Transcript ppt - GEON
NSF Cyberinfrastructure Workshop Metadata, semantic information and ontologies Lead: Danielle Forsyth Respondents: Jim Bonner Bertram Ludaescher Complex System Modeling and Verification requires vast amounts of Data… A Discovery Model Data What we observe Information What we derive Knowledge How we explain what we observe and derive Wisdom When we ‘become one with’ the processes we collect data about Does It Really Work That Way? Does Wisdom Follow Data ? Data Examination Hypothesis Does Data Confirm Wisdom ? Hypothesis Experiment Data Is it a Circular Process ? Examination Hypothesis Data Experiment Data = Wisdom ? Data and Information demand Attention A Wealth of Data Demands a Wealth of Attention A Wealth of Attention Devoted to Data results in a Dearth of Attention Devoted to Wisdom Observed Data Observed Reality Derived Data Predicted Reality Model Observed Data Observed Reality difference Derived Data Model Predicted Reality Observed Data Observed Reality difference Derived Data Physical Model SocioEconomic Model Predicted Reality difference Policy Enablers Desired Reality Ontologies and Metadata Support a Data Search Metaphor • Collect metadata with, but not necessarily part of, the data. • Grow the metadata as data is used. • Index the metadata description for search. • Use a rich metadata description language to support inferencing and data mining. • Keep the data where it makes the most sense for collection and processing – distribute the search. Ontologies • Ontologies are dictionaries of categories and properties – Dictionaries (namespaces) as policy and organization – Categories (classes) as conceptual buckets – Properties as descriptive elements • Data Properties – serial number, weight, length … • Relationship Properties – entered by, derived from … • Ontologies have both a qualitative (my mud index = your turbidity scale) and quantitative components – Standards driven by web based text applications and transaction systems do not necessarily meet scientific needs. Approaches to Ontologies • Published dictionaries – Fixed, periodically updated – Flexible and evolving • • • • My copy of the published dictionary Shared copy of the dictionary My own dictionary My community’s dictionary Approaches to Ontologies • Product • Processing • Community – ie. SWEET • Top Down • Data/Problem Driven • Application • Problem Design or build with Search in mind .. Requirements • Industry standard, machine readable and semantically rich descriptions that support: – – – – machine based inferencing and reasoning community and researcher based knowledge building/sharing knowledge mapping and re-use an approach that allows for context appropriate and policy based access to data and knowledge – Access to data and knowledge by broader communities within government, industry and policy • Allow community to leverage broader industry efforts – Problem/process centric approach