The history of Artificial Intelligence," said my computer science professor on the first day of class, "is a history of failure." This harsh judgment summed up 50 years of trying to get computers to think. Sure, they could crunch numbers a billion times faster in 2000 than they could in 1950, but computer science pioneer and genius Alan Turing had predicted in 1950 that machines would be thinking by 2000: Capable of human levels of creativity, problem solving, personality, and adaptive behavior. Maybe they wouldn't be conscious (that question is for the philosophers), but they would have personalities and motivations, like Robbie the Robot or HAL 9000. Not only did we miss the deadline, but we don't even seem to be close.

Traditionally, the needs of vulnerable populations have been addressed by a plethora of public and private agencies that rely on donations of money, goods and services which they distribute based on their perception of what is needed and where. This approach, however, lacks a comprehensive understanding of the demand side as well as the ability to coordinate between various suppliers of goods and services, identify latent supply and predict future demand. To help address these issues, we have developed a knowledge-based platform that harnesses advances in several AI fields for efficient and effective provisioning of goods and services.

In order to compare and analyse open data across cities, standard representations or ontologies have to be created. This paper defines a shelter ontology that includes concepts of shelters, slums, households and homelessness. The design of the ontology is based upon the data requirements of ISO 37120. ISO 37120 defines 100 indicators to measure and compare city performance. There are three shelter-themed indicators defined, namely 15.1 Percentage of city population living in slums, 15.2 Number of homeless per 100 000 population, and 15.3 Percentage of households that exist without registered legal titles. This ontology enables both the representation of the ISO 37120 Shelter theme indicators' definitions, and a city's indicator values and supporting data. This enables the analysis of city indicators by intelligent agents.

The Leibniz Center for Law has its roots in the former department of Computer Science & Law of the Law Faculty of the University of Amsterdam, and currently houses about 15 researchers. The Leibniz Center conducts research and provides education in the field of Artificial Intelligence and law. In the tradition of Leibniz, we focus on the development and application of techniques from Artificial Intelligence to the field of Law for the purpose of supporting legal practice, and bringing new insights to legal theory. The Leibniz Center for Law has longstanding experience on legal ontologies, automatic legal reasoning and legal knowledge-based systems, (standard) languages for representing legal knowledge and information, user-friendly disclosure of legal data, and the application of ICT in education and legal practice (e.g. It plays an important role in the development of eGovernment on both national and international level. The center provides advice on change-management issues of knowledge-intensive legal processes and the improvement of knowledge-productivity in legal organisations.

Largest free, formal ontology available, with 25,000 terms and 80,000 axioms when all domain ontologies are combined. These consist of SUMO itself, the MId-Level Ontology (MILO), and ontologies of communications, countries and regions, distributed computing and user interfaces, economy, finance, automobiles and engineering components, Food, Dining, Sports, Shopping catalogs and Hotels, geography, government and Justice, language taxonomy, media and Music, Military (general, devices, processes, people), North American Industrial Classification System, people and their Emotions, physical elements, transnational issues, transportation and its Details, viruses, world airports A-K, world airports L-Z, weapons of mass destruction. See also a large amount of instance content from DBPedia about people and the YAGO, project which includes millions of facts from Wikipedia merged with SUMO, and an initial merge of the Mondial geographical data with SUMO. The Open Biomedical Ontologies are lightly mapped to SUMO. Additional ontologies of terrorism are available on request.

Editor's Note: An update to this article has been posted here on 7/14/04. As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. And they are doing so without relying on the jumble of rule-based techniques common in earlier knowledge representation efforts. These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information. They appear most effective when the semantic distinctions that humans take for granted are crucial to the application's purpose.

Communicative What does it mean to be able to communicate with someone (or something, in the computational case)? Greatly simplifying an issue which has been a cornerstone of intellectual thought for many years, useful communication requires shared knowledge. While this includes knowledge of a language the words and syntactic structures it is even more focused on knowledge about the problem being solved. To deal with a travel agent, you need to be able to talk about travelling, to interact with a florist you need some knowledge of flowers, and to deal with an internet agent you must share a vocabulary about the area of concern. A key problem with current search engines is that although based in language, they have no knowledge of the domains of interest. Searching physics papers would be enhanced if the search engine actually'knew' something about physics (how experiments are performed, which words are ambiguous, whether papers are theorerical or empirical, etc.), rather than simply looking for the appearance of key words.

This is an updated version of Usability Issues in Description Logic Systems'' published in Proceedings of International Workshop on Description Logics, Gif sur Yvette, (Paris), France, September, 1997.

This document has been reviewed by W3C Members and other interested parties, and it has been endorsed by the Director as a W3C Recommendation. W3C's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability of the Web. This is one of six parts of the W3C Recommendation for OWL, the Web Ontology Language. It has been developed by the Web Ontology Working Group as part of the W3C Semantic Web Activity (Activity Statement, Group Charter) for publication on 10 February 2004.

Class: A class defines a group of individuals that belong together because they share some properties. For example, Deborah and Frank are both members of the class Person. Classes can be organized in a specialization hierarchy using subClassOf. There is a built-in most general class named Thing that is the class of all individuals and is a superclass of all OWL classes. There is also a built-in most specific class named Nothing that is the class that has no instances and a subclass of all OWL classes.