechnology and genomics data science have the potential to accelerate and precise breeding programs greatly. As large-scale genomic data sets for crop species are available in multiple independent data sources and scientific literature, this thesis provides innovative technologies that use natural language processing (NLP) and semantic web technologies to address challenges of integrating genomic data for improving plant breeding. Firstly, in this research study, we developed a supervised Natural language processing (NLP) model with the help of IBM Watson, to extract knowledge networks containing genotypic-phenotypic associations of potato tuber flesh color from the scientific literature. Secondly, a table mining tool called QTLTableMiner++ (QTM) was developed which enables knowledge discovery of novel genomic regions (such as QTL regions), which positively or negatively affect the traits of interest. The objective of both above mentioned, NLP techniques was to extract information which is implicitly described in the literature and is not available in structured resources, like databases. Thirdly, with the help of semantic web technology, a linked-data platform called Solanaceae linked data platform(pbg-ld) was developed, to semantically integrates geno- and pheno-typic data of Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. Lastly, analysis workflows for prioritizing candidate genes with QTL regions were tested using pbg-ld. Hence, this research provides in-silico knowledge discovery tools and genomic data infrastructure, which aids researchers and breeders in the design of a precise and improved breeding program.</p