John Zachman introduced a framework for information systems architecture (SA) that has been widely adopted by systems analysts and database designers. It provides a taxonomy for relating the concepts that describe the real world to the concepts that describe an information system and its implementation. The ISA framework has a simple elegance that makes it easy to remember, yet it draws attention to fundamental distinctions that are often overlooked in systems design. This paper presents the framework and its recent extensions and shows how it can be formalized in the notation of conceptual graphs. Wopyright 1992 by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied or distributed royalty free without further permission by computer-based and other information-service systems. Permission to republish any other portion of this paper must be obtained from the Editor.

■ We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological, and information-processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.This article is the result of an ongoing collaborative effort by the coauthors, preceding and during the AGI Roadmap Workshop held at the University of Of course, this is far from the first attempt to plot a course toward humanlevel AGI: arguably this was the goal of the founders of the field of artificial intelligence in the 1950s, and has been pursued by a steady stream of AI researchers since, even as the majority of the AI field has focused its attention on more narrow, specific subgoals. The ideas presented here build on the ideas of others in innumerable ways, but to review the history of AI

A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user's view and access data according to the system's view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query. JOHN F. SOWA IBM J. RES. DEVELOP. I complex data base with dozens of relations, few users can correctly guess the meaning of every domain in every relation. The meaning of a relation is called its intension, and the set of all the n-tuples stored in the data base is called its extension. The question of representing extensions, accessing them, and modifying them is the familiar one that all data base systems address. The question of representing intensions, however, tends to be ignored, largely because adequate formalisms and techniques for handling them have not been available. For a data base system, the three principal kinds of intensional information are the functional dependencies, the domain roles, and the constraints on domain values. In a data base relation, functional dependencies indicate which domains are permissible keys and which domains are dependent upon the keys; for the relation in Fig. I , EMPLOYEE is the key domain, and the domains MANAGER and DATE are determined when EMPLOYEE is specified. The domain roles indicate how the domains are related; for Fig. 1 , the MANGER of each n-tuple performs an act, HIRE, the EMPLOYEE is the one who is hired, and the DATE is when the particular act occurred. The constraints indicate permissible values; for Fig. 1, they would specify the expected form of a name or date, the requirement that no date of hire may precede the date the company was founded, and the constraint that no person may hire himself. Besides representing intensions, the system must use them to provide a more natural interface and to check the plausibility of new information that is being added. This paper defines conceptual graphs as an intensional formalism and shows how they might be used to meet the following requirements: Familiar conventions A person who knows the forms and procedures of a business enterprise should be able to ask questions about it without having to learn the peculiarities of the computer system. Automatic inference The system should infer relations that are not stored explicitly in the data base. Naturalness The intensional formalism should be close enough to the semantics of natural language to support convenient dialogue and prompting facilities. Semuntic integrity The domain constraints should help to keep the data base an accurate reflection of the real world. These are requirements for the user's interface; the physical implementation must also satisfy other criteria, such as speed and reliability. By separating the conceptual graphs that describe the meaning of data from the system that stores and accesses da...

A semantic network is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs.

The lexical entry for a word must contain all the information needed to construct a semantic representation for sentences that contain the word. Because of that requirement, the formats for lexical representations must be as detailed as the semantic forms. Simple representations, such as features and frames, are adequate for resolving many syntactic ambiguities. But since those notations cannot represent all of logic, they are incapable of supporting all the function needed for semantics. Richer semantic-based approaches have been developed in both the model-theoretic tradition and the more computational AI tradition. Although superficially in conflict, these two traditions have a great deal in common at a deeper level. Both of them have developed semantic structures that are capable of representing a wide range of linguistic phenomena. This paper compares these approaches and evaluates their adequacy for various kinds of semantic information that must be stored in the lexicon. It presents conceptual graphs as a synthesis of the logicist and AI representations designed to support the requirements of both.