The semantic web could offer a common framework that allows data to be shared and reused across canonical form, application, enterprise, and community boundaries. Read what keynote speaker Miel Hostens, Post-Doc assistant at Ghent University, Belgium, has to say about it.
Can you tell someting about your work/reserach focus?
I hold a Post-Doc assistant position at the ambulatory clinic of the Department of Reproduction, Obstetrics and Herd Health at the Faculty of Veterinary Medicine of Ghent University. I focus on the optimization of productive and reproductive performances in small and large dairy herds with an emphasis on nutrition. I am one of the main data analysts for multiple work packages within the EU Framework 7 project GplusE. Furthermore, I am actively involved in the education of Master students in Veterinary Medicine and statistical training of Ph.D. students. Besides that I am involved in post academic and extension services in the area of herd health management in dairy cows.
Which trends and challenges you see for linked data/semantic web?
Since the 1950’s, computers have been used as a management tool in dairy farming. Over subsequent decades, dairy herd management software has evolved consistently and the personal computer has emerged as an important management tool to primarily monitor production, reproduction and health. In the meantime technologies to collect and store data have been evolving at a quicker pace compared to the speed at which new insights in animal science have been discovered. The exponentially increased volume and speed at which data is created in the post-dotcom decade, is commonly referred to as Big Data. One of the drivers of excitement around Big Data has been the expectation that we will be able to discover new insights in order to support decision making in animal health as ultimate goal.
Albeit, integrated information and especially decision making support tools in the field are lacking. It illustrates that while the technology has been widely accepted and published in animal science, the effective implementation and validation is often minimal. Structural file format or database heterogeneity, syntax heterogeneity, implementation heterogeneity and semantic heterogeneity restrict proper use of real-world animal data. The use of ontologies (i.e. controlled vocabularies) is a means to make scientific descriptions more comparable. In dairy cows for example, most ontologies and trait definitions originate from milk recording organizations. At present, 30 to 40 mainly production related traits are commonly recorded in dairy cattle and used in selection schemes in many developed countries. Over the last decades, fertility and health-related phenotypes have been implemented with varying success.
One of the main challenges in animals health is that even elementary concepts often have no canonical form which would be the preferred notation that encapsulates all equivalent forms of the same concept (eg. disease or health event definition). The semantic web could offer a common framework that allows data to be shared and reused across canonical form, application, enterprise, and community boundaries. Multiple technical and business challenges unique to the animal domain can be identified and need to be addressed before the semantic web can grow from a conceptual towards an applied framework in animal health management.
What are your expectations about Semantics 2017 in Amsterdam?
I mainly hope to see how industry and research are interacting and leading towards real use cases of semantics as a bridge between domains.
