The Semantic Web: Implications for Future Intelligent Systems Lee McCluskey, Artform Research Group, Department of Computing And Mathematical Sciences,

The Semantic Web: Implications for Future Intelligent Systems

Lee McCluskey,

Artform Research Group,

Department of Computing And Mathematical Sciences,

University of Huddersfield

Artform Research Group

Meta-Talk

Outline: Web / Semantic Web

XML,RDF,Oil,DAML… Ontologies Intelligent Systems IS and the SW

Related Areas: Distributed AI (DAI), Ontologies, Mark-up Languages, Standards, Knowledge Management, e-Commerce (B2B,B2C), OODBS, ...

Caveat: I usually give seminars on subjects which I am familiar as a researcher and builder of the technology - NB with this one I am only a potential user!


Take-home Slide The Semantic Web is the Vision (not a current reality) of

having an internet with resources that are machine understandable or accessible to automated processes - machines should do much more than present the information visually or do human-consumable IR.

Already very high level languages are being designed for this purpose (with XML as “machine code”)

The first International SW conference took place in 2002 (ICSW’02, Sardinia)

Processes on the SW will need to do AI Planning and AI Planning can exploit the SW to do Knowledge Acquisition.

BUT still a long long way to go before realisation


WWW

- is successful largely through the use of layers of internationally accepted standards (TCP/IP,html)

- ‘first generation’ - hand written html pages

- ‘second generation’ - dynamic web - pages created by programs to display the results of a process, or the output of a query of an accessed database.

--so web pages used as an interface to networked processes (services) as well as for general information display.


WWW +

Now: much R&D has been directed at writing programs/services that utilise HTML web info

EG my favourite - ISI’s travel assistant - a web service that uses other web services (weather, timetables, hotel) to make travel plans in response to a high level directive “I need to be in X on days Y using budget Z”

BUT: this is very hard because of poor web structures.. Eg ISI’s travel assistant has to use a learning program to induce web page ‘wrappers’


Metadata and XML

Not surprisingly, to start to give ‘meaning’ to info on the web we must use META-DATA eg using tags around data to describe its content.

In XML - eXtensible Mark-up Language - tags are not fixed - one can invent new tags to structure the information in a web page.

XML is considered to be the basis for all semantic web languages - the “machine code” of the new generation web


RDF - the Resource Description Framework

RDF is a convention for describing meta-data.

It’s a ‘lightweight’ model in data terms - and one that can be encoded in XML. It is based on

everything having a URI = Universal Resource Identifier

Properties - resources with a ‘name’ such as slots in an object frame

An RDF document is a series of Statements -

(Resource, Property, Value)


RDF example

RDF ~ set of (Resource, Property, Value)"The Author of

http://scom.hud.ac.uk/scomtlm/Artform/planning.html

is Lee McCluskey.”

IN RDF:

<rdf:Description about= http://scom.hud.ac.uk/scomtlm/Artform/planning.html'> <Author> Lee McCluskey </Author>

</rdf:Description>

Resource, Property, Values can all have URI’s


RDFS -

RDF Schema = RDF + classes, properties of properties, etc - gives more structure to RDF

This would make the Semantic Web look like one enormous distributed OODB (… but DB’s usually angled towards one application - the SW (will be?) is ubiquitous..)


Vocabularies….

RDF/RDFS allows anyone to write their own

name-space document (a ‘schema’). This defines properties and classes in some application domain

These form vocabularies which can be used globally for sharing the meaning of tags


Ontologies

An ‘Ontology’ is an agreed on, shared, common understanding of a domain written as an explicit, formal specification.

(cf my seminar on Ontologies last year! Slides can be got via my website under ‘seminar slides’)


Ontologies First used for Knowledge-Sharing in KBS, it seems

that many scientific areas are creating their own Ontologies (even Genomics!!)

They can be as simple as a ‘concept hierarchy’ or as complex as an axiomatic theory of sets.

There are two “kinds” of ontology: representation ontology (axiomatization of basic operations used in many applications) and application ontology which are domain specific.


Oil (Ontology Infrastructure Language)

Oil is a web-integrated prototype standard for specifying Ontologies

OIL

Natural Frame-based (OO) language

Based on Web Standards XML and RDF(S)

Description-logic-based semantics and reasoning

Tool Support e.g. Oiled


Hierarchy of Languages

DAML + OiL

RDFS

RDF

XML


DAML+Oil example Define a "product number"'s domain and range..

<daml:DatatypeProperty rdf:ID="productNumber">

<rdfs:label>Product Number</rdfs:label>

<rdfs:domain rdf:resource="#Product"/>

<rdfs:range rdf:resource=

"http://www.w3.org/2000/10/XMLSchema#nonNegativeInteger"/>

</daml:DatatypeProperty>

”Availability" is a sort of enumerated type..

<daml:Class ID="Availability">

<daml:oneOf parseType="daml:collection">

<daml:Thing rdf:ID="InStock">

<rdfs:label>In stock</rdfs:label> </daml:Thing>

<daml:Thing rdf:ID="BackOrdered">

<rdfs:label>Back ordered</rdfs:label> </daml:Thing>

<daml:Thing rdf:ID="SpecialOrder">

<rdfs:label>Special order</rdfs:label> </daml:Thing>

</daml:oneOf>

</daml:Class>


What about Sevices?

The idea of the SW now extends to ‘Services’… networked processes may be specified in terms of pre- and post conditions, where these conditions are written in a globally accepted mark-up language…!


Intelligent KBS

Two related, major problems have remain unsolved in symbolic intelligent systems -

-- Knowledge Acquisition - can be prohibitive! New forms of reasoning / intelligent process have had to have new hand-crafted knowledge bases built for them for each application area in which they are need to work.

-- ‘General’ reasoning systems don’t scale up: The high profile successes tend to be in very narrow applications such as in Chess or Expert Systems


The Solution in the Semantic Web

The SW may solve BOTH these problems:

-- The SW infrastructure of Ontologies and structured web information could ease the problems of knowledge acquisition

-- It could provide a global, dynamic, ‘unlimited’ environment in which Intelligent Processes can be deployed

General Planning and Learning programs will be much easier to apply and evaluate


Example - automated planning

Sensors

Reasoning Mechanism

World Model (facts, objects, actions, rules)

Effectors

GOAL =>

=> PLAN =>

PLANNINGAGENT


Example - automated planning

Knowledge Acquisition:

facts - from effective SW information retrieval

actions - pre- and post conditions of semantic web services

Online Ontologies - allow the Agent to interpret the acquired facts and actions

Sensors - e.g. results of searches or communications

Effectors - e.g. execution of semantic web services


“My theory”... The biggest problems in AI planning

currently – are lack of Accessibility and Usability of the technology

Timely maturing of 4 research areas –

-- Semantic Web

-- knowledge engineering, sharing and re-use

-- Planning language conventions

-- Planning KE

will solve these problems..


Conclusions The Semantic Web looks like becoming a

reality in the future (when?). It will be a bit like an enormous distributed OODB with composable services

The Semantic Web’s dynamic, boundless aspects coupled with structured descriptions of info and processes will present great opportunities for research and developers of AI technology (as well as much else!)


References

You can get these slides from my web site Any other resources are easy to get - eg search

on “Semantic Web Conference”...

Documents

The Semantic Web: Implications for Future Intelligent Systems Lee McCluskey, Artform Research Group, Department of Computing And Mathematical Sciences,