In all scientific disciplines there are multiple competing and complementary theories that have been, and are being, developed. There are also observational data about which the theories can potentially make predictions. To enable semantic inter-operation between the data and the theories, we need ontologies to define the vocabulary used in them. For example, in the domain of minerals exploration, research geologists spend careers developing models of where to find particular minerals. Similarly, geological surveys publish geological descriptions of their jurisdictions as well as instances of mineral occurrences. The community is starting to develop standardized ontologies to enable consistent use of vocabulary and the semantic inter-operation between the model descriptions and the instance descriptions. This paper describes a framework for representing instances and theories using these ontologies, and describes ontologically-mediated probabilistic matching between instances and theories. We give an example of our matcher in the geology domain, where the problem is to determine what minerals can be expected at a location, or which locations may be expected to contain particular minerals. This is challenging as models and instances are built asynchronously, and they are described in terms of individuals and properties at varied levels of abstraction and detail. This paper shows, given a model, an instance, and a role assignment that specifies which individuals correspond to each other, how to construct a Bayesian network that can compute the probability that the instance matches the model.
Abstract. This paper is part of a project to match real-world descriptions of instances of objects to models of objects. We use a rich ontology to describe instances and models at multiple levels of detail and multiple levels of abstraction. The models are described using qualitative probabilities. This paper is about the problem of type uncertainty; what if we have a qualitative distribution over the types. For example allowing a model to specify that a meeting is always scheduled in a building, usually in a civic building, and never a shopping mall can help an agent find a meeting even if it is unsure about the address.
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.
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