## 4 Discussion & Conclusions  
The main objective of developing the pbg-ld platform was to improve the FAIRness of candidate gene identification in _Solanaceae_ species by providing (semantically) integrated genomics and QTL datasets available in public resources (i.e., UniProt, Ensembl Plants, SGN and Europe PMC) from a central endpoint. Genomic knowledge discovery is often confronted by the challenges of data integration from a multitude of independent databases and research articles. For discovering candidate genes with the help of large-scale data integration, there is a need to organize candidate data resources according to the FAIR data principles. The core development in this research is a Linked Data platform that semantically organizes and integrates genotypic and phenotypic data on _Solanaceae_ species according to these principles. Hence, this progress in digital science helps genomic datasets to be more findable, accessible, interoperable and reusable. After selecting various datasets and information relevant to candidate gene discovery, a critical step in the knowledge discovery process is the transformation of data into a suitable data infrastructure. Biological data are complex and highly connected, for example, there is ambiguity in the names of genes, proteins and transcripts, hence a semantic model with correct identifiers is required to differentiate them. pbg-ld addresses the challenges of providing a semantic layer over most used datasets for candidate gene discovery in tomato and potato. A critical step in our approach was the transformation of (semi-)structured or non-RDF data sources to inter-linked RDF graphs using existing and newly developed tools such as the QTM and SIGA.py. Further, FDP provides meta-data descriptions, which makes the user aware of the source graph(s) to perform queries and to interpret the results. Different data access points provide flexibility for users who wish to analyze and/or visualize data on this platform. Lastly, open accessibility of all the above mentioned tools used to generate and publish these data sets, offers the ability for the scientific community users to extend this tool for other crop species themselves. Data sets are not static and constantly emerging over time. pbg-ld combines open data from different third party resources such as Europe PMC, SGN, UniProt and Ensemble Plants. As these data sets are not static, a significant improvement in pbg-ld could be to automate the process of regular updates of the data sets. For example, QTL information in pbg-ld is extracted from tables of scientific articles in Europe PMC using the QTM tool. However, QTL data graphs are static and require manual updates. Ideally, the QTL data graph should be updated automatically, whenever a new QTL study is published. A researcher is interested in retrieving the most newly published scientific articles in the domain of his/her research interests. Another improvement to the pbg-ld platform could be to enable (interactive) visualization of the knowledge graphs. Complementary to our developments, some similar plant-specific software and databases provide genotypic and phenotypic data sets in a semantically integrated way. For example, KNETMiner is an open source software that integrates plant-specific biological data sets into a knowledge graph [80]. These biological data sets contain information related to genes, biological pathways, phenotypes and publications for many important crop species like wheat, rice, sorghum, potato, tomato, and so forth. Additionally, KNETMiner has an evidence-based gene ranking algorithm that ranks and visualizes this integrated data based on gene annotations. However, the current version of KNETMiner [81] provides free-access to integrated data sets only for some important crops like wheat (_Triticum aestivum_), rice (_Oryza sativa_), _Arabidopsis thaliana_. The integrated data set for other crop species like, tomato (_S. lycopersicum_), potato (_S. tuberosum_), and sorghum (_Sorghum bicolor_) is are not openly accessible.