Semantic Web – Lecture 3

Ontology languages and methods

«The Semantic Web and its languages» «Ontologies: Principles Methods, and

Applications»

Wei Feng Ida Kokkersvold Geir Solskinnsbakk

The Semantic Web and its languages

The semantic web and its languages Resource Description Framework (RDF) Ontology Inference Layer (OIL) DARPA Agent Markup Language (DAML)

RDF - introduction

Web metadata standard (W3C) Interoperability between applications

exchanging machine-understandable information

Formal semantics

RDF - Modeling

Items described are called resourcesAll items named by a URI can be described

Object – attribute – value (statements)hasPrice(book, $30)

RDFS – a set of ontological modeling primitives on top of RDF

RDF - Syntax

XML Lacks primitive data types Data model whose syntax is largely

irrelevantSeveral syntaxes were proposed

RDF – applications

Mozilla – representation format RSS (RDF Site Summary) DAML OIL

OIL - introduction

Machine accessible semantics XML and RDF Frame based language

Classes (frames)Properties (slots)

Description LogicsFormal semanticsEfficient reasoning

OIL - Syntax

Well defined syntax based on XML Extension of RDF / RDFS

RDF applications will understand many constructs in OIL

RDFS provides modeling primitives such as instance-of and subclass-of relationships, and syntax for writing class hierarchies

Extension makes OIL a full modelling language

OIL – basic constructs

Illustration of the most basic constructs

Class definition Slots – properties Restricted values Combining classes

using logical exspressions

OIL - applications Search engines

Search semantic concepts rather than matching keywords

E-commerce (comparing prices)Today: shop-botsFuture: shared ontologies that agents can use

to create mappings between product catalogs Knowledge management

Transform document repositories into knowledge repositories

Oil – design principles

Compatability with W3C standards – XML/RDF

Maximize expressiveness to model a wide variety of ontologies

Provide a formal semantics Enable sound, complete, and efficient

reasoning Limiting expressiveness

OIL vs RDF/RDFS

RDF / RDFS syntax -> Backward compatibility

OIL ontologies are partly available to RDF-aware applications

DAML - introduction

Funded by US Government DAML-Ont / DAML-Logic DAML-Ont Released October 2000

(http://www.daml.org)

Replaced by DAML+OIL in January 2001 (http://www.daml.org)

OWL derived from DAML+OIL (http://www.w3.org)

DAML - motivation

Providing a fundament for the semantic Web

Making semantic information available to agents

Compatability with current and future internet technology

DAML was created as an example language by the DARPA project

DAML - description

Communities can extend simple shared ontologies (highlevel concepts)

Mark objects on the Web Include descriptions of:

Information Functions Data

Web based information fusion

DAML vs OIL

OIL achieves better backward compatability with RDFS than DAML

Some constructs in DAML make reasoning services comparable to OIL impossible

In OIL one can state either sufficient or sufficient and necessary conditions for a class, making automatic classification possible

Ontologies: Principles, Methods

and Applications

Why Ontologies and What are they

What are the Problems?The lack of a shared understanding about communication leads to poor communication within and between these people and their

organizations

The lack of a shared IT system understanding leads to difficulties in identifying requirements and thus in the defining of a

specification of the system

Disparate modelling methods, paradigms, languages and software tools severely limit

Inter-operability the potential for reuse and sharing

Why Ontologies and What are they

How can we Solve them ?To reduce or eliminate conceptual and terminological confusion and come to a shared understanding. Communication between people with different needs Inter-Operability among systems achieved by translating between

different modeling methods paradigms languages and software tools System Engineering Benefits

• Re-Usability• Reliability• Specication

What is an ontology?

Ontology is the term used to refer to the shared understanding of some domain of interest which may be used as a unifying framework to solve the above problems.

Uses of Ontologies

We subdivide the space of uses forontologies into the following three

categories: Communication Inter-Operability Systems engineering

Communication

Ontologies reduce conceptual and terminological confusion by providing a unifying framework within an organization. Now these are several aspects of the use of ontologies to facilitate communication: Normative Models Networks of Relationships Consistency and Lack of Ambiguity Integrating Different User Perspectives

Inter-Operability

A major theme for the use of ontologies in domains such as enterprise modeling and multiagent architectures is the creation of an integrating environment for different software tools.

Dimensions of Inter-Operability: Internal Inter-Operability External Inter-Operability Integrated Ontologies Among Domains Integrating Ontologies Among Tools

Ontology as Inter-Lingua Example

System Engineering

Specification The shared understanding can assist the process of identifying

requirements and defining a specification for an IT system. Reliability

Informal ontologies can improve the reliability of software systems by serving as a basis for manual checking of the design against the specification.

Using formal ontologies enables the use of semi-automated consistency checking of the software system with respect to the declarative specification.

Reusability To be effective, ontologies must also support reusability so that we

can import and export modules among different software systems. Ontologies provide an easy to reuse library of class objects for

modeling problems and domains.

A Skeletal Methodology for Building Ontologies

We envisage a comprehensive methodology fordeveloping ontologies to include the following: Identify Purpose and Scope Building the Ontology

ontology capture ontology coding integrating existing ontologies

Evaluation Documentation Guidelines for each phase

Ontology Capture

Ontology capture consists of identifying and defining theimportant concepts and terms. We consider the followingfour phases in turn: scoping, producing definitions, review,and development of a meta-ontology. Scoping

Brainstorming Grouping

Produce DefinitionsThe main work of building an ontology is producing definitions

Deciding What To Do Next Determining Meta-Ontology

let the careful consideration of the concepts and their inter-relationships determine the requirements for the meta-ontology.

Work AreasAddress each work area in turn.

TermsProceed in a middle-out fashion rather than top-down or bottom-up.

Ontology Capture

Benefits of a Middle-Out Approach A bottom-up approach results in a very high level of

detail. This, in turn: increases overall effort makes it difficult to spot commonality between

related concepts increases risk of inconsistencies which leads in

turn to re-work and more effort

Ontology Capture

A top-down approach results in better control of the level of detail, however starting at the top can result in choosing and imposing arbitrary high-level categories. Because these are not naturally arising, there is a risk of less stability in the model which in turn leads to rework and greater effort.

Ontology Capture

A middle-out approach can result in: Detail arises only as necessary, by specializing

the basic concepts, so some effort is avoided. By starting with the most important concepts first,

and defining higher level concepts in terms of these.

The higher level categories naturally arise and thus are more likely to be stable.

This, in turn, leads to less rework and less overall effort

Ontology Capture

Formal approach Formal framework for design and evaluating ontologies

Formality required in an ontology depend on the degree of automation

Formal language to specify and design ontologies

Declarative specification

Formal language for implementing an ontology: KIF

Overview of a Formal Methodology

Procedure for a formal approach to ontology design and evaluation

1. Capture of motivating scenarios2. Formulation of informal competency

questions3. Specification of the terminology of the

ontology within a formal language4. Formulation of formal competency

questions using the terminology of the ontology Determine TontologyU Tground╞ Q Determine whether TontologyU Tground╞ not Q

Consist of the following steps

Consist of the following steps

1. Specification of axioms and definitions for the terms in the ontology within the formal language

2. Justification of the axioms and definitions by proving characterization theorems

Inter-Operability

Process Interchange Format

KRSL Plan Ontology

Ontologies as Standards

Workflow Management Coalition

STEP

CORBA

KIF and Conceptual Graphs

Integration of ontologies

CYC

TOVE

KACTUS

Plinius

KSL Ontology Server

A tool for design and development of ontologies

Overcome problems in knowledge sharing

Uses KIF

Under development

Future Research

Ontologies for Inter-Operability

Tools for Ontology Design

Ontology Libraries

Future Research

New Ontologies

Integrating Ontologies

Methodologies