The 3rd CSIRO Workshop on ‘Semantic Machine Learning and Linked Open Data (SML2OD2014)  Applications was held in conjunction with the 4th International Conference on Big Data  and Cloud Computing (BD Could 2014)   3-5  December,  2014, in Sydney, Australia.


Historical and spatial big data from the environmental and agricultural domains already exist in the modern technology-driven world. Government agencies, utilities and research bodies already have large amounts of data, but their value is not being fully realised because they are not integrated and consequently big knowledge is difficult to access. Semantic web, semantic machine learning and linked open data technology may help to build an “outer knowledge layer” so that this information could be accessed by domain people and the broader community. It can also be used to answer complex dynamic queries at run time from the system point of view.

The main goal of this workshop is to identify the key challenges which are faced by the agriculture, viticulture and aquaculture communities, discuss potential solutions and identify the opportunities emerging from cross-domain interactions among agriculture experts, hydrologists, dairy experts, aquaculture experts and ICT experts. Therefore, we expect to gain from the domain experts an explanation of how they can apply semantic web standards, machine learning techniques, and linked data standards into their scientific research.

There will be an open call for research papers presenting various aspects of real world applications of the linked open data concept, machine learning and semantic web technologies applied to the agricultural domains. Selected research papers, demos and late breaking results will be accepted for the workshop. There will be a keynote speaker who will explain the current trends of research in these domains. Our key areas of interest for the purpose of knowledge management are agriculture, aquaculture, environment and dairy production. This also extends to strategies for soil moisture and environmental sensor data integration, dynamic big data annotation, environmental ontology and plant disease knowledge bank.


  • To bring scientists, industry and domain experts to assess how academic advances are addressing real-world requirements. The workshop will strive to improve academic awareness of industrial and final user needs, and therefore direct research towards those needs. Simultaneously, the workshop will serve to inform industry and user representatives about existing research efforts that may meet their requirements. The workshop will also investigate the evolution of environmental linked data.
  • To examine big data integration and big knowledge management in the context of the environment.
  • To explore new research and development horizons for semantic machine learning applications


More specifically, the topics of interest include but are not limited to:

  • Performance evaluation of sensor data using linked data principles;
  • Environmental data integration;
  • Smart farm and its application in linked data;
  • Environmental ontology;
  • Semantic web and big data  management;
  • Agricultural multilingual taxonomies and glossaries;
  • Semantic based decision support system;
  • Environmental big data and knowledge management;
  • Applications for research that build on top of linked data;
  • Environmental analytics;
  • Agricultural informatics;
  • Semantic machine learning applications;
  • Semantic based precision agriculture.
  • Linked data analysis;