W G 14 2 ONTOLOGY Herbert Schentz I ncluding USE CASES from : I ntegrated Assessm ent Models & environm ental observation Thom as Dirnböck 02.10.2003 | Folie 1 02.10.2003 | Folie 2 Overview Definition Part of the definition within Wikipedia � � Definition Definition (http://en.wikipedia.org/wiki/Ontology_%28computer_science%29) � � Examples Examples … a formal logical ontology is specified as consisting of the … a formal logical ontology is specified as consisting of the … a formal logical ontology is specified as consisting of the … a formal logical ontology is specified as consisting of the � � Why Ontologies? Why Ontologies? following logical elements: concepts (classes, objects, or following logical elements: concepts (classes, objects, or following logical elements: concepts (classes, objects, or following logical elements: concepts (classes, objects, or categories) categories) with their characteristics (attributes, slots, functions, categories) with their characteristics (attributes, slots, functions, categories) with their characteristics (attributes, slots, functions, � � Common Ontology for semantic Common Ontology for semantic roles, or properties) and relations (generalization and roles, or properties) and relations (generalization and roles, or properties) Dataintegration Dataintegration specialization, functions) restrained by logical axioms (assertions) specialization, functions) � � Establishing Ontologies Establishing Ontologies and exemplified by instances of classes and specific properties…. � � Invitation for the Kick Off Invitation for the Kick Off 02.10.2003 | Folie 3 02.10.2003 | Folie 4 WG 142 Ontology Wikipedia Overview A sm all cat and m ice ontology leg has 4 (maximum) ?mpf ?! � � Definition Definition mammal (maximalSize) � � Examples Examples has 1 (maximum) tail � � Why Ontologies? Why Ontologies? � � Common Ontology for semantic Common Ontology for semantic is a is a Dataintegration Dataintegration Establishing Ontologies Establishing Ontologies � � cat some hunt mouse Invitation for the Kick Off Invitation for the Kick Off � � 02.10.2003 | Folie 5 02.10.2003 | Folie 6 WG 142 Ontology WG 142 Ontology 1
A partial ontology for soil investigation Overview Administrative Administrative Administrative Administrative unit unit unit unit Site parameter site selection Definition Definition ?mpf ?! Cadmium heavy metal � � Geological region Sampling soil Analysis � � Examples Examples Aha ! parameter sampling chromatographie specification transport � � Why Ontologies? Why Ontologies? transportation parameter wet analysis ion � � Common Ontology for semantic Common Ontology for semantic parameters Phases sorting Dataintegration Dataintegration Codelist & Rules Establishing Ontologies Establishing Ontologies � � ...... pop Invitation for the Kick Off Invitation for the Kick Off Methodes ISO preparation decomposition � � Relations might be: Follows, partOf, produces, parameter isIn, .... 02.10.2003 | Folie 7 02.10.2003 | Folie 8 WG 142 Ontology WG 142 Ontology Form al languages Consequence � There are form al, human and machine readable languages to notify ontologies: You all have produced, worked with, needed, 1. The class m odel of UML (not so well m achine described, read, ... ontologies without knowing readable) that those were ontologies. 2. RDF / RDFs (Resource description File – W3C Standard) So what is so exciting about 3. OWL (Web Ontology language – W3C Standard) ontologies ? We can exchange, share and harmonize We can exchange, share We can exchange, ontologies ! 02.10.2003 | Folie 9 02.10.2003 | Folie 10 WG 142 Ontology WG 142 Ontology w hy Ontologies Overview Knowledge representation: Expanded Thessauri: The usual habitat of an Controlled vocabulary + a lot � � Definition Definition eagle-owl is .... of relations between the terms � � Examples Examples Ontologies: Semantic � � Why Ontologies? Why Ontologies? Unified formal descriptions of web the meaning of the things, we � � Common Ontology for semantic Common Ontology for semantic want to share, exchange, Dataintegration Dataintegration use as inputs of models, ... Establishing Ontologies Establishing Ontologies � � Interface Descriptions: Metadata: Extending the structural Invitation for the Kick Off Invitation for the Kick Off More detailed through the � � description of interfaces by description of relations and descriptions of the contents restrictions 02.10.2003 | Folie 11 02.10.2003 | Folie 12 WG 142 Ontology WG 142 Ontology 2
Com m on Ontologies for sem antic W hy m odels? Dataintegration Distributed Distributed Local Datamining Applications Data � Models combine knowledge from (models) various disciplines in an analytical framework � Assess the socio-econom ic and Workflow environmental consequences of Engine human activites � Derive „If-Then“ futures by Portal im plementing key processes – Scenarios View Data � Inform decision makers about consequences and options A Common Ontology is the key for data sharing and A Common Ontology data integration 02.10.2003 | Folie 13 02.10.2003 | Folie 14 WG 142 Ontology Integrated Assessment Models I ntegrated Assessm ent ( I A) Models and Advantages of I A and needs from Scenarios – som e exam ples environm ental observation I A Models Scenarios � Advantages of IA � Environment-Energy Sector � ECN (1995) Energy scenarios for a � Reveals consequences of and options for policy changing Europe � RAINS (I I ASA SO2, NOx, NH3 Emission � Reveals interactions between different und Effekte) � IPCC multi model approach � CPB (1997) Economy and physical environmental issues and the society environment) (I MAGE, AI M, ASF, MARI A, MESSAGE, MiniCAM, ..) � Rises public awareness of complex and long- Environment-Economy Sector � � OECD (1997) The world in 2020: towards term, global environmental problems a new global age � GTAP (Global Trade Analysis) � WorldScan (e.g. policies for � Needs from environmental observation � IPCC (2000) SRES Emission Scenarios implementing the Kyoto protocol) � Sustainable development � Need for integration of different scales � VISIONS 4. EU Framework Program � WORLD 3 („Limits to growth“ and � Need for integration of data „Beyond the limits“) � Millenium Ecosystem Assessment (1998- � TARGETS (Tool to assess regional and � Need for integration of knowledge 2005) Ecosystem services and human well global environmental and health targets for being sustainability) � Need for evaluation and validation Source: „Cloudy cristal balls“ report EEA 02.10.2003 | Folie 15 02.10.2003 | Folie 16 ECN – Energy Research Centre of the Netherlands Integrated Assessment Models CPB – Dutch Central Planning Bureau I ntegration of data and m odels I ntegration of know ledge � The reliability of integrated assessments is based on the Distributed Distributed Distributed Data mining use of multidisciplinary methods and the involvement of ecological Data Applications With local tools non-scientists Species • Social scientist prediction • Politician model • Lay experts Participatory • NGO methods • etc. Calculation of trajectories Portal Scenarios IA models Groundwater • Mathematician • Stakeholders flow model • System modeller • Politician • Experts from social • Scientists Massflow and natural sciences • Mediator model • etc. • etc. 02.10.2003 | Folie 17 02.10.2003 | Folie 18 Integrated Assessment Models Integrated Assessment Models 3
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