1 / 10

SPATIAL RELATIVE TIME MINERALS STRUCTURE METHODS

SPATIAL RELATIVE TIME MINERALS STRUCTURE METHODS. A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012. Mineral, Methods Spatial. A.K.Sinha Introduction to Semantics Workshop, Ar lington , April 30-May 1, 2012. Objects Measurements Minerals Process …WHY

lee
Download Presentation

SPATIAL RELATIVE TIME MINERALS STRUCTURE METHODS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SPATIAL RELATIVE TIME MINERALS STRUCTURE METHODS A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  2. Mineral, Methods Spatial A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  3. Objects Measurements Minerals Process…WHY Service…thermo- dynamic comput- ational tools A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  4. Data should be easily accessible, for instance; if there are too many, it can be difficult to maintain access to them. Data should be organized in such a way that a scientist working on a particular problem can pluck the data of interest from a larger body of information, much of it not relevant to the task at hand; the more data there are, the harder it is to organize them. Data should be arranged so that the relationships among them are simple to understand and so that one can readily see how individual details fit into a larger picture; this becomes more demanding as the amount and variety of data grow. Data should be framed in a common language so that there is a minimum of confusion among scientists who deal with them From Bioinformatics: Converting Data to Knowledge , NAS A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  5. Geoscience Interoperability Goal: from data to knowledge • Challenges • Heterogeneity associated with global data sets • In a web rich world (MILLIONS OF WEB SITES) unlikely to succeed in enforcing a common structured format • Data needed to solve societal challenges are likely to come from other disciplines as well • Need to think beyond discovery (well funded) to integration (poorly understood and poorly funded) • Solution • use strong semantics; likely to have consensus on meaning of data • data to knowledge advanced by reasoning and inference capabilities • develop a geoscience-centric semantic framework that emphasizes reuse of available ontologies • develop web based, semantically enabled query, search and integration technologies A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  6. Sharing Geoscience Knowledge in a semantic world: Building three classes of ontology frameworks PROCESS ONTOLOGY • Objects represent our understanding of the state of the system when the data were acquired, while processes capture the physical and chemical forcings on objects that may lead to changes in state and condition over time. Service provides tools (e.g., simulation models and analysis algorithms) to assess multiple hypotheses, including inference or prediction. • These three classes of ontologies within the semantic layer of the scientific cyberinfrastructure are thus required to enable automated discovery, analysis, utilization, and understanding of data through both induction and deduction Knowledge SERVICE ONTOLOGY OBJECT ONTOLOGY A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  7. AuScope Modelling Framework from Wyborn, 2007 MODIFIED TO EMPHASIZE THE NEED FOR SERVICE ONTOLOGY Natural Hazards Resources & Mining SustainableEnergy/Water Geodynamics SocietalNeed Storm Surge Mine Waste Disposal Predictive ore deposition Mine design Earthquake Mantle Convection Slab subduction Tsunami ModellingServices Inundation Reactive Transport Coupled Mechanics & Reactive Transport ESys Crustal Geodynamics Modelling code Underworld Modelling Codes Interpolation Mesh Chemistry (Gibbs) ReactionKinetics Finite Element Solver(Finley) MantleConvectionCode Finite Element Solver Such codes with specific services in mind are amenable to registration through semantically organized service ontology A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012 Base Scientific Concepts ChemicalReactions Rock Mechanics Fluiddynamics Coupled Processes Deformation Surface Processes FluidFlow

  8. Developing inferencing capabilities : making sense of complex data by citizens and policy makers The conceptual relationships utilized in the above example are based on: 1. Ignimbrite is apyroclastic rock   is a       volcanic rock     is a       rock 2. Hazardous eruption    is a       Explosive eruption          is a       eruption 3. Explosive eruption      has Materialpyroclastic rocks Therefore, ignimbrites are a product of hazardous volcanic eruptions A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012 Sinha et al., 2010

  9. In preparation For GSA Special Paper, 2012, Sinha, Thessen,Lin, Barnes and Richards, Informatics and Science: Towards an integrative View of the earth A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

  10. In preparation For GSA Special Paper, 2012, Sinha, Thessen, Lin, Barnes and Richards, Informatics and Science: Towards an integrative View of the earth A.K.Sinha Introduction to Semantics Workshop, Arlington , April 30-May 1, 2012

More Related