Ontology Engineering Maintenance Semantic Web Fall 2005 Computer

Ontology Engineering & Maintenance Semantic Web - Fall 2005 Computer Engineering Department Sharif University of Technology

Outline Ontology Engineering p Ontology Maintenance p n n n Mapping Merging Integration

Introduction p Why do we use ontology? n To describe the semantics of the data (which we name as Meta-Data) p Why do we describe the semantics? n In order to provide a uniform way to make different parties to understand each other p Which data? n Any data (on the web, or in the existing legacy databases)

Introduction p Formal definition on Ontology: n p Ontologies are knowledge bodies that provide a formal representation of a shared conceptualization of a particular domain. Ontologies are widely used in the Semantic Web. n Recently ontologies have become increasingly common on WWW where they provide semantics of annotations in web pages

What Is “Ontology Engineering”? Ontology Engineering: Defining terms in the domain and relations among them n n Defining concepts in the domain (classes) Arranging the concepts in a hierarchy (subclass -superclass hierarchy) Defining which attributes and properties (slots) classes can have and constraints on their values Defining individuals and filling in slot values

Ontology-Development Process here: determine scope consider reuse enumerate terms define classes define properties define constraints create instances define classes enumerate terms define classes create instances In reality - an iterative process: determine scope consider reuse define properties define classes consider reuse define properties enumerate terms define properties define constraints consider reuse define constraints create instances

Determine Domain and Scope determine scope p p p consider reuse enumerate terms define classes define properties define constraints create instances What is the domain that the ontology will cover? For what we are going to use the ontology? For what types of questions the information in the ontology should provide answers?

Consider Reuse determine scope p consider reuse enumerate terms define classes define properties define constraints create instances Why reuse other ontologies? n n n to save the effort to interact with the tools that use other ontologies to use ontologies that have been validated through use in applications

What to Reuse? p Ontology libraries n n n p DAML ontology library (www. daml. org/ontologies) Ontolingua ontology library (www. ksl. stanford. edu/software/ontolingua/) Protégé ontology library (protege. stanford. edu/plugins. html) Upper ontologies n n IEEE Standard Upper Ontology (suo. ieee. org) Cyc (www. cyc. com)

What to Reuse? (II) p General ontologies n n p DMOZ (www. dmoz. org) Word. Net (www. cogsci. princeton. edu/~wn/) Domain-specific ontologies n n UMLS Semantic Net GO (Gene Ontology) (www. geneontology. org)

Enumerate Important Terms determine scope consider reuse enumerate terms define classes define properties define constraints create instances What are the terms we need to talk about? p What are the properties of these terms? p What do we want to say about the terms? p

Define Classes and the Class Hierarchy determine scope p n n p enumerate terms define classes define properties define constraints create instances A class is a concept in the domain n p consider reuse a class of wines a class of wineries a class of red wines A class is a collection of elements with similar properties Instances of classes n a glass of California wine you’ll have for lunch

Class Inheritance p p Classes usually constitute a taxonomic hierarchy (a subclass-superclass hierarchy) A class hierarchy is usually an IS-A hierarchy: an instance of a subclass is an instance of a superclass p p If you think of a class as a set of elements, a subclass is a subset e. g. , Apple is a subclass of Fruit Every apple is a fruit

Levels in the Hierarchy Top level Middle level Bottom level

Modes of Development top-down – define the most general concepts first and then specialize them p bottom-up – define the most specific concepts and then organize them in more general classes p combination – define the more salient concepts first and then generalize and specialize them p

Documentation p Classes (and Properties) usually have documentation n p Describing the class in natural language Listing domain assumptions relevant to the class definition Listing synonyms Documenting classes and slots is as important as documenting computer code!

Define Properties (Slots) of Classes determine scope p consider reuse enumerate terms define classes define properties define constraints create instances Properties in a class definition describe attributes of instances of the class and relations to other instances Each wine will have color, sugar content, producer, etc.

Properties (Slots) p Types of properties n n p “intrinsic” properties: flavor and color of wine “extrinsic” properties: name and price of wine parts: ingredients in a dish relations to other objects: producer of wine (winery) Simple and complex properties n n simple properties (attributes): contain primitive values (strings, numbers) complex properties: contain (or point to) other objects (e. g. , a winery instance)

Property Constraints (facets) determine scope p consider reuse enumerate terms define classes define properties define constraints create instances Property constraints (facets) describe or limit the set of possible values for a property The name of a wine is a string The wine producer is an instance of Winery A winery has exactly one location

An Example: Domain and Range DOMAIN class p p slot allowed values When defining a domain or range for a slot, find the most general class or classes Consider the flavor slot n n p RANGE Domain: Red wine, White wine, Rosé wine Domain: Wine Consider the produces slot for a Winery: n n Range: Red wine, White wine, Rosé wine Range: Wine

Create Instances determine scope p enumerate terms define classes define properties define constraints create instances Create an instance of a class n n p consider reuse The class becomes a direct type of the instance Any superclass of the direct type is a type of the instance Assign slot values for the instance frame n n Slot values should conform to the facet constraints Knowledge-acquisition tools often check that

Defining Classes and a Class Hierarchy p The things to remember: n n p There is no single correct class hierarchy But there are some guidelines The question to ask: “Is each instance of the subclass an instance of its superclass? ”

Transitivity of the Class Hierarchy p The is-a relationship is transitive: B is a subclass of A C is a subclass of B C is a subclass of A p A direct superclass of a class is its “closest” superclass

Multiple Inheritance p p p A class can have more than one superclass A subclass inherits slots and facet restrictions from all the parents Different systems resolve conflicts differently

Disjoint Classes p p Classes are disjoint if they cannot have common instances Disjoint classes cannot have any common subclasses either Red wine, White wine, Rosé wine are disjoint Dessert wine and Red wine are not disjoint Dessert wine Red wine White wine Wine Rosé wine

Avoiding Class Cycles Danger of multiple inheritance: cycles in the class hierarchy p Classes A, B, and C have equivalent sets of instances p n By many definitions, A, B, and C are thus equivalent

The Perfect Family Size p p p If a class has only one child, there may be a modeling problem If the only Red Burgundy we have is Côtes d’Or, why introduce the sub-hierarchy? Compare to bullets in a bulleted list

The Perfect Family Size (II) p p If a class has more than a dozen children, additional subcategories may be necessary However, if no natural classification exists, the long list may be more natural

Single and Plural Class Names A “wine” is not a kind-of “wines” p A wine is an instance of the class Wines p Class names should be either p Class n instance-of Instance n all singular all plural

Classes and Their Names p p p Classes represent concepts in the domain, not their names The class name can change, but it will still refer to the same concept Synonym names for the same concept are not different classes n Many systems allow listing synonyms as part of the class definition

Content: Top-Level Ontologies p What does “top-level” mean? n n n p Objects: tangible, intangible Processes, events, actors, roles Agents, organizations Spaces, boundaries, location Time IEEE Standard Upper Ontology effort n n Goal: Design a single upper-level ontology Process: Merge upper-level of existing ontologies

CYC: Top-Level Categories

WORDNET: Representation of Subclass Relation among Synsets

Sowa’s Ontology

Ontology Analysis p Analysis: semantic consistency n n p Analysis: style n n n p Violation of property constraints Cycles in the class hierarchy Terms which are used but not defined Interval restrictions that produce empty intervals (min > max) Classes with a single subclass Classes and slots with no definitions Slots with no constraints (value type, cardinality) Tools for automated analysis n n Chimaera (Stanford KSL) DAML validator

Ontology Evaluation One of the hardest problems in ontology design p Ontology design is subjective p What does it mean for an ontology to be correct (objectively)? p The best test is the application for which the ontology was designed p

Ontology Maintenance p p p Ontology mapping Ontology merging Versioning and evolution n n Compatibility between different versions of the same ontology Compatibility between versions of an ontology and instance data

Ontology Mapping - Introduction This distributed nature of ontology development has led to a large number of different ontologies covering the same or overlapping domains. Here comes the ontology mapping into the play p In order to two parties to understand each other, they should use the same formal representation for the shared conceptualization (so the same ontology) p Unfortunately it is not easy to make everybody to agree on the same ontology for a domain. p And when you have different ontologies for the same domain the problem shows up. p n Parties with different ontologies do not understand each other.

Ontology Mapping p Given two ontologies O 1 and O 2, mapping one ontology onto another means that for each entity (concept C, relation R, or instance I) in ontology O 1, we try to find a corresponding entity, which has the same intended meaning, in ontology O 2

An Example www. cs. washington. edu www. cs. usyd. edu. au People … Staff Professor James Cook Ph. D, U Sydney Staff Faculty Assoc. Professor Name Education Data Instance Academic Asst. Professor Technical … Senior Lecturer Semantic Mapping Find Prof. Cook, a professor in a Seattle college, earlier an assoc. professor at his alma mater in Australia

Overview of Process People Staff Professor Faculty aff) t S , c (Fa Sim ? Sim(Fac, Acad) Academic Technical Sim( Fac, Pr Asst. of) Professor Senior Lecturer Assoc. Professor Define Similarity Staff Compute Similarity Lecturer Satisfy Constraints

The Ontology Mapping Process

Mapping – Similarity computation – QOM approach

Ontology Merging p p Creating one new ontology from two or more ontologies. The most prominent approaches are the union and the intersection approaches p In the union approach, the merged ontology is the union of all entities in both source ontologies, where differences in representation of similar concepts have been resolved p In the intersection approach, the merged ontology consists only of the parts of the source ontology which overlap

Ontology Merging p From two source ontologies, create a new ontology that: n Contains the information from both sources n Does not contain redundant information n Does not contain inconsistent information

Ontology Merging p One-to-one mapping of ontologies n n n p Mappings are created between pairs of ontologies Problems with this approach arise when many such mappings need to be created The complexity is O(n^2), where n is the number of ontologies Using a global ontology n n Each ontology is mapped to the central ontology Drawbacks of using a global ontology are similar to those of using any standard It is hard to reach a consensus on a standard shared by many people who use different terminologies for the same domain A standard impedes changes in an organization

Ontology Merging

The End