Formal Ontology and Information Systems

Nicola Guarino (FOIS’98)

Presenter: Yihong Ding

CS652 Spring 2004

2

Ontologies are important

Knowledge engineering Knowledge representation Language engineering Qualitative modeling Information modeling Information retrieval, extraction, and integration Object-oriented analysis Knowledge management and organization Database design Agent-based system design The Semantic Web

3

e-Business requests

“Trying to engage with too many partners too fast is one of the main reasons that so many online market makers have foundered. The transactions they had viewed as simple and routine actually involved many subtle distinctions in terminology and meaning”

Harvard Business Review, October 2001

4

Technical problem for e-Business requests

“Lack of technologies and products to dynamically mediate discrepancies in business semantics will limit the adoption of advanced Web services for large public communities whose participants have disparate business processes”

Gartner Research, February 28, 2002

5

XML is not the solution

“XML is only the first step to ensuring that computers can communicate freely. XML is an alphabet for computers and as everyone who travels in Europe knows, knowing the alphabet doesn’t mean you can speak Italian or French”

Business Week, March 18, 2002

6

Open- and closed-world assumptions

Closed-world assumptionThe information provided is complete (a knowledge base

contains all relevant facts).Known the knowledge base is incomplete (does not have

enough information to produce an answer to a question), a decision must be made without complete information.

If you cannot prove P or not P, assume it is false.This is the usual semantics of relational databases.The closed-world assumption is designed to finesse but not

solve these problems and is adopted in default of a better solution.

7

Open- and closed-world assumptions

Open-world assumptionAny proposition or theorem which cannot be derived from

the facts and axioms present in the system is held to be unknown.

Things which are known to be true or false must be stated explicitly, or else inferable from facts and axioms.

The two boolean values (true and false) are inadequate, and we have to use the ThreeValuedLogic.

The open-world assumption more clearly models reality.The number of domains can be infinite.

8

Example: from schema to ontology

Car Make Model

001 Ford Taurus

002 Honda Accord

… … …… … …

… … …

Schema: Closed-world assumption

Local DatabaseCar

make

model

has

has

9

Example: from schema to ontology

Car

make

model

has

has

… Model… Taurus… Accord… …… …… …

Automobile definition

… Model… f-150… Civic… …… …… …

www.cars.com

Car makes and models

Automobile make and model definition

Ontology: Open-world assumption

10

What is an ontology?

“An ontology is a formal, explicit specification of a shared conceptualization.” [Gruber 93]

Formal The ontology should be machine readable.

Explicit The type of concepts use, and the constraints of their use are explicitly

defined. Shared

The ontology should capture consensual knowledge accepted by the communities.

Conceptualization An ontology is an abstract model of phenomena in the world by having

identified the relevant concepts of those phenomena.

11

What is a conceptualization?

Formal structure of (a piece of) reality as perceived and organized by an agent, independently of: the vocabulary used the actual occurrence of a specific situation

apple

melasame conceptualization

LI

LE

12

Conceptualization

a

b

c e

d

Scene 1: blocks on a table Scene 2: a different arrangement

a

b

c

e

d

Conceptualization of scene 1:<{a, b, c, d, e }, {on, above, clear, table }>

13

Relations vs. Conceptual Relations

ordinary (extensional) relations are defined on a domain D:

conceptual (intensional) relations are defined on a domain space <D, W>

rn 2 Dn

n : W 2 Dn

A conceptualization is a set of conceptual relations defined on a domain space.

14

Intended Model and Ontological Commitment

World structure: a structure of <D, R>, which refers to a particular world

Intended world structure: a world structure for a conceptualization in a particular world Each conceptualization contains many of them. One intended world structure for each world.

Intended model: the representation of an intended world structure in a model by ontological commitment.

Ontological commitment: the intensional interpretation of a logical language L

15

Ontologies and Intended Models

Models MD(L)

Language L

Commitment: K = < C,I >

Conceptualization

OntologyIntended

models IK(L)

Interpretation

16

Ontology Quality

Good

WORSE

imprecision

incompleteness

Bad

17

The Ontology Integration/Sharing Problem (1)

M(L)

IA(L)IB(L)

Agents A and B can communicate only if their intended models overlap

18

The Ontology Integration/Sharing Problem (2)

M(L)

IA(L)

IB(L)

Two different ontologies may overlap while their intended models do not

(especially if the ontologies are not accurate enough)

19

The role of foundational ontologies (1)

ITOP(L)

IA(L)

M(L)

IB(L)

False agreement!

False agreement minimized

20

The role of foundational ontologies (2)

Bottom-up integration of domain-specific ontologies can never guarantee consistency of intended models (despite apparent logical consistency).

Top-level foundational ontologiesSimplify domain-specific ontology designIncrease quality and understandabilityEncourage reuse

21

Hierarchies of Ontologies

22

Towards Ontology-Driven IS: temporal dimension (1)

Using an ontology at development time Benefit

Enable knowledge reuse instead of software reuse Enable application domain knowledge reuse and share

across heterogeneous software platformsAvoid bothering too much on implementation details

Two scenariosFirst scenario: has ontology library containing reusable

domain and task ontologiesSecond scenario: has very generic ontology consisting of

coarse domain-level distinctions

23

Towards Ontology-Driven IS: temporal dimension (2)

Using an ontology at run time Benefit

Enable communication between software agents

Two casesOntology-aware IS: an IS component is just aware of the

existence of a (possibly remote) ontology and can query it for whatever specific application purpose

Ontology-driven IS: the ontology is just another component (typically local to the IS), cooperating at run time towards the “higher” overall IS goal

24

Towards Ontology-Driven IS: structural dimension

Ontology as a database component

Ontology as a user interface component

Ontology as an application program component