An Overview: Ontology Concepts, Tools, Sample CEOP Ontology And Perspectives Rama Suresh Email: firstname.lastname@example.org NASA/MTECH CEOS WGISS Joint Subgroup Meeting Tromso Norway, May 11, 2004
Outline Background Ontology Concepts Ontology tools and Protégé CEOP background and Sample Ontology Perspectives EO Projects
Background • Current Web is a powerful means for collaboration between people, broadcasting and publishing information worldwide • The next generation web will extend collaborations between to computers... • Machines become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers… • When it arrives, the day-to-day mechanisms of trade, bureaucracy, and our daily lives will be handled by machines talking to machines, leaving humans to provide the inspiration and intuition… • The intelligent "agents" people have touted for ages will finally materialize… • This machine-understandable Web will come about through the implementation of a series of technical advancements and social agreements that are now beginning… Weaving the Web, Tim Berners-Lee, with Mark Fischetti. Harper San Francisco, October 1999
Formula for computer “conversation” Meaning = Ontology + representation + constraints Conclusions= Inference engine(new knowledge, experience, context) Must find low priced 3star hotel in Tromso, accurate 3 day Weather forecast for Tromso from May 10-12. Rama Bot
Discovering the new semantic worlds • Future – ubiquitous, machine-to-machine collaboration • Today – increasing consistency of metadata management for some localized uses cite
What Is An Ontology • An ontology is an explicit description of a domain: • Concepts • properties and attributes of concepts • constraints on properties and attributes • Individuals (often, but not always) • An ontology defines • a common vocabulary • a shared understanding
Why Develop an Ontology? • To share common understanding of the structure of information • among people • among software agents • To enable reuse of domain knowledge • to avoid “re-inventing the wheel” • to introduce standards to allow interoperability
Building Ontology • Acquire domain knowledge • - Assemble appropriate information resources and expertise in the domain of interest • - These definitions must be collected so that they can be expressed in a common language selected for the ontology • 2. Organize the ontology • - Design the overall conceptual structure of the domain. • - Identify the domain's principal concrete concepts and their properties, and their relationships among the concepts • 3. Flesh out the ontology • - Add concepts, relations, and individuals to the level of detail necessary to satisfy the purposes of the ontology.
Building Ontology 4. Check your work - Reconcile syntactic, logical, and semantic inconsistencies among the ontology elements. - Consistency checking may also involve automatic classification that defines new concepts based on individual properties and class relationships. 5. Commit the ontology - Incumbent on any ontology development effort is a final verification of the ontology by domain experts - Subsequent commitment of the ontology by publishing it within its intended deployment environment.
Ontology Issues • Ontology building today is a fragmented practice. • Proliferation of logic languages • Information models that have combined to yield even more ontology forms and editing environments • These tools and methodologies, along with the ontologies built with them, generally exist without proven interoperability • Challenges for establishing methods to integrate ontology components with enterprise information systems and standards
Ontology Tools Survey Software tools are available to accomplish most aspects of ontology development. While ontology editors are useful during each step outlined above, other types of ontology building tools are also needed along the way. More than 50 tools have been identified for building and integrating ontologies: Commercial, public domain and Academic projects http://xml.com/2002/11/06/Ontology_Editor_Survey.html Protégé is one of the tools described in the survey.
Protégé-2000 • An extensible and customizable toolset for constructing knowledge bases (KBs) and for developing applications that use these KBs • Outstanding features • Automatic generation of graphical-user interfaces, based on user-defined models, for acquiring domain instances • Extensible knowledge model and architecture • Scalability to very large knowledge bases
Protégé-2000 • Java based graphical ontology-development tool • Supports a rich knowledge model • Open-source and freely available • Large user base • Easy to use • Some other available tools: • Ontolingua and Chimaera • OntoEdit • OilEd • OWL plug in
determine scope consider reuse enumerate terms define classes define properties define constraints create instances determine scope consider reuse enumerate terms consider reuse define classes enumerate terms define classes define properties define classes define properties define constraints create instances define classes create instances consider reuse define properties define constraints create instances Protégé system development methodology Protégé-2000 support In reality - an iterative process:
GUI Components • Tabs partition different work areas • Classes tab for defining and editing classes • Forms tab for custom-tailoring GUI forms for defining and editing instances • Instances tab for defining and editing instances • Classes & Instances tab for working with both classes and instances • Widgets for creating, editing, and viewing values of a slot (or a group of slots) • Text-field or text-area widget for a slot with string value type • Diagram widget for set of slots defining a graph • Slot widgets check facet constraint violations (red rectangles) • Buttons and menus for performing operations
Classes, slots, facets and instance are all frames
Protégé Information • Protégé web site: http://protege.stanford.edu • Documentation • User’s Guide • Tutorial • protege-discussion mailing list • Ontology library • Contributed ontologies and plugins
A Sample Ontology for CEOP In Situ Data • Why CEOP In Situ data? • Global level significant involvement and commitment • In situ data is relatively simple • What will it do? • Will improve resource discovery and create an open interface based on standards that will include large number of users. • What are the next steps? • Build an ontology for CEOP involving CEOP and CEOS community that could potentially lead to a EO semantic web
CEOP • The Coordinated Enhanced Observing Period (CEOP)was originally envisioned as a major step towards bringing together the research activities in the GEWEX Hydrometeorology Panel (GHP) and is being developed and implemented within the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research Program (WCRP) • CEOP Data sets • Satellite Data • TERRA, AQUA, ENVISAT and ADEOS-II), in addition to TRMM, LANDSAT, NOAA-K series and other operational satellites • Observation Data (In Situ Data) • Model Output Data • http://www.ceop.net/
CEOP In Situ Data • Primary and ancillary data sets • Each Reference site has four data sets for each station that is a part of the Reference Site • Surface Meteorological and Radiation Data set • Flux Data Set • Soil Temperature and Soil Moisture Data Set • Meteorological Tower Data Set • Source JAXA Metadata paper – Ben Burford
What can we do with it? We can build autonomous agents or software to fetch information Bring sites collecting data for certain period of time Bring data sets with values for temperature ranging from degree Celsius to….. Bring sites that have data parameters…….
Perspectives CEOS 17th Plenary Recommendation “CEOS space agency members need to develop information systems with more integratedcatalog, search, ordering and retrieval mechanism. It is recommended that the CEOS WGISS study how to develop this incrementally for particular application fields- building on experience gained in developing such capabilities with CEOP – and to report on its findings to the 2004 Plenary” Building an Ontology for CEOP and Earth observation data is one step in this direction. This could potentially lead to EO Semantic web that will help Users in faster and efficient resource discovery Effective use of these technologies could potentially lead to an integrated catalog, search, ordering and retrieval mechanism
Other Earth Science Ontology Projects SWEET – NASA JPL http://sweet.jpl.nasa.gov/ GEON – University of San Diego www.geongrid.org UK Met Office GCMD, ECHO and ESML