1 Foundations I: Methodologies, Knowledge Representation Deborah McGuinness and Peter Fox CSCI/ITEC Week 2, September 14, 2009

Key Points Regarding Flu Epidemics Wash your hands often, especially after shaking hands with others (use hand disinfectants if there is no access to soap and water). Avoid close contact with people who are sick Cover your mouth and nose with a tissue when coughing or sneezing. If you don't have a tissue, use the inside of your elbow. Do not touch your eyes, nose, or mouth, especially after contact with shared keyboards, microscopes, instruments, or other people. STAY HOME-If you have flu-like symptoms (i.e., fever (100 degrees F [37.8 decrees C] or higher, cough, sore throat, runny or stuffy nose, body aches, headache, chills, fatigue). For more information

Review of reading Assignment 1 Ontologies 101, Semantic Web, e-Science, RDFS, Common Logic, OWL guide Any comments, questions? 3

Contents Review of methodologies Elements of KR in semantic web context And in e-Science Choices of representation, models Examples of KR Encoding and understanding representations Assignment 1 4

5 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. Use Case Small Team, mixed skills Analysis Adopt Technology Approach Leverage Technology Infrastructure Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Science/Expert Review & Iteration Develop model/ ontology Evaluation

KR and methodologies Procedural Knowledge: Knowledge is encoded in functions/procedures. This can be viewed as hard coded and less flexible. ex: function Person(X) return boolean is if (X = ``Socrates'') or (X = ``Hillary'') then return true else return false; Or function Mortal(X) return boolean is return person(X); Networks: A compromise between declarative and procedural schemes. Knowledge is represented in a labeled, directed graph whose nodes represent concepts and entities, while its arcs represent relationships between these entities and concepts. Frames: Much like a semantic network except each node represents prototypical concepts and/or situations. Each node has several property slots whose values may be specified or inherited. Logic: A way of declaratively representing knowledge. For example: –person(Socrates). –person(Hillary). –forall X [person(X) ---> mortal(X)] –DL, FOL, HOL 6

KR and methodologies Decision Trees: Concepts are organized in the form of a tree. Statistical Knowledge: The use of certainty factors, Bayesian Networks, Dempster-Shafer Theory, Fuzzy Logics,..., etc. Rules: The use of Production Systems to encode condition- action rules (as in expert systems). Parallel Distributed processing: The use of connectionist models. Subsumption Architectures: Behaviors are encoded (represented) using layers of simple (numeric) finite-state machine elements. Hybrid Schemes: Any representation formalism employing a combination of KR schemes. 7

Remember, in science! Some of the knowledge is lost when it is placed into any particular representation structure, or may not be reusable (e.g. Frames) So, you may ask something that cannot be answered or inferred Knowledge evolves, i.e. changes Knowledge and understanding is very often context dependent (and discipline, language, and skill-level dependent, and …) 8

And, if you are used to logic You are working mostly within the world of logic, whereas we are trying to represent knowledge with logic and we are usually dealing with tangible objects, such as trees, clouds, rock, storms, etc. Because of this, we have to be very careful when translating real things into logical symbols - this can, surprisingly, be a difficult challenge. Consider your method of representation (yes, we do want to compute with it) 9

Thus A person who wants to encode knowledge needs to decouple the ambiguities of interpretation from the mathematical certainty of (any form of) logic. The nature of interpretation is critical in formal knowledge representation and is carefully formalized by KR scientists in order to guarantee that no ambiguity exists in the logical structure of the represented knowledge. 10

Representing Knowledge With Objects Take all individuals that we need to keep track of and place them into different buckets based on how similar they are to each other. Each bucket is given a descriptive based on what objects it contains. Since the individuals in a given bucket are at least somewhat similar, we can avoid needing to describe every inconsequential detail about each individual. Instead, properties that are common to all individuals in a bucket can just be assigned to the entire bucket at once. Properties are typically either primitive values (such as numbers or text strings) or may be references to other buckets. 11

Representing Knowledge With Objects Some buckets will be more similar to each other than others and we can arrange the buckets into a hierarchy based on the similarity. If all buckets in a branch in the tree of buckets share a property, the information can be further simplified by assigning the property only to the parent bucket. Other buckets (and individuals) are said to inherit that property. Buckets may have different names: e.g. Classes, Frames, or Nodes BUT, once we move to (e.g.) DL, not all object rules apply, e.g. cannot override properties Multiple inheritance is not always obvious to people 12

Re-enter Semantic Web At its core, the Semantic Web can be thought of as a methodology for linking up pieces of structured and unstructured information into commonly-shared description logics ontologies. 13

14 Semantic Web Layers

15 Elements of KR in Semantic Web Declarative Knowledge Statements as triples: {subject-predicate-object} interferometer is-a optical instrument Fabry-Perot is-a interferometer Optical instrument has focal length Optical instrument is-a instrument Instrument has instrument operating mode Instrument has measured parameter Instrument operating mode has measured parameter NeutralTemperature is-a temperature Temperature is-a parameter A query: select all optical instruments which have operating mode vertical An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature

16 Ontology Spectrum Catalog/ ID Selected Logical Constraints (disjointness, inverse, …) Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. General Logical constraints Originally from AAAI Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in:

17 OWL or RDF or OWL 2 RL? In representing knowledge you will need to balance expressivity with implementability –OWL (Lite, DL, Full) 1 or 2? –RDF and RDFS –Rules, e.g. SWRL or OWL 2 RL You will need to consider the sources of your knowledge You will need to consider what you want to do with the represented knowledge

18 The knowledge base Using, Re-using, Re-purposing, Extending, Subsetting Approach: –Bottom-up (instance level or vocabularies) –Top-down (upper-level or foundational) –Mid-level (use case) Coding and testing (understanding) Using tools (some this class, more over the next two classes) Iterating (later) Maintaining and evolving (curation, preservation) (later)

19 ‘Collecting’ the ‘data’ Part of the (meta)data information is present in tools... but thrown away at output e.g., a business chart can be generated by a tool: it ‘knows’ the structure, the classification, etc. of the chart,but, usually, this information is lost storing it in web data would be easy! Semantic Web-aware tools are around (even if you do not know it...), though more would be good: –Photoshop CS stores metadata in RDF in, say, jpg files (using XMP) –RSS 1.0 feeds are generated by (almost) all blogging systems (a huge amount of RDF data!) Scraping - different tools, services, etc, come around every day: –get RDF data associated with images, for example: service to get RDF from flickr images –service to get RDF from XMP –XSLT scripts to retrieve microformat data from XHTML files –RSS scraping in use in Virtual Observatory projects in Japan –scripts to convert spreadsheets to RDF SQL - A huge amount of data in Relational Databases –Although tools exist, it is not feasible to convert that data into RDF –Instead: SQL ⇋ RDF ‘bridges’ are being developed: a query to RDF data is transformed into SQL on-the-fly

20 More Collecting RDFa (formerly known as RDF/A) extends XHTML by: –extending the link and meta to include child elements –add metadata to any elements (a bit like the class in microformats, but via dedicated properties) It is very similar to microformats, but with more rigor: –it is a general framework (instead of an ﾒ agreement ﾓ on the meaning of, say, a class attribute value) –terminologies can be mixed more easily GRDDL - Gleaning Resource Descriptions from Dialects of Languages ATOM - XML-based Web content and metadata syndication format (used with RSS)

21 GRDDL - bottom up GRDDL - Gleaning Resource Descriptions from Dialects of Languages Pretty much = “XML/XHTML (for e.g.) into RDF via XSLT” Good support, e.g. Jena Handles microformats Active community How to categorize, use, re-use (parts of)?

22 Foundational Ontologies Domain independent concepts and relations physical object, process, event,…, participates,…  (Usually) Rigorously defined formal logic, philosophical principles, highly structured  Examples DOLCE – Descriptive Onotology for Linguistic and Cognitive Engineering SUMO – Suggested Upper Merged Ontology CYC Upper Level Ontology BFO – Basic Formal Ontology GFO – General Formal Ontology (developed by Onto Med)

23 Foundational Ontologies PURPOSE: help integrate domain ontologies Geophysics ontology Marine ontology Water ontology Planetary ontology Geology ontology Struc ontology Rock ontology “…and then there was one…” Foundational ontology Courtesy: Boyan Brodaric

24 Foundational Ontologies PURPOSE: help organize domain ontologies “…a place for everything, and everything in its place…” Foundational ontology shalerock formationlithification Courtesy: Boyan Brodaric

25 Problem scenario  Little work done on linking foundational ontologies with geoscience ontologies  Such linkage might benefit various scenarios requiring cross-disciplinary knowledge, e.g.: water budgets: groundwater (geology) and surface water (hydro) hazards risk: hazard potential (geology, geophysics) and items at threat (infrastructure, people, environment, economic) health: toxic substances (geochemistry) and people, wildlife many others… Courtesy: Boyan Brodaric

26 DOLCE

27 Physical Object SelfConnectedObject ContinuousObject CorpuscularObject Collection Process Abstract SetClass Relation Proposition Quantity Number PhysicalQuantity Attribute SUMO - Standard Upper Merged Ontology

28 ts/IFOMIS%20Report%2005_2003.pdfhttp:// ts/IFOMIS%20Report%2005_2003.pdf

29

30 Using SNAP/ SPAN

31 DOLCE + SWEET DOLCE= SWEET< SWEET Physical-body BodyofGround, BodyofWater,… Material-Artifact Infrastructure, Dam, Product,… Physical-Object LivingThing, MarineAnimal Amount-of-Matter Substance Activity HumanActivity Physical-Phenomenon Phenomena Process State StateOfMatter Quality Quantity, Moisture,… Physical-Region Basalt,… Temporal-Region Ordovician,…  Benefits full coverage rich relations home for orphans single superclasses  Issues individuals (e.g. Planet Earth) roles (contaminant) features (SeaFloor) Courtesy: Boyan Brodaric

32 Conclusions  Surprisingly good fit amongst ontologies so far: no show-stopper conflicts, a few difficult conflicts  DOLCE richness benefits geoscience ontologies good conceptual foundation helps clear some existing problems  Unresolved issues in modeling science entities modeling classifications, interpretations, theories, models,… Courtesy: Boyan Brodaric  Same procedure with GeoSciML

33

34 SWEET 2.0 Modular Design Math, Time, Space Basic Science Geoscience Processes Geophysical Phenomena Applications importation Supports easy extension by domain specialists Organized by subject (theoretical to applied) Reorganization of classes, but no significant changes to content Importation is unidirectional

35 SWEET 2.0 Ontologies

36 Using SWEET Plug-in (import) domain detailed modules Lots of classes, few relations (properties) Version 2.0 is re-usable and extensible

37 Mix-n-Match The hybrid example: –Collect a lot of different ontologies representing different terms, levels of concepts, etc. into a base form: RDF

38 CF attributes SWEET Ontologies (OWL) Search Terms CF Standard Names (RDF object) IRIDL Terms NC basic attributes IRIDL attributes/objects SWEET as Terms CF Standard Names As Terms Gazetteer Terms CF data objects Location Blumenthal

39 Data Servers Ontologies MMI JPL Standards Organizations Start Point RDF Crawler RDFS Semantics Owl Semantics SWRL Rules SeRQL CONSTRUCT Search Queries Location Canonicalizer Time Canonicalizer Sesame Search Interface bibliography IRI RDF Architecture Blumenthal

40 Mid-Level: Developing ontologies 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

41 Use Case example Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the non-vertical mode during January 2000 as a time 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 –Non-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

42 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

43

44

45

46 Higher level use case Find data which represents the state of the neutral atmosphere above 100km, toward the arctic circle at any time of high geomagnetic activity

47 Extending the KR for a purpose Input Physical properties: State of neutral atmosphere Spatial: Above 100km Toward arctic circle (above 45N) Conditions: High geomagnetic activity Action: Return Data Specification needed for query to CEDARWEB Instrument Parameter(s) Operating Mode Observatory Date/time Return-type: data GeoMagneticActivity has ProxyRepresentation GeophysicalIndex is a ProxyRepresentation (in Realm of Neutral Atmosphere) Kp is a GeophysicalIndex hasTemporalDomain: “daily” hasHighThreshold: xsd_number = 8 Date/time when KP => 8

48 Translating the Use-Case - ctd. Input Physical properties: State of neutral atmosphere Spatial: Above 100km Toward arctic circle (above 45N) Conditions: High geomagnetic activity Action: Return Data Specification needed for query to CEDARWEB Instrument Parameter(s) Operating Mode Observatory Date/time Return-type: data NeutralAtmosphere is a subRealm of TerrestrialAtmosphere hasPhysicalProperties: NeutralTemperature, Neutral Wind, etc. hasSpatialDomain: [0,360],[0,180],[100,150] hasTemporalDomain: NeutralTemperature is a Temperature (which) is a Parameter FabryPerotInterferometer is a Interferometer, (which) is a Optical Instrument (which) is a Instrument hasFilterCentralWavelength: Wavelength hasLowerBoundFormationHeight: Height ArcticCircle is a GeographicRegion hasLatitudeBoundary: hasLatitudeUpperBoundary: GeoMagneticActivity has ProxyRepresentation GeophysicalIndex is a ProxyRepresentation (in Realm of Neutral Atmosphere) Kp is a GeophysicalIndex hasTemporalDomain: “daily” hasHighThreshold: xsd_number = 8 Date/time when KP => 8

49 Knowledge representation - visual UML – Universal Modeling Language –Ontology Definition Metamodel/Meta Object Facility (OMG) for UML –Provides standardized notation CMAP Ontology Editor (concept mapping tool from IHMC - ) –Drag/drop visual development of classes, subclass (is-a) and property relationship –Read and writes OWL –Formal convention (OWL/RDF tags, etc.) White board, text file

50

51 Representing processes

52 Is OWL/RDF the only option? No… SKOS - Simple Knowledge Organization Scheme for Taxonomies Annotations (RDFa) – for un- or semi- structured information sources Atom (and RSS) – for representing syndication feeds – structured More expressive languages IKL, CL, …

53 Use Case Provide a decision support capability for an analyst to determine an individual’s susceptibility to avian flu without having to be precise in terminology (-nyms)

54

55

56 Building SKOS ThManager Protégé (4) plugin for SKOS

57 Is OWL the only option II? No… Natural Language (NL) –Read results from a web search and transform to a usable form –Find/filter out inconsistencies, concepts/relations that cannot be represented Popular options –CLCE (common logic controlled english) –Rabbit, e.g. ShellfishCourse is a Meal Course that (if has drink) always has drink Potable Liquid that has Full body and which either has Moderate or Strong flavour –PENG (processable English) Really need PSCI - process-able science but that’s another story (research project)

58 Sydney syntax If X has Y as a father then Y is the only father of X. The class person is equivalent to male or female, and male and female are mutually exclusive. equivalent to The classes male and female are mutually exclusive. The class person is fully defined as anything that is a male or a female.

59 PENG - Processible English 1.If X is a research programmer then X is a programmer. 2.Bill Smith is a research programmer who works at the CLT. 3.Who is a programmer and works at the CLT?

60 CLCE - Common Logic Controlled English CLCE: If a set x is the set of (a cat, a dog, and an elephant), then the cat is an element of x, the dog is an element of x, and the elephant is an element of x. PC:~( ∃ x:Set)( ∃ x1:Cat)( ∃ x2:Dog)( ∃ x3:Elep hant)(Set(x,x1,x2,x3) ∧ ~(x1 ∈ x ∧ x2 ∈ x ∧ x3 ∈ x))

61 Rules (aka ‘Logic’) OWL-DL and OWL-Lite are based on Description Logic There are things that DL cannot express (though there are things that are difficult to express with rules and easy in DL...) –A well known examples is Horn rules (eg, the ‘uncle’ relationship): (P1 ∧ P2 ∧...) → C –e.g.: parent(?x,?y) ∧ brother(?y,?z) ⇒ uncle(?x,?z) –Or, for any X, Y and Z: if Y is a parent of X, and Z is a brother of Y then Z is the uncle of X

62 Examples from A simple use of these rules would be to assert that the combination of the hasParent and hasBrother properties implies the hasUncle property. Informally, this rule could be written as: –hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3) In the abstract syntax the rule would be written like: –Implies(Antecedent(hasParent(I-variable(x1) I- variable(x2)) hasBrother(I-variable(x2) I- variable(x3)))Consequent(hasUncle(I-variable(x1) I- variable(x3)))) From this rule, if John has Mary as a parent and Mary has Bill as a brother then John has Bill as an uncle.

63 Examples An even simpler rule would be to assert that Students are Persons, as in –Student(?x1) ⇒ Person(?x1).Implies(Antecedent(Student(I- variable(x1)))Consequent(Person(I-variable(x1)))) –However, this kind of use for rules in OWL just duplicates the OWL subclass facility. It is logically equivalent to write instead Class(Student partial Person) or SubClassOf(Student Person) –which would make the information directly available to an OWL reasoner.

64 Semantic Web with Rules Metalog RuleML SWRL RIF OWL 2 RL WRL Cwm Jess - rules engine

65 Query Querying knowledge representations in OWL and/or RDF OWL-QL (for OWL) -ql/ -ql/ SPARQL for RDF and XQUERY (for XML) SeRQL (for SeSAME) RDFQuery (RDF) Few as yet for natural language representations

66 Developing a service ontology Use case: find and display in the same projection, sea surface temperature and land surface temperature from a global climate model. Find and display in the same projection, sea surface temperature and land surface temperature from a global climate model. Classes/ concepts: –Temperature –Surface (sea/ land) –Model –Climate –Global –Projection –Display …

67 Service ontology Climate model is a model Model has domain Climate Model has component representation Land surface is-a component representation Ocean is-a component representation Sea surface is part of ocean Model has spatial representation (and temporal) Spatial representation has dimensions Latitude-longitude is a horizontal spatial representation Displaced pole is a horizontal spatial representation Ocean model has displaced pole representation Land surface model has latitude-longitude representation Lambert conformal is a geographic spatial representation Reprojection is a transform between spatial representation ….

68 Service ontology A sea surface model has grid representation displaced pole and land surface model has grid representation latitude- longitude and both must be transformed to Lambert conformal for display

69 Best practices (some) Ontologies/ vocabularies must be shared and reused - swoogle.umbc.edu, bioportal, OOR Examine ‘core vocabularies’ to start with –SKOS Core: about knowledge systems –Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management –FOAF: about people and their organizations –SIOC: about communities –DOAP: on the descriptions of software projects –DOLCE seems the most promising to match science ontologies Go “Lite” as much as possible, then increasing logic - balancing expressibility vs. implementability Minimal properties to start, add only when needed

70 Summary The science of knowledge representation has, throughout its history, consisted of a compromise between pragmatism, scientific rigor, and accessibility to domain experts Many different options for ontology development and encoding, i.e. knowledge representation Sometimes, your choice of representation may need to change based on language and tools availability/ capability… Balancing expressivity and implementability means we favor an object-type, e.g. DL representation (but also suggests the need for a meta-representation: e.g. KIF – Knowledge Interchange Format) Next class (3) – ontology engineering Use cases should drive the functional requirements of both your ontology and how you will ‘build’ one (see class 4)

71 Assignments for Week 2 Reading: Semantic Web for the Working Ontologist Assignment 1: Representing Knowledge and Understanding Representations