Outline! Introduction to Knowledge Representation and Ontologies! Ontologies and knowledge representation in Computing Sciences!

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1 Outline! Introduction to Knowledge Representation and Ontologies! Gilles Falquet! Université de Genève,! Centre universitaire d informatique! Knowledge representation in computing science:!! computational linguistics and terminology!! artificial intelligence!! information systems!! semantic web! Ontology and ontologies! Languages for ontologies! Ontologies in other domains of knowledge! 1 2 Terminology and Ontology! Ontologies and knowledge representation in Computing Sciences! Terminological Databases! Computational linguistics/terminology!!"terminological databases!!"lexical ontologies! Artificial intelligence!!"reasoning on world states! Information systems!!"object-based analysis and design!!"system interoperability!!"semantic web! Set of entries comprised of! " Term! " Definition! " Source! " Reliability! " Synonyms! " Generic! " Translation! " Etc.! 3 4

2 Terminology and Ontology! Eurodicautom entry! Ontology " Thesaurus! The goal of a thesaurus is to define a controlled vocabulary (e.g. for indexing articles)!! Thesauri are not ontologies! " Entries are not necessarily concepts ( transportation )! " Entries are often domain names! " Some relations are vague (e.g see also )! " The generic/specific relation has several meanings! In Urbamet: Transportation > Accident > Speed! (subdomains)! 5 6 Computational Linguistics: Lexical Ontologies! Wordnet: a Lexical Ontology! Goal: associate senses with words! The lexicon determines the ontology (what is not named does not exist).! Show semantic relationships between senses! Based on the English (Spanish, ) lexicon! Connect each form (sequence of letters) to its senses! A sense (concept) is a synset! # terms (word, sense)! forms (words)! senses! 17% of the words are polysemous! 40% of the words have a synonym! 7 8

3 Senses! "table"! form! n.! n.! term! #table(1)! A piece of furniture having...! #table(2)! A set of data arranged in rows and columns! sense! (concept)! 9 10 Synsets! Semantic Relations! table! mesa! form! $"furniture"! "table"! forme! n.! n.! n.! term! adj.! n.! n.! term! synonym! #table(1)! Piece of furniture...! #table(2)! Flat tableland with steep edget! (synsets)! senses! 11 #meuble(1)! object that...! hyperonymy! #table(1)! A piece of furniture with! sens! 12

4 Meronymy! "leg"! "table"! form! n.! n.! n.! 3 types of parts :! - component! - substance! - member! word! meronym (part)! 13 #leg(3)! objet that supports...! #table(1)! piece of furniture! sense! 14 Applications! Ontologies in Artificial Intelligence! In natural language processing! " Normalization (unify synonyms)! " Word sense disambiguation! Intelligent systems must be able to understand the world, to infer implicit facts, etc.! In information retrieval! " Query expansion (with synonyms, hyponyms, )!?! a! b! 15 Put a on b!! 16

5 "A cube is an object! Logical Models! "A tetrahedron is an object! "Two objects cannot be at the same place! "A cube cannot stand on a tetrahedron! "An object cannot be moved if there is another object on top of it! Use propositional and predicate logic! " to represent world state! " to represent inference rules! Inference engines to deduce implicit facts, to find solutions,! a! b! Put a on b!! The CyC Project! Axiom 1.!x Cube(x) " Object(x)! Axiom 2.!x!y Object(x)! Object(y)! x " y!! " location(x) " location(y)! Axiom 3.!x!y On(x, y) " Movable(y)! Build a theory of commonsense, to add AI to all computer programs! In first order logic! Currently millions of axioms! Grouped in coherent microtheories : geometry, physics, movement, transport,! a! b! (the top level is freely available)! 19 20

6 Information Systems! Object/Class-based models! Collect, store, process, retrieve information that is required to manage an organization.! Necessary to know what exists in the organization s domain.! What type of information do we have?! What are the relations between these information types?!!" Object/Class-based models!!" the UML standard! The Semantic Web Initiative! Machines cannot understand natural language! Hence, the web is not machine processable.! " impossible to write a program to find a German car for sale at a price lower than 1000 %! Idea: associate a formal representation to each web resource.! The Semantic Web Initiative! Alberto sells a Alberto Ford (67sells 0%). a Alberto His Ford (67sells address 0%). a Alberto Ford Hisis (67 sells Geneva address a Alberto Ford (67sells 0%).! Hisis Geneva 0%). a Ford His (67 address! is address 0%). His Geneva! is Geneva address! is Geneva! a1 type car! a1 price 670! a1 make Ford! a1 owner a2! a2 name Alberto! a2 addr a3! a3 city Geneva! a3 street! vehicle! car! Ford! VW!! bicycle!! location! city!! Geneva! Genova!! town! resource! (document)! resource description! (RDF document)! reference ontology! 23 24

7 Ontology and Ontologies! Concept! Ontology (philo.) The branch of metaphysics dealing with the nature of being. In particular:! " Categories of being! " Entities and types of entities! " Relationships between entities! An ontology. Enumeration/description/organisation of existing entities.! " Hierarchies of concepts! " General vs. Local ontologies (for a particular field of knowledge)! A class of objects grouped according to their properties.! " usual sense of concept = general notion, abstract idea! Concept extension: all the objects having the desired properties = the instances of the concept.! Concept intension: the properties that define the concept.! is-a Semantic relation! A Top Level Ontology! Generic/specific: A is more specific than B (A is-a B) if! every instance of A is an instance of B! (inclusion of the extensions)! all cars are vehicles! all humans are animals! Ontologies are usually organized according to the is-a relation.! 27 John F. Sowa. Knowledge Representation! 28

8 Terminology and Ontology! Terminology and Ontology! Languages for Ontological Knowledge Representation! Concepts are strongly related to human languages.! A concept is generally designated by a (list of) word(s)! A syntax!! textual, graphical,!! how to write well formed sentences! In terminology a term is the association of a word with the concept it designates in a particular domain! A semantics!! what do the sentences mean! Table in the Furniture domain! Table in the Data Representation domain! Table in the Database domain! 29 Properties! " Formality: formal syntax and semantics! " Expressiveness: what can we express with this language?! " Computability of reasoning tasks! 30 Semantic networks! Object-oriented Modeling! Syntax: arrows and bubbles! Semantics: not formally defined! Leg! has! is a! Elephant! Animal! eat! Grass! Distinction between the class (concept) and the object (instance) level! Syntax: Class diagrams! " classes (concepts)! " associations (with cardinality constraints)! " attributes! " subclass (is-a) relationships! " aggregation (part-of) relationships! is a! Clyde! 31 No complete formal semantics! A standard: UML! 32

9 CityGML! Example! Animal! weight! Leg! weight! length! 4..4! Elephant! (weight)! name! age! First Order Logic! Predicate logic - Properties! Syntax:! " symbols: variables, predicates, functions,!,#,!, ",, (, ),,! " grammar rules to construct formulaes! Semantics:! interpretation domain (a set)! " predicate symbol # n-ary relation! " function symbol # n-ary function! "!, ", # truth tables! "!,# # evaluation rules! " etc.! 35 Highly expressive! " every algorithm is expressible in PL! But reasoning cannot be fully automated! " no general algorithm for proving that A is a consequence of B (in finite time)! There exist partial theorem provers! " they answer yes, no or run forever! 36

10 Description Logics (DL)! Example Ontology! A family of logic languages! " Reasoning can be automated in many DLs! " Reasonably expressive! " Less expressive than predicate logics! Syntax:! " Concept names, role names, individual names! " and, or, not, some, all, at least, at most! " Axioms for inclusion and equivalence of concepts! Primitive concepts: Man, Woman, Student, GraduateStudent! Roles: CHILD! Axioms ( Terminological Box )! 1." Person $ Man or Woman! 2." GraduateStudent $ Student! 3." Student $ Person! 4." Parent $ Person and some CHILD. Person! 5." AcademicParent $ Parent and (all CHILD. Student)! 6." Man and Woman $ Ø!!(disjointness)! Semantics: based on set theory, the interpretation of a concept is a subset of the universe &.! An interpretation (individuals)! Reasoning Tasks in DL! Parent! AcademicParent! Karl! Student! Subsumption! " Check if C $ D (all C s are D s)! AcademicParent $ Person?! Elena! Suzan! Satisfiability! " Is it possible to have an individual in C?! Emma! Bob! Marc! Instance checking! " Does the individual w belong to C?! CHILD! 39 40

11 OWL! Why Expressive Formal Ontology Languages?! Ontology Web Language! Recommended by the W3 consortium! Three levels: Lite, DL, Full! OWL DL is a DL language (SHOIQ) with an XML syntax! Available tools! " ontology editors (Protégé, )! " reasoners (Pellet, Racer, Fact++, )! (Originally)! " to implement reasoning capabilities in AI systems (robotics, expert systems, artificial mathematicians, )! (Now)! " a formal ontology can be checked for consistent (every concept must be satisfiable)! " reasoners can automatically (re)compute the concept hierarchy! " concept definitions can be formally compared! Using Ontologies in a Domain of Knowledge! Ontology Design! Improve communication! " agree on common concepts => create communication standards and languages! " compare point of views! " communicate with other domains! Reflect on the domain! " e.g. build new conceptual organizations, new concepts! A basis to build information systems or other computerized applications! " e.g. managing, comparing, extracting knowledge from medical reports => reference to common concepts! 43 Define the objectives of the ontology! Di'erent types of ontologies! Top level ontology! Domain ontology! " reference for mutual understanding between human or artificial agents! Task ontology! Application ontology! " automated reasoning is important! " usability! " generally not reusable (too specific)! 44

12 Design Methodology - Methontology! Glossary of Terms! Methontology (Gomez-Perez et al.)! Identify the type of representation! Construction! 1. Glossary of terme! 2. Taxonomy of concepts! 3. Ad hoc binary relations! 4. Concept dictionnary! Description language! Binary relations - Instance attributes - Class attributes - Formal axioms - Rules - Instances! Name! American Airlines Flight! Business Trip! Location! arrival Date! departure Place! Type! Concept! Concept! Concept! Instance Attribute! Relation (Travel, Location)! Other Methods! Ontologies and Point of Views! Example: Grûninger et Fox! " define scenarios! " elaborate capacity questions! " elaborate formal capacity questions (in logic)! " specify axioms! "! Generally impossible or impracticable to have a single domain ontology for all needs.! An approach: consider an ontology as a point of view on a domain!!" Need to manage several point of views! 47 48

13 Ontology alignment! The Towntology Action! animal! vertebrate! mammal! animal! lion! Study the use of ontologies in Urban civil engineering! " To improve communication: H-H, H-C, C-C! Build a repository of (pre)ontologies related to UCE! Identify knowledge sources (legal texts, thesauri, databases ) that could help building urban ontologies! feline! alignment! records! Child ontology! Explore use cases! " practical! " theoretical (in urban morphology)! lion! 49 Explore ontology design and management tools! 50 Example: Articulation Ontology! An more topics! Interconnecting CityGML (3D city model) and an air quality models (mainly di'. equations).! Technique: articulation ontolgy (OUPP)! Ontology management! Extracting ( learning ) ontologies from texts and data! water body! street! buildi ng! part! street! canyon! street! canyon! thermal! conditions! pollutant! distribution! Transforming sources to ontologies! Visualizing large ontologies!! Others ways to define concepts (e.g. prototypical instances)! CityGML! OUPP! AirQuality! 51 52