Nov 2002© Per Flensburg Ontology An introduction and overview.

nov 2002 © Per Flensburg

Ontology

An introduction and overview


The concept of ontology

• Ontology have something to do with meaning• It is used within data bases and artificial

intelligence.• Often called “semantics” since it deals with

meaning of lingusitic expressions• These concepts are here used synonymously.


Sources

• Main source for these slides: Dieter Fensel: Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce

• lnteresting articles about semantics and ontology: http://w3.msi.vxu.se/~per/IVC743/Semantics.html

• There are also reports from some courses in Växjö dealing with various things about ontology


Definition (från AI)

• In the simplest case, an ontology describes a hierarchy of concepts related by subsumption relationships; in more sophisticated cases, suitable axioms are added in order to express other relationships between concepts and to constrain their intended interpretation.


More definitions

• Fensel: shared and common understanding of a domain that can be communicated between people and heterogeneous and widely spread application systems.

• Fensel again: ontologies describe the static domain knowledge of a knowledge-based system.


More about ontologies

• A language for defining ontologies is syntactically and semantically richer than common approaches for databases.

• The information that is described by an ontology consists of semi-structured natural language texts and not tabular information.

• An ontology must be a shared and consensual terminology because it is used for information sharing and exchange.

• An ontology provides a domain theory and not the structure of a data container.


Example -schedule


Schedule - syntax

• Simple table structure• Data base schema


Schedule - instance

• A row in the schedule• Is in fact a representation of a fact.


Schedule - Data Description

The number of the week, according to standard ISO 321-543-432-645.a

The day expressed as weekday, number and month

• Is a meta-meta description in relation to the fact


Ontology for IVC743

• Name: Schedule at VXU• Purpose: Temporal relation between the following entities:

Room, Person and Activity.• Temporal expression: Week, day and time in sep-oct• Room-domain: All lecture rooms at Växjö university with a

capacity of at least 35 persons• Person-domain: PF, RL, Inge Andersson, Olle Dahlborg,

Carina Hallqvist, Bertil Ekdahl, Anna Wingkvist, Gunnar Mosnik, Jonas Richardson

• Activity-domain: {a description of what is going to be dealt with in each lecture}


Purpose

• The ontology can be used in many cases, not only for our schedule

• It says something about the content, not the form• If you see an instance with a value not belonging

to the ontology, you know something is incorrect• If you are familiar with the ontology, no further

explanations is needed in order to understand the meaning.


Course ontology

• This generic ontology has the following syntax:• Name:<string>• Purpose: <Temporal/causal/result…> relation

between <list of entities>• Temporal expression: <ddd dd mmm hh - hh>• Room-domain: <list of lecture rooms>• Person-domain: <list of teachers>• Activity-domain: <list of activities>


Initiatives

• Resource Description Framework (RDF)• Semantic web• XML Schemes, standard for describing the

structure and part of the semantics of data.• XSL, describing mappings between different

presentation sheets.


Intranets and semantics

• In a fast changing world knowledge becomes increasingly important

• Maintaining and accessing knowledge (organisational memory) is thus important.

• The knowledge is often weakly structured, stored in intranets and in different formats (picture, sound etc.)

• Knowledge management, which turn information into useful knowledge is thus heavily needed.

Knowledge = Content


Document management

• Key-word based retrieval provides lots of irrelevant information out of context.

• Extracting information requires human attention, both for extracting and integrating

• Maintaining weakly structured sources is time-consuming


Semantic possibilities

• Search for content, not key-words• Query answering instead of information retrieval• Correct exchange of structured or semi-

structured information via for instance XSL.• Define view on documents or sets of

documents, information fusion


• Agents that find the best shopping opportunity.

B2C-siteB2C-site

Example: Shop-bots

B2C-siteB2C-site

Shop-bot

Wrapper


Problems with current shop-bots

• A wrapper is needed for each place and type of bot.

• No flexibility in retrieving the information• Information at the B2C-site must be provided in a

structured form.• Usually this information is provided in natural

language also which inevitably will cause inconsistency problems


Solution

• Using various XML-techniques provides better possibilities for translation between the bot and the site.

• However, they must share the same ontology.• An ontology describes the various products and

can be used to navigate and search automatically for the required information.


Electronic commerce B2B

• Standard techniques, such as EDIFACT cumbersome and error-prone and not integrated with other documents.

• The XML-family of techniques can be used for describing syntax and semantics of data, but not for the business processes and for the products.

• Standard ontologies in combination with XLS-based translation services is thus needed.


About ontologies

A brief introduction


Definition

• An ontology is a formal, explicit specification of a shared conceptualisation.

• A conceptualisation refers to an abstract model of some phenomenon in the world which identifies the relevant concepts of that phenomenon.

• Explicit means that the type of concepts used and the constraints on their use are explicitly defined.

• Formal refers to the fact that the ontology should be machine readable.


Role of an ontology

• Facilitate the construction of a domain model by providing a vocabulary of terms and relations.

• Still the problem of translating between different ontologies persist.

• Also when you go outside the target for the ontology you are lost. The ontology might conserve a certain way of thinking.


Types of ontologies

• Domain ontologies capture the knowledge valid for a particular type of domain

• Metadata ontologies like Dublin Core provide a vocabulary for describing the content of on-line information sources (Libraries).

• Generic or common sense ontologies aim at capturing general knowledge about the world, providing basic notions and concepts for things like time, space, state, event etc.


More types

• Representational ontologies provide representational entities without stating what should be represented. A well-known representational ontology is the Frame Ontology which defines concepts such as frames, slots, and slot constraints allowing the expression of knowledge in an object-oriented or frame-based way.

• Method and task ontologies provide a reasoning point of view on domain knowledge such as hypothesis, cause-effect statements etc.


Constructing ontologies

• Prerequisite: ontologies are small modules with a high internal coherence and a limited amount of interaction between the modules.

• Constructing a new ontology is a matter of assembling existing ones.• Inclusion • Restriction• Polymorphic refinement


Formal languages

• Various kind of formal languages are used for representing ontologies, among others• Description logics• Frame Logic• First-order predicate logic extended with meta-

capabilities to reason about relations.


The Sensus system

• The basic idea is to use so-called seed elements which represent the most important domain concepts for identifying the relevant parts of a toplevel ontology.

• The selected parts are then used as starting points for extending the ontology with further domain specific concepts.


Word-Net

• An on-line lexical reference system. English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept. Different semantic relations link the synonym sets.

• WordNet contains around 100.000 word meanings organized in a taxonomy.

• http://www.cogsci.princeton.edu/~wn/


Semantical relationships

• Synonymy: Similarity in meaning of words.• Antonymy: Dichotomy in meaning of words• Hyponymy: Is-a relationship between concepts.

This is-a hierarchy ensures the inheritance of properties from superconcepts to subconcepts.

• Meronymy: Part-of relationship between concepts.

• Morphological relations which are used to reduce word forms.


Features of Word-Net

• Free of charge• Multilingual European version also exists (

http://www.let.uva.nl/~ewn )

• Its large size (i.e., number of concepts)

• Its domain-independence

• Its low level of formalization

• The definitions are vague and limits the possibility for automatic reasoning support


Example


Example (con’t)


CYC http://www.cyc.com/

• Comes from AI• Humans decide based on their common sense

knowledge what to learn and what not to learn from their observations.

• CYC started as an approach to formalize this knowledge and provide it with a formal and executable semantics.

• Hundreds of thousands of concepts have been formalized with millions of logical axioms, rules, and other assertions.


Features of CYC

• The upper-level ontology of CYC with 3000 concepts has been made publicly available.

• Most of the more specific concepts are kept secret• CYC groups concepts into microtheories to structure the

overall ontology. They are a means to express context dependency i.e., what is right in one context may be wrong in another

• CycL, a variant of predicate logic, is used as language for expressing these theories.



TOVE (TOronto Virtual Enterprise)

• Task and domain-specific ontology. • The ontology supports enterprise integration,

providing a shareable representation of knowledge in a generic, reusable data model

• TOVE provides a reusable representation (i.e., ontology) of industrial concepts.

• http://www.eil.utoronto.ca/tove/toveont.html


From home-page


Next picture


Characteristics

• It provides a shared terminology for the enterprise that each agent can jointly understand and use

• It defines the meaning of each term in precise and unambiguous manner as possible

• It implements the semantics in a set of axioms that will enable TOVE to automatically deduce the answer to many “common sense” questions about the enterprise

• It defines a symbology for depicting a term or the concept constructed thereof in a graphical context


(KA)2 – a case study on

• Knowledge Annotation Initiative of Knowledge Acquisition Community

• http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html • The process of developing an ontology for a

heterogeneous and world-wide (research) community• The use of the ontology for providing semantic access to

on-line information sources of this community.


Example in (KA)2

• Class: research-topic• Attributes:• Name: <string>• Description: <text>• Approaches: <set-of keyword>• Research-groups: <set-of research-

group>• Researchers: <set-of researcher>• Related-topics: <set-of research-

topic>• Subtopics: <set-of research-topic>• Events: <set-of events>• Journals: <set-of journal>

•Projects: <set-of project>•Application-areas: <text>•Products: <set-of product>•Bibliographies: <set-of HTML-link>•Mailing-lists: <set-of mailing-list>•Webpages: <set-of HTML-link>•International-funding-agencies: <funding-agency>•National-funding-agencies: <funding-agency>•Author-of-ontology: <set-of researcher>•Date-of-last-modification: <date>


Procedure

• A lot of instances of the schema was developed and published on the home page

• Examples:• specification languages• knowledge acquisition methodologies• agent-oriented approaches• knowledge acquisition from natural language• knowledge management


Knowledge management

An application


Knowledge management deals with

• Acquiring • Maintaining• Accessing knowledge of an organization.• Here we will apply it to internet and concentrate

on the last issue: Search for knowledge.


Search engines

• They have typically three parts: A webcrawler for downloading, an indexer for finding key terms and a query interface that retrieves answers to the proposed questions.

• They are all based on keywords. The indexing process of the web-pages is thus crucial for the retrieval.


Search domain

• Consists of about 300 millions fix documents, but this is only about 20% of what is available in total. The rest (80%) is dynamically generated (example: Aftonbladet)

• Altavista provides it all, Google sort according to documents pointing at the actual document and Yahoo uses human invention.


Dimensions in searching

• Precision: how many retrieved documents are really relevant?

• Recall: have I found all relevant information?• Time: for the humans to find the desired

information among the retrieved.• Scattering:The information might be scattered

over several pages with only implicit relations between them


Ontobroker

• Define an ontology• Use it to annotate/structure/wrap your web

documents• Somebody else can make use of Ontobroker’s

advanced query and inference services to consult your knowledge.

• To achieve this goal, Ontobroker provides three interleaved languages and two tools.


Languages

• It provides a representation language formulating ontologies.

• A subset of it is used to formulate queries, i.e. to define the query language.

• An annotation language is offered to enable knowledge providers to enrich web documents with ontological information.


Representation languageR

epre

sent

atio

n la

ngua

ge


Some definitions

• Class definition: c[]• defines a class with name c.

• Attribute definition: c[a=>> {c1,...,cn}]• implies that the attribute a can applied to the elements of c

and an attribute value must be member of all classes c1,...,cn.

• Is-a relationship: c1:: c2

• defines c1 as a subclass of c2 which implies that:• all elements of c1 are also elements of c2

• all attributes and their value restrictions defined for c2 are also defined for c1, and

• multiple attribute inheritance exists


More definitions

• Is-element-of relationship: e : c• defines e as an element of the class c.

• Rules like• FORALL x,y x[a ->> y] <- y[a ->> x].

• If a is an attribute for x it is also an attribute for y

• FORALL x,y x:c1[a1 ->> y] <-> y:c2[a2 ->> x].• The common set of attributes for x and y


Annotation language

• Ontobroker provides an annotation language called HTMLA

• The following HTML page states that the text string „Richard Benjamins“ is the name of a researcher where the URL of his homepage is used as his object id.

• <html><body><a onto="page:Researcher"> <h2>Welcome to my homepge</h2>

• My name is <a onto="[name=body]">Richard Benjamins</a>. </body></html>

• Cf XML!


Query language

• The query language is defined as a subset of the representation language. The elementary expression is:

• X ∈ c Λ attribute(x) = v• written in Frame logic:• x[attribute -> v] : c


Inference engine


Summary

• Ontobroker only recognises pages that are annotated according to its rules.

• Principally you still have a database and a database schema in the bottom

• What can’t be expressed in first order predicat logic can’t be expressed.


New idea (On2broker)

The web

Info agentData base

Fact retrieval

Ontologies

XML, RDF etc.Query engine


Cf, Skogsresurs

Concept base Search agent URL

ClassificationURL-databaseProfile

Interesting linksUser

www.skogsresurs.com


Comments

• Facts are defined by the logic of the ontologies • They are retrieved according to logical rules• They are stored into formal databases• Thus only facts possible to express in first order

predicat logic is possible to retrieve.• Also On2broker has severe efficiency problems

if the number of facts extend 100 000


IBROW: Dynamic reasoning

• Brokering dynamic reasoning services in WWW• http://www.swi.psy.uva.nl/projects/IBROW3/home.html • It will access libraries in the Internet, search for

appropriate inference services, verify their requirements, request additional information from the customer if needed, adapt the inference services to the particular domain knowledge, plug them together, and execute them via CORBA.

• Therefore, the user no longer buys, downloads and installs software. Instead he uses it as a computational service provided via the network


Use of IBROW

• In a business-to-business (B2B) context, IBROW technology can be used to construct half products, which then need further processing by industries before delivering end products to consumers. For example, a car manufacturer could be interested in a service that helps him to develop and/or adapt a new car design.

• In another scenario, the IBROW broker provides a service to configure the bare bones of a knowledge system, which then needs to be refined for end consumers based on their particular needs.


Other use

• Yet another model would use IBROW technology to provide an underlying infrastructure to support knowledge engineers in selecting, testing, adapting, refining, and combining generic components into concrete systems.


Electronic Commerce

An application are for ontologies


Types of products

• Intelligent information search agents (i.e., shopping agents) that help customers to find products.

• Intelligent information providers (i.e., on-line stores) that help vendors to present their goods in appropriate manner.

• Intelligent information brokers (i.e., on-line market places) that mediate between buyers and vendors.


Shopbots

Client (Browser)

Shopbot

Wrapper 1

On-line store 1

Wrapper 2

On-line store 2

Wrapper 3

On-line store 3


Examples

• Bookblvd (http://www.bookblvd.com/ ), • Bottom Dollar (http://www.bottomdollar.com/ ),• Buyer’s Index (http://www.buyersindex.com/ ), • CompareNet (http://www.compare.net/ ),• Dealpilot (http://www.dealpilot.com/ ), • Jango (http://www.jango.com/ ), • Junglee (http://www.junglee.com/)• MyShop (http://www.myshop.de ), • Shopfind (http://www.shopfind.com/ )• Shopper (http://www.shopper.com ).


Why shopbots fail

• Web users do not want to pay because they are used to free service.

• Product providers do not want to fund the agent because of its ability to always find the cheapest source.

• Product providers would fund the agent if it manipulated the search results. This would eliminate objectivity however which is a requirement for high acceptance.

• In the end, most of them were bought by Internet portals

I think Fensel principally has right, but in practice shop-bots have become a tremendous succes!

I think Fensel principally has right, but in practice shop-bots have become a tremendous succes!


Other types of bots

• Lifestyle finder recommends documents matching your interests based on your answers to a set of questions.

• Alexa: watches all its users and anticipates their preferences. An “intelligent” proxy which caches the pages a user will visit.

• Firefly asks for ratings of specific musical artists, correlates each user with others who share their tastes, and recommends songs or albums which their cohorts have rated highly.


B2B

• 1:1, negotiation, often EDIFACT-based

• 1:N, A large company dictates, often EDIFACT

• N:M, a fragmented marketplace


The fragmented marketplace

• Replace a trade agent• Bring seller and buyer together on global base• Require means for translation and

representation• Require means for content description


Translation


Representation and translation

• RDF provides a standard for describing semantics

• XML provides a standard for describing the structure of a document

• XML schema provides a standard for describing the semantics

• XSL provides a standard for describing mappings between terminologies

• But none provides a standard vocabulary


Ontologies in B2B

• Corresponds to standardised product catalogues. Attempts:• Common business library (http://www.commerce.net

) • Commerce XML (http://www.oasis-open.org )• Dublin Core (http://www.indecs.org )• Rosettanet (http://www.rosettanet.org/ )• Etc.


Branch portals

• Chemdex (www.chemdex.com)• Life science products trading

• PaperExchange (www.paperexchange.com)• Paper industry spot market

• VerticalNet (www.verticalnet.com)• Generic portal


VerticalNet

Seller ontology 1

Seller ontology 2

Seller ontology 3

Buyer ontology 1

Buyer ontology 2

Buyer ontology 3

Tran

sfor

mat

ion Visualisation

Standardontology

Ontologymapping


To sum it all up

Putting it all together


The techniques

• XML: Describing the structure of a document, cf. defining layout in a database

• DTD: • Element declaration that define composed tags and value

ranges for elementary tags• Attribute declaration that define attributes of tags• Entity declaration

• XSL: Describes how to render the document and the processing of the parts there, cf. calculating fields in a database. Can be used for translation


The role of XSL

XML

DTD1

XML

DTD2

XSL


A document

XML

DTD

XSL

Name: Value on name

Part: Value on part

Quantity: Value on quant

Price: Value on price

Part: Value on part

Quantity: Value on quant

Price: Value on price

Total price: Value on total

Adress: Value on adr

Ontololgy


XML and semantics

• XML describe documents in tree format• Thus the overlaying context is transferred to the

underlying branches• This is the only semantics that exists.


RDF

• A subject is an entity that can be referred to by a address in the WWW (i.e., by an URI).

• A predicate defines a binary relation between resources and/or atomic values provided by primitive data type definitions in XML.

• An object specifies for a subject a value for a predicate. That is, objects provide the actual characterizations of the Web documents.


Example of RDF

• Author(http://www.msi.vxu.se/~per/) = X• Name(X) = Per Flensburg• Email(X) = [email protected]• Claim(Anna)=(affiliation(Author(

(http://www.msi.vxu.se/~per/))=Växjö university)


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