Part 5: Ontologies

Logic and Ontologies Up to now we have studied Logic Languages for Knowledge Representation and Reasoning: in both static and dynamic domains with possibly incomplete knowledge and nonmonotonic reasoning interacting with the environment and completing the knowledge, possibly contracting previous assumptios All of this is parametric with a set of predicates and a set of objects The meaning of a theory depends, and is build on top of, the meaning of the predicates and objects

Choice of predicates We want to represent that trailer trucks have 18 wheels. In 1st order logics:  x trailerTruck(x)  hasEighteenWheels(x) or  x trailerTruck(x)  numberOfWheels(x,18) or x ((truck(x) y(trailer(y)  part(x,y)))  s (set(s)  count(s,18)  w (member(w,s)  wheel(w)  part(x,w))) The choice depends on which predicates are available For understanding (and sharing) the represented knowledge it is crucial that the meaning of predicates (and also of object) is formally established

Ontologies Ontologies establish a formal specification of the concepts used in representing knowledge Ontology: originates from philosophy as a branch of metaphysics Ontologia studies the nature of existence Defines what exists and the relation between existing concepts (in a given domain) Sought universal categories for classifying everything that exists

An Ontology An ontology, is a catalog of the types of things that are assumed to exist in a domain. The types in an ontology represent the predicates, word senses, or concept and relation types of the language when used to discuss topics in the domain. Logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest. Up to now we have implicitly assumed the ontology I assumed that you understand the meaning of predicates and objects involved in examples

Aristotle’s Ontology Being Substance Accident Property Relation Inherence Directedness Containment Movement Intermediacy Quality Quantity Activity Passivity Having Situated Spatial Temporal

The Ontology Effort to defined and categorize everything that exists Agreeing on the ontology makes it possible to understand the concepts Efforts to define a big ontology, defining all concepts still exists today: The Cyc (from Encyclopedia) ontology (over 100,000 concept types and over 1M axioms Electronic Dictionary Research: 400,00 concept types WordNet: 166,000 English word senses

Cyc Ontology

Cyc Ontology Thing Object Intangible Represented Thing Event Stuff Intangible Object Collection Relationship Process Intangible Stuff Attribute value Slot Occurrence Internal machine thing Attribute

Small Ontologies Designed for specific application How to make these coexist with big ontologies?

Domain-Specific Ontologies Medical domain: Cancer ontology from the National Cancer Institute in the United States Cultural domain: Art and Architecture Thesaurus (AAT) with 125,000 terms in the cultural domain Union List of Artist Names (ULAN), with 220,000 entries on artists Iconclass vocabulary of 28,000 terms for describing cultural images Geographical domain: Getty Thesaurus of Geographic Names (TGN), containing over 1 million entries

Ontologies and the Web In the Web ontologies provide shared understanding of a domain It is crucial to deal with differences in terminology To understand data in the web it is crucial that an ontology exists To be able to automatically understand the data, and use in a distributed environment it is crucial that the ontology is: Explicitly defined Available in the Web The Semantic Web initiative provides (web) languages for defining ontologies (RDF, RDF Schema, OWL)

Defining an Ontology How to define a catalog of the types of things that are assumed to exist in a domain? I.e. how to define an ontology for a given domains? What makes an ontology? Entities in a taxonomy Attributes Properties and relations Facets Instances Similar to ER models in databases

Main Stages in Ontology Development Determine scope Consider reuse Enumerate terms Define taxonomy Define properties Define facets Define instances Check for anomalies Not a linear process!

Determine Scope There is no correct ontology of a specific domain An ontology is an abstraction of a particular domain, and there are always viable alternatives What is included in this abstraction should be determined by the use to which the ontology will be put by future extensions that are already anticipated

Determine Scope (cont) Basic questions to be answered at this stage are: What is the domain that the ontology will cover? For what we are going to use the ontology? For what types of questions should the ontology provide answers? Who will use and maintain the ontology?

Consider Reuse One rarely has to start from scratch when defining an ontology In these web days, there is almost always an ontology available that provides at least a useful starting point for our own ontology With the Semantic Web, ontologies will become even more widely available

Enumerate Terms Write down in an unstructured list all the relevant terms that are expected to appear in the ontology Nouns form the basis for class names Verbs form the basis for property/predicate names Traditional knowledge engineering tools (e.g. laddering and grid analysis) can be used to obtain the set of terms an initial structure for these terms

Define the Taxonomy Relevant terms must be organized in a taxonomic is_a hierarchy Opinions differ on whether it is more efficient/reliable to do this in a top-down or a bottom-up fashion Ensure that hierarchy is indeed a taxonomy: If A is a subclass of B, then every object of type A must also be an object of type B

Define Properties Often interleaved with the previous step Attach properties to the highest class in the hierarchy to which they apply: Inheritance applies to properties While attaching properties to classes, it makes sense to immediately provide statements about the domain and range of these properties Immediately define the domain of properties

Define Facets Define extra conditions over properties Cardinality restrictions Required values Relational characteristics symmetry, transitivity, inverse properties, functional values

Define Instances Filling the ontologies with such instances is a separate step Number of instances >> number of classes Thus populating an ontology with instances is not done manually Retrieved from legacy data sources (DBs) Extracted automatically from a text corpus

Check for Anomalies Test whether the ontology is consistent For this, one must have a notion of consistency in the language Examples of common inconsistencies incompatible domain and range definitions for transitive, symmetric, or inverse properties cardinality properties requirements on property values can conflict with domain and range restrictions

Protégé Java based Ontology editor It supports Protégé-Frames and OWL as modeling languages Frames is based on Open Knowledge Base Connectivity protocol (OKBC) It exports into various formats, including (Semantic) Web formats Let’s try it

The newspaper example (part) :Thing Author Person Content News Service Employee Article Advertisement Editor Reporter Salesperson Manager Properties (slots) Persons have names which are strings, phone number, etc Employees (further) have salaries that are positive numbers Editor are responsible for other employees Articles have an author, which is an instance of Author, and possibly various keywords Constraints Each article must have at least two keywords The salary of an editor should be greater than the salary of any employee which the editor is responsible for Attribute