Challenges in the New E-ra: the Pandora's Box for AI?

This document emerged after a series of two talks given by Yannis Kalfoglou and Daniela Carbogim to the SSP Research Group at the Division of Informatics, on the 8th and the 15th of March of 2000, respectively.

As a result of the aforementioned talks, we have produced this document in order to support timely dissemination of knowledge and alert scholars on issues that might be interesting to them. We would very much appreciate comments, ideas and contributions you may have, so feel free to send us an email.


The New E-ra

Nowadays, we are witnessing a new electronic era (in short, e-ra) and the development of a global information society.

The Internet has some remarkable characteristics which have given rise to the Information Revolution: But "revolution" is about radical change (whereas "evolution" is about gradual change), so what are the changes in this new Information Society? Here are some candidates These changes create needs and expectations in the Information Society - in the new e-ra - which result for instance in new forms of work organisation (Domenico De Masi argues that in the post-industrial society we won't be able to differentiate work from leisure, neither from learning), new communities, new types of legal and juridical problems. The new e-ra creates cooperation-driven demands, business-driven demands, technology-driven demands...

AI has provided a number of methods and techniques for representing and structuring knowledge and reason about it. This is the main reason why we feel that AI has an important role to play in the new Information Society.

Links: The Information Society

The New E-ra Demands & Expectations

It is a widely acknowledged fact that the Internet alters the way we do business, we communicate, we socialise, in short, changes our lives. This has injected unparallel interest from all sorts of businesses in an area traditionally studied by computer scientists; and poses such demands as: On the other hand, recent advances of the new e-ra technologies drive demands as: which fuels our expectations:

AI Contributions and Potential Applications

We identify the following areas of AI contributions: which could be deployed to application areas such as:


As this document is not meant to be an ontologies recourse, we limit our links and references to collections of information related to the field. These are collections accessible online and not references to the literature, like special issues of journals.

Links: Ontologies

Computational Logic and Logic-Based Techniques

Let us look at the role of logic-based reasoning techniques can play in this new e-ra, especially from the perspective of information integration.

"Information integration is about getting information sources to talk to each other"

This definition is given in a roadmap paper produced by the computational logic community entitled Information Integration and Computational Logic.

Some of the work carried out by the SSP Group has been related to logic-based information integration, such as:

In the Computational Logic community, most of the work on information integration has been about using logic as an integration framework for defining mediators. Mediators are components that link different information sources to the user and applications.

By modelling the contents of information sources and the relation between the different sources and the mediator, logic-based representation and reasoning thus provides a mechanism for communication and "intelligent" cooperation.

Note that when we talk about semantic integration of information, a number of issues arise:

These have been at the centre of logic-based knowledge representation research:

However, if the use of logic-based information integration is to be scaled up in the new e-ra, it should be somehow supported by web technology.

An example is the work on integration of business rules for e-commerce (CommonRules), which provides an XML encoding of corteous logic programs, used to represent business rules. Applications of business rules in multi-agent and e-commerce scenarios include negotiation between agents.

Links: Logic-Based Approaches for Information Integration


We look at argumentation as a reasoning technique for dealing with imperfect information. Conflict is the essence of argumentation

Argumentation paradigm: reasoning by constructing and weighing up arguments.

A shift on focus:

CSCA & Mediation Systems

Applications to design of artifacts (both in the world of bits and in the world of atoms): supporting collaborative processes. Argumentation-based Design Rationale is about explicitly recording reasons why an artifact was developed in some way.

It has been argued that argumentation-based design rationale is useful and usable, and plays several roles in design such as:

The aim of mediation systems is to augment and mediate argumentation in groups:

Successes and Failures of CSCA systems involve the interaction of a number of factors such as:

A vision for the role of argumentation in the new e-ra:

Argumentation and Negotiation

Negotiation: the problem of achieving mutually acceptable agreements between agents.

Can argumentation provide or support intelligent interaction between agents?

Our view is that we can ground a number of problems with very distinctive characteristics (such as negotiation and design) into a similar source in argumentation.

Links: Argumentation

XML & XML Schemata

XML is a metalanguage for creating markup languages that describe data:

DTD's (Document Type Definition) and XML Schemata

StyleSheet Technologies: CSS and XSL

Links: XML & XML Schemata

RDF & RDF Schemata

RDF (Resource Description Framework) was among the first standards of W3C for processing meta-data. Briefly speaking, RDF provides the means for adding semantics to a document without making any assumptions about the structure of the document. The strength of RDF is that it provides meta-level facilities: you can make statements about statement.

In detail, the data model of RDF provides three object types:

A resource is an entity that can be referred to by an address (URI), a property defines a binary relation between resources and/or atomic values provided by primitive datatype definitions in XML, and a statement specifies a value for a property.

For example, the following statement:


defines Yannis as the author of the mentioned web page. In the same fashion, the statement:


is a meta-statement, where we define that Daniela claims that the author of the mentioned web page is Yannis.

RDF Schemata (in short, RDFs) provide a basic type schema which could be "directly used to describe an ontology". They are based on core classes, property types, and constraints. Core classes are: Resource, Property type, Class. Core property types are: instance-of, subclass-of. Core constraints are: range and domain.

Of particular interest, are the constraints definition facilities. Some examples of constraints are:

  1. "the value of a property should be a resource of a designated class. This is expressed by the range property. For example, a range constraint applying to the 'author' property may express that the value of an 'author' property must be a resource of class 'person'."
  2. "a property may only be used on properties of a certain class. For example, that an 'author' property could only originate from a resource that was an instance of class 'Book'. This is expressed using the 'domain property'."

Links: RDF & RDF Schemata

Knowledge Management and Organisational Memory

Knowledge Management (KM) is the: "formal management of knowledge for facilitating creation, access, and reuse of knowledge".

Organisational memories (OMs) "provide the means for storing, retrieving and distributing knowledge from an organisation's repositories".

KM aims to develop and deploy knowledge whereas OMs preserve knowledge. They both centered upon the enhancement of an organisation's competitiveness by improving the way it manages its knowledge.

Why do we need KM? Because of

  1. "environmental pressures: increasingly competitive global market place (maybe 'marketspace'?)",
  2. "technological advancements: recent developments in Internet technology".
The goal of KM is to "create valuable information: convert individually available knowledge into group or organisationally available knowledge".

Convert and connect processes:

KM, OMs and the new E-ra:

It is a fact that KM/OMs were always a concern for organisations. There was always an interest and investment to technologies that can effectively support and achieve efficient KM. So what the new E-ra has to do with it?

The emergence of the new E-ra made KM/OMs a necessity. Why?

The technological infrastructure of the new E-ra facilitates the development, deployment, distribution, and maintenance of knowledge assets that otherwise would require expensive equipment, high level of expertise, and labour-intensive engineering to implement. It is exactly these knowledge assets that are now recognised by many organisations as their most valuable assets, a situation which is anticipated to be the rule rather than the exception in the foreseeable future. Globalisation makes organisations to re-think and revise their structure in order to continue to ...exist! Whereas a well-designed KM/OMs could drive this change the new E-ra provides the infrastructure to achieve this.

As for ontologies, this is not meant to be a definitive resource of information for KM and OMs. We provide, however, some interesting links.

Links: Knowledge Management and Organisational Memory

Is The New E-ra a Pandora's Box for AI?

After a fruitful discussion we have managed to reach a verdict! We took into account all possible angles of viewing this intricate issue and came up with a perspective conclusion to our issue: "is the new e-ra the Pandora's box for AI?".

Here is the answer, in a structured way: