Abstract. This chapter overviews work on semantic science. The idea is that, using rich ontologies, both observational data and theories that make (probabilistic) predictions on data are published for the purposes of improving or comparing the theories, and for making predictions in new cases. This paper concentrates on issues and progress in having machine accessible scientific theories that can be used in this way. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geohazards), and sketches the formal foundations that underlie this vision. The aim is to get to the stage where: any new scientific theory can be tested on all available data; any new data can be used to evaluate all existing theories that make predictions on that data; and when someone has a new case they can use the best theories that make predictions on that case. 1

There has been much recent progress in building ontologies and publishing scientific data based on these ontologies. This paper overviews issues and progress in the other half of semantic science: having machine accessible scientific theories that can make predictions on this data and can be used for new cases. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geo-hazards), and sketches the formal foundations that underlie this vision.

Video data is being collected at alarming rates and yet there exists no comprehensive forensic toolset that enables the analyst to quickly examine video in the context of the massive collections. This research builds a System that studies video at a semantic level by means of a joint solution to semantic entity extraction, entity-entity relationship extraction, and dynamic event recognition. The working of the System is grounded in formal ontology. This ontology is jointly induced from the data and established by the human domain experts (i.e., interactive machine learning). Specifically, we implement a Multi Entity Bayesian Network (a form of a probabilistic ontology); we test our System on two-on-two basketball game videos, and our results demonstrate state of the art detection rates on activities like passing the ball, and shooting, consequently promising