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Foundations II: Ontology Engineering Class Session 3

Foundations II: Ontology Engineering Class Session 3. Deborah McGuinness and Joanne Luciano with Peter Fox and Li Ding CSCI-6962-01 September 20, 2010. Review of reading Assignment. Semantic Web for the Working Ontologist (Allemang and Hendler), first few chapters.

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Foundations II: Ontology Engineering Class Session 3

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  1. Foundations II: Ontology EngineeringClass Session 3 Deborah McGuinness and Joanne Luciano with Peter Fox and Li Ding CSCI-6962-01 September 20, 2010

  2. Review of reading Assignment • Semantic Web for the Working Ontologist (Allemang and Hendler), first few chapters. • Rector et al. OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns. • Any comments, questions? • Homework assignment due at 1200h today

  3. Semantic Web Methodology and Technology Development Process Establish and improve a well-defined methodology vision for Semantic Technology based application development Leverage controlled vocabularies, et c. Adopt Technology Approach Leverage Technology Infrastructure Science/Expert Review & Iteration Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Analysis Use Case Develop model/ ontology Small Team, mixed skills 3

  4. Semantic Web Layers 4 http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

  5. Ontology Spectrum An ontology specifies a rich description of the Terminology, concepts, nomenclature Properties explicitly defining concepts Relations among concepts (hierarchical and lattice) Rules distinguishing concepts, refining definitions and relations (constraints, restrictions, regular expressions) relevant to a particular domain or area of interest. www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html slide from Kendall/McGuinness SemTech Tutorial

  6. Ontologies provide a common vocabularyfor use by independently developed resources, processes, services Agreements among organizations sharing common services can be made with regard to their usage; the meaningof relevant concepts can be expressed unambiguously By composing / mappingontologies and mediatingterminology across participating events, resources and services, independently-developed services can work together to share information and processes consistently, accurately, and completely Ontologies also ensure Valid conversations among agents to collect, process, fuse, and exchange information Accurate searching by ensuring context using concept definitions and relations instead of/in addition to statistical relevance of keywords Ontology-based Technologies slide from Kendall/McGuinness SemTech Tutorial 2008

  7. Background Knowledge We need to provide machine understandable encodings of terms that are used in applications Approaches to drive ontology creation Bottom up (using data from databases or scraping) **Mid level (using use cases and knowledge of the subject area) Top down (using foundational or upper level ontologies and building “down”)

  8. Reuse existing knowledge Standards exist in most domains; many of which are overlapping Identify the set that is most relevant to the problem and business issue A component-based approach helps deal with overlapping standards; complex relationships can and must be defined such that term usage and overlap is unambiguous and machine interpretable Brainstorming with domain experts can be useful to start; then refine and iterate to the level required by the application adapted from Kendall/McGuinness SemTech Tutorial 2009

  9. Use Case Example We will look at one example use case and the thought process involved in generating the plan for the ontology encoding from the SESDI project (Semantically-Enabled Scientific Data Integration)

  10. Selected VxyO Motivation: Mt. Spurr, AK. 8/18/1992 eruption, USGS http://www.avo.alaska.edu/image.php?id=319

  11. Eruption cloud movement from Mt.Spurr, AK,1992 USGS

  12. Tropopause http://aerosols.larc.nasa.gov/volcano2.swf

  13. Atmosphere Use Case Determine the statistical signatures of both volcanic and solarforcings on the height of the tropopause From paleoclimate researcher – Caspar Ammann – Climate and Global Dynamics Division of NCAR - CGD/NCAR Layperson perspective: - look for indicators of acid rain in the part of the atmosphere we experience… (look at measurements of sulfur dioxide in relation to sulfuric acid after volcanic eruptions at the boundary of the troposphere and the stratosphere) Nasa funded effort with Fox - NCAR, Sinha - Va. Tech, Raskin – JPL, McGuinness

  14. Gather the Thought Process Ask which questions are being focused on Ask for an answer to the questions Ask how the questions are answered Ask for criteria for a “good” answer

  15. Use Case detail: A volcano erupts Preferentially it’s a tropical mountain (+/- 30 degrees of the equator) with ‘acidic’ magma; more SO2, and it erupts with great intensity so that material and large amounts of gas are injected into the stratosphere. The SO2gasconverts to H2SO4 (Sulfuric Acid) + H2O (75% H2SO4 + 25% H2O). The half life of SO2 is about 30 - 40 days. The sulfuric acidcondensates to little super-cooledliquiddroplets. These are the volcanic aerosol that will linger around for a year or two. Brewer Dobson Circulation of the stratosphere will transportaerosol to higher latitudes. The particles generate great sunsets, most commonly first seen in fall of the respective hemisphere. The sunlight gets partially reflected, some part gets scattered in the forward direction. Result is that the direct solar beam is reduced, yet diffuse skylight increases. The scattering is responsible for the colorful sunsets as more and more of the blue wavelength are scattered away.in mid-latitudes the volcanic aerosol starts to settle, but most efficient removal from the stratosphere is through tropopause folds in the vicinity of the stormtracks. If particles get over the pole, which happens in spring of the respective hemisphere, then they will settle down and fall onto polar icecaps. Its from these icecaps that we recover annual records of sulfateflux or deposit. We get icecores that show continuous deposition information. Nowadays we measure sulfate or SO4(2-). Earlier measurements were indirect, putting an electric current through the ice and measuring the delay. With acids present, the electric flow would be faster. What we are looking for are pulse likeevents with a build up over a few months (mostly in summer, when the vortex is gone), and then a decay of the peak of about 1/e in 12 months. The distribution of these pulses was found to follow an extreme value distribution (Frechet) with a heavy tail.

  16. Use Case detail: … climate So reflection reduces the total amount of energy, forward scattering just changes the beam, path length, but that's it. The dryfogs in the sky (even after thunderstorm) still up there, thus stratosphere not troposphere. The tropical reservoir will keep delivering aerosol for about two years after the eruption. The particles are excellent scatterers in short wavelength. They do absorb in NIR and in IR. Because of absorption, there is a local temperature change in the lower stratosphere. This temperature change will cause some convective motion to further spread the aerosol, and second: Its good factual stuff. Once it warms up, it will generate a temperature gradient. Horizontal temperaturegradients increase the baroclinicity and thus storms, and they speedup the local zonalwinds. This change in zonal wind in high latitudes is particularly large in winter. This increased zonal wind (Westerly) will remove all coldair that tries to buildup over winter in high arctic. Therefore, the temperature anomaly in winter time is actually quite okay. Impact of volcanoes is to cool the surface through scattering of radiation. In winter time over the continents there might be some warming. In the stratosphere, the aerosol warm. The amount of GHG emitted is comparably small to the reservoir in the air. The hydrologic cycle responds to a volcanic eruption.

  17. Stepping back We have identified a number of noun phrases and verbs that will be needed if we are to answer the questions Noun phrases are typically modeled as classes Verbs are typically modeled as properties Constraints are typically modeled as value (and other) restrictions

  18. Starting Points When building a background ontology for an application, we need to decide whether it is best to start from scratch or to reuse other ontologies. Look around for existing resources These can be: Existing ontologies Database schemas Controlled vocabularies Table of contents like material (on a web page, in a book, catalog, etc.

  19. How to find starting points Web searches for content area SWOOGLE Talk to experts Standards bodies (IEEE, OMG, etc.) In this case, SWEET – Semantic Web Earth and Environmental Terminology was a reasonable starting point Why – because it was reasonably well used, it included terminology we needed, it incorporated some standard terminologies we cared about

  20. Atmosphere (portions from SWEET)

  21. Atmosphere II

  22. More on Scoping Focus initially on: Class hierarchy Important relationships (yielding properties and sometimes property hierarchies) Important restrictions (yielding classes to be used as value restrictions) Acknowledge other important issues such as: Required vs. optional (yielding cardinality restrictions) Disjointness Processes

  23. Representing processes 23

  24. Developing ontologies in VSTO Use cases and small team (7-8; 2-3 domain experts, 2 knowledge experts, 1 software engineer, 1 facilitator, 1 scribe) Identify classes and properties (leverage controlled vocab.) Start with narrower terms, generalize when needed or possible Adopt a suitable conceptual decomposition (e.g. SWEET) Import modules when concepts are orthogonal Review, vet, publish Only code them (in RDF or OWL) when needed (CMAP, …) Ontologies: small and modular 24

  25. Use Case example Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the vertical mode during January 2000 as a time series. Plot the neutral temperaturefrom the Millstone-HillFabry Perot, operatingin thevertical modeduringJanuary 2000as atime series. Objects: Neutral temperature is a (temperature is a) parameter Millstone Hill is a (ground-based observatory is a) observatory Fabry-Perot is a interferometer is a optical instrument is a instrument Vertical mode is a instrument operating mode January 2000 is a date-time range Time is a independent variable/ coordinate Time series is a data plot is a data product 25

  26. Class and property example Parameter Has coordinates (independent variables) Observatory Operates instruments Instrument Has operating mode Instrument operating mode Has measured parameters Date-time interval Data product 26

  27. Modeling Advice As we model, we want to think about how we will represent the information. When we clean things up, we will want to follow best practices: Consistent Understandable Extensible Longevity (e.g., prices on wines in wine agent may need to change frequently and may be best in a separate file)

  28. Domain Modeling Next simple domain modeling and evaluation using a simplified example from the domain of wine and foods

  29. General Nature of Descriptions a WINE a LIQUID a POTABLE grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY grape dictates color (modulo skin) harvest time and sugar are related General Categories Structured Components Interconnections Between Parts

  30. General Nature of Descriptions a WINE a LIQUID a POTABLE grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY grape dictates color (modulo skin) harvest time and sugar are related Class Superclass General Categories Number / Card Restrictions Structured Components Roles / Properties Value Restrictions Interconnections Between Parts

  31. Define domain terms and inter-relationships Define concepts in the domain (classes, nouns) Identify subclass/superclass relationships Identify attributes/properties/slots (verbs) Identify any general properties (relations, functions, verbs) Restrict slot values Define individuals Define relationships between individuals (filling in slots) More: http://www.bell-labs.com/project/classic/papers/sowabook.ps.gz Ontology Development slide from Kendall/McGuinness SemTech Tutorial 2009

  32. Classes & Class Hierarchy A class is a concept in the domain Vintage – a wine made from grapes grown in a specified year A class of properties (flavor, body, color, sugar…) A class is a collection of elements with similar properties White wine – wines made from white grapes White table wine – wines made from white grapes that are not appellations or regional (not “quality wine” in the EU) A class contains necessary conditions for membership (specific network broadcast properties, frequency, time & location) Instances of classes Marietta Old Vines Red -> Red Wine Forman Vineyards -> Winery slide from Kendall/McGuinness SemTech Tutorial 2009

  33. Classes are organized into subclass-superclass (or generalization-specialization) hierarchies True subclass relationships are the basis of a formal is-a hierarchy Classes are “is-a” related if an instance of the subclass is an instance of the superclass Classes may be viewed as sets Subclasses of a class are comprised of a subset of the superset Examples RedWine is a subclass of Wine Every red wine is a wine or every instance of a red wine (e.g., Marietta Old Vines Red) is an instance of wine NapaValleyWine is a subclass of CaliforniaWine Every wine from Napa Valley is a wine from California Class Inheritance

  34. Levels in the Class Hierarchy Class inheritance is Transitive A is a subclass of B (white wine, dessert wine are subclasses of wine) B is a subclass of C (viognier is a subclass of white wine, late harvest wine is a subclass of dessert wine) therefore A is a subclass of C (late harvest viognier is a subclass of white wine, dessert wine and wine)

  35. Properties & Slots Slots in a class definition describe attributes of members of a class each wine will have color, sugar content, flavor, body, etc. Types of properties “intrinsic” properties: flavor and color of wine “extrinsic” properties: name and price of wine parts: ingredients in a recipe relations to other objects: producer of wine (winery) Data and object properties simple (datatype) contain primitive values (strings, numbers) complex properties contain other objects (e.g., a winery instance)

  36. Class & Slot Inheritance A subclass inherits all the slots from its super class If a wine has a name and flavor, a red wine also has a name and flavor If a class has multiple super classes, it inherits slots and restrictions from all of them Port is both a dessert wine and a red wine. It inherits “sugar content: sweet” from the dessert wine and “color:red” from red wine slide from Kendall/McGuinness SemTech Tutorial 2009

  37. Property or Slot Constraints Constraints on properties describe or limit the set of possible values A channel adapter in a message bus must be associated with at least one channel A policy applies for exactly one frequency range Slot cardinality – the number of values a slot can or must have Cardinality – cardinality N means that the slot must have exactly N values Minimum cardinality - 1 means that the slot must have a value (required), 0 means that the slot value is optional Maximum cardinality - 1 means that the slot can have at most one value (single-valued slot), N means that the slot can have up to N values (N > 1, multi-valued slot)

  38. Slot Value Constraints Slot value type – defines the set of possible values for the property String: a string of characters (“Château Lafite”) Number: an integer or a float (15, 4.5) Boolean: a true/false flag Enumerated type: a list of allowed values (red, white, rose) Filler: a single value (e.g., the color slot for a RedWine must be filled with the single value “red”) Object type – a class defined in an ontology (e.g., Winery is the value restriction on the hasMaker slot on the class Wine) slide from Kendall/McGuinness SemTech Tutorial 2009

  39. Domain & Range Properties In OWL and many other KR languages, relations (properties, slots) are strictly binary The domain & range represent the source & target arguments, respectively, for the property Domain of a slot – the class (or classes) that may have the slot -Wine is the domain of the slot hasWineColor Range of a slot – the class (or classes) to which slot values belong - everything that fills the hasWineColor slot is an instance of the enumerated class {red, white, rose} Some KR languages that inherently support n-ary relations, such as CL, do not make this distinction More flexible, intuitively more like mathematics, where functions have ranges (or return types) but not all relations are functions Requires additional relations to specify argument order, which can be critical for ontology alignment slide from Kendall/McGuinness SemTech Tutorial 2009

  40. Property Inheritance A subclass inherits all the slots of its superclass(es) A subclass can add constraints to “narrow” the set of allowed values Make the cardinality range smaller Replace a class in the range with a subclass slide from Kendall/McGuinness SemTech Tutorial 2009

  41. Individuals or Instances of Classes An Individual (instance, object in other paradigms) Any class that an individual is a member of, or is an individual of, is a type of the individual Any superclass of a class is an ancestor (or type) of the individual Specify slot values for the individual Slot values should conform to the constraints such as range, value type, cardinality restrictions, etc. slide from Kendall/McGuinness SemTech Tutorial 2009

  42. Vehicle Example: OWL Individuals BASF Dupont Daimler-Chrysler Boeing BMW * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  43. OWL Statements BASF Dupont Daimler-Chrysler Boeing BMW builtBy builtBy a Mini Cooper S a Dakota * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  44. OWL ObjectProperty BASF Dupont Daimler-Chrysler Boeing <owl:ObjectProperty rdf:ID="builtBy"> <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> BMW builtBy builtBy a Mini Cooper S a Dakota VIN * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  45. OWL ObjectProperty range BASF Dupont Daimler-Chrysler Boeing BMW <owl:ObjectProperty rdf:ID="builtBy"> <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> builtBy builtBy a Mini Cooper S a Dakota * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  46. OWL ObjectProperty range BASF Dupont Daimler-Chrysler Boeing BMW <owl:ObjectProperty rdf:ID="builtBy"> <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> builtBy builtBy a Mini Cooper S a Dakota domain * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  47. Inverse Properties domain BASF Dupont Daimler-Chrysler Boeing <owl:ObjectProperty rdf:ID=“hasBuilt"> <rdfs:range rdf:resource="#DurableGood"/> <rdfs:domain rdf:resource="#Enterprise"/> <owl:inverseOf rdf:resource="#builtBy"/> </owl:ObjectProperty> BMW hasBuilt hasBuilt a Mini Cooper S range a Dakota * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  48. Inverse Properties Inverse slots contain redundant information, but Allow acquisition of the information in either direction Enable additional verification Allow presentation of information in both directions The actual implementation may vary from system to system Are both values stored? When are the inverse values filled in? What happens if we change the link to an inverse slot? Repository models often provide support for traversing relationships (domain, domainOf; range, rangeOf), allowing where-used kinds of searches One of the most common uses of owl:inverseFunctionalProperty is to conceptualize relational database keys slide from Kendall/McGuinness SemTech Tutorial 2009

  49. Symmetric Transitive More on Properties hasfriend Deborah Peter hasfriend hasPart RPI TWC hasPart SoScience hasPart * Adapted from Evan Wallace, NIST slide from Kendall/McGuinness SemTech Tutorial 2009

  50. Class Descriptions class identifier enumeration property restriction intersection union complement slide from Kendall/McGuinness SemTech Tutorial 2009

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