The Semantic Web: Implications for Future Intelligent Systems

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

2. 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!

3. 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

4. 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.

5. 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’

6. 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

7. 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)

8. 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

9. 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..)

10. 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

11. 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’)

12. 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.

13. Oil (Ontology Infrastructure Language) Oil is a web-integrated prototype standard for specifying Ontologies Based on Web Standards XML and RDF(S) Natural Frame-based (OO) language OIL Description-logic-based semantics and reasoning Tool Support e.g. Oiled

14. Hierarchy of Languages DAML + OiL RDFS RDF XML

15. 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>

16. 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…!

17. 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

18. 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

19. Example - automated planning GOAL => PLANNING AGENT Sensors Reasoning Mechanism World Model (facts, objects, actions, rules) => PLAN => Effectors

20. 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

21. “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..

22. 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!)

23. References • You can get these slides from my web site • Any other resources are easy to get - eg search on “Semantic Web Conference”...