- Ontology Matching in the Biomedical Domain – Challenges, Solutions and Applications
- The Agricultural Semantic Web and its role in Digital Agriculture by Brett Drury
- Querying SIB Swiss Institute of Bioinformatics resources with SPARQL
Ontology Matching in the Biomedical Domain – Challenges, Solutions and Applications
Ontology matching is the process of defining correspondences between two or more related ontologies, which can be used to either map or integrate them. This is critical to ensure data findability and interoperability when datasets are described using different ontologies, a problem that is increasingly more common in the biomedical domain due to the prolific development of ontologies therein.
Biomedical ontologies pose unique challenges to ontology matching due to their distinct profile. In this tutorial, we overview these challenges, the state-of-the-art solutions to address them, the ontology matching tools that implement such solutions, and their performance in independent evaluation. Furthermore, we discuss the role of the user in validating ontology alignments and/or performing interactive matching. Finally, we review current infrastructures, initiatives and applications involving ontology matching.
Organized by: Daniel Faria, Catia Pesquita, Ian Harrow, Thomas Liener, Simon Jupp (TBC) and Ernesto Jimenez-Ruiz
The Agricultural Semantic Web and its role in Digital Agriculture
The world faces a food crisis. The World Bank has stated that agricultural gains will level off by 2050. Faced with increasing population, flat crop yields and an increasing demand for a Western lifestyle the current agricultural methods are not sufficient to meet future food demand. Newer agricultural techniques such as GMOs have faced hostility from the general public, and their development has been stymied. The answer seems to be Digital Agriculture which allows farmers to increase yields by making better decisions. The focus of many digital agriculture related academic papers has been the applications of data gathered from sensors and other agricultural data sources. The semantic web has largely been ignored. This tutorial will argue that the semantic web is a required element of digital agriculture, and will present a wide ranging review of the Agricultural Semantic Web as well as its applications. In addition there will be an opportunity to undertake some practical exercises using the resources described in the tutorial.
Brett is currently a Senior Data Scientist at Skim Technologies located in Porto, Portugal. Previously he was the Head of Research at Scicrop an Agtech based start-up located in Sao Paulo, Brazil. Brett gained his PhD at the University of Porto under the direction of Prof Luis Torgo and he undertook his Post Doctoral Studies at the University of Sao Paulo under the guidance of Prof Alenu Lopes. He was a Research Fellow and Adjunct Lecturer at the National University of Ireland. He was also a FAPESP PIPE grant holder. He is also currently an external member of LIAAD-INESC-Tec , an academic research centre based in Porto .
A survey of semantic web technology for agriculture
Querying SIB Swiss Institute of Bioinformatics resources with SPARQL
The SIB Swiss Institute of Bioinformatics has been publishing data Resource Description Framework (RDF) since 2007, with the UniProt knowledgebase as the first SIB resource to provide it’s data on the semantic web. Since then, more and more SIB resources are modelling their knowledge with RDF and made them queryable and accessible through their own SPARQL endpoints.
In this tutorial, we explain how you can use the data from nine independent SIB resources ( GlyConnect, UniProt, Rhea, OrthoDB, OMA, Bgee, HAMAP, MetaNetX and NeXtProt) to answer interesting biological questions. For each resource we will have an introduction about what kind of data is available, followed by how it is modelled and then how you can query it using SPARQL. Then we will show the strength of SPARQL 1.1 federated queries to show how the connected SIB databases can answer more than any of our databases could independently. Domain knowledge wise it covers proteins, glycans, reactions, orthology, metabolic networks, chemical mapping, and genome/proteome annotations. The session will be led with a quick introductions to SPARQL in general and the SIB Swiss Institute of Bioinformatics.
The entire session will be using the online public endpoints (with a local mini set backup in case of network issues). For the students we expect them to bring their own laptop with a good keyboard and wifi, curiosity and minimal experience with any query language.