Semantic Science: Ontologies, Data and Probabilistic Theories

Poole, David; Smyth, Clinton; Sharma, Rita

doi:10.1007/978-3-540-89765-1_2

Cited by 20 publications

(7 citation statements)

References 15 publications

(15 reference statements)

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“…In other problem domains, experiments in combining an ontological approach with probabilistic methods have been investigated. For example, BN-specific projects have use cases for medical decision support [34], financial fraud detection [5], and instance matching in a geological domain [27]. MLNs have been applied with semantic technologies in problem domains for ontology matching [25] and for natural language processing, where ontological concepts are extracted from text [12,28].…”

Section: Related Workmentioning

confidence: 99%

Combining ontological modelling and probabilistic reasoning for network management

Apajalahti

Hyvönen

Niiranen

et al. 2017

AIS

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Advanced automation is needed in future mobile networks to provide adequate service quality economically and with high reliability. In this paper, a system is presented that takes into account the network context, analyses uncertain information, and infers network configurations by means of probabilistic reasoning. The system introduced in this paper is an experimental platform integrating a mobile network simulator, a Markov Logic Network (MLN) model, and an OWL 2 ontology into a runtime environment that can be monitored via a Resource Description Framework (RDF)-based user interface. In this approach, the OWL ontology contains a semantic representation of the relevant concepts, and the MLN model evaluates elements of uncertain information. Experiments based on a prototype implementation demonstrate the value of semantic modelling and probabilistic reasoning in network status characterization, optimization, and visualization.

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Section: Related Workmentioning

confidence: 99%

Combining ontological modelling and probabilistic reasoning for network management

Apajalahti

Hyvönen

Niiranen

et al. 2017

AIS

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“…We use to term semantic science (Poole et al, 2008) in an anaolgous way to the semantic web, because the computer should understand the hypotheses and data which form the foundation of science. Science is used as the basis for trust; we trust scientific conclusions because they are based on the evidence available.…”

Section: Introductionmentioning

confidence: 99%

Foundations of model construction in feature-based semantic science

Poole

2012

Journal of Logic and Computation

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The aim of what semantic science is to have scientific ontologies, data, and hypotheses represented and published in machine understandable forms that enable predictions on new cases. There is much work on developing scientific ontologies and representing scientific data in terms of these ontologies. The next step is to publish hypotheses that can make (probabilistic) predictions on the published data and can be used for prediction on new cases. The published data can be used to evaluate hypotheses. To make a prediction in a particular case, hypotheses are combined to form models. This paper considers feature-based semantic science where the data and new cases are described in terms of features. A prediction for a new case is made by building a model made up of hypotheses that fit together, are consistent with the ontologies used, and are adequate for the case. We give some desiderata for such models, and show how the construction of such models is a form of abduction. We provide a definition for models that satisfies these criteria and prove that it produces a coherent probability distribution over the values of interest.

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“…We are interested in decision making and probabilistic reasoning in complex scientific domains [Poole et al, 2008] in which both scientific theories (or hypotheses) and data pertinent to them are available in computer-readable form. We want to make probabilistic predictions in these domains [Schumm, 1991;Jaynes, 2003;Howson and Urbach, 2006] and incorporate rich ontologies [Fox et al, 2006] to allow for semantic interoperability between the theories and the data about which they make predictions.…”

Section: Introductionmentioning

confidence: 99%

“…The former we call ontologies, and the latter theories. See [Poole et al, 2008] for more discussion of these issues.…”

Section: Introductionmentioning

confidence: 99%

A framework for ontologically-grounded probabilistic matching

Sharma¹,

Poole

Smyth³

2010

International Journal of Approximate Reasoning

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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.

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Semantic Science: Ontologies, Data and Probabilistic Theories

Cited by 20 publications

References 15 publications

Combining ontological modelling and probabilistic reasoning for network management

Combining ontological modelling and probabilistic reasoning for network management

Foundations of model construction in feature-based semantic science

A framework for ontologically-grounded probabilistic matching

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