John F. Sowa

Extending and formalizing the framework for information systems architecture

John Zachman introduced a framework for information systems architecture (ISA) that has been widely adopted by systems analysts and database designers. It provides a taxonomy for relating the concepts that describe the real work 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.

Conceptual graphs for a data base interface

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 users view and access data according to the systems 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.

Ontology, Metadata, and Semiotics

The Internet is a giant semiotic system. It is a massive collection of Peirce’s three kinds of signs: icons, which show the form of something; indices, which point to something; and symbols, which represent something according to some convention. But current proposals for ontologies and metadata have overlooked some of the most important features of signs. A sign has three aspects: it is (1) an entity that represents (2) another entity to (3) an agent. By looking only at the signs themselves, some metadata proposals have lost sight of the entities they represent and the agents – human, animal, or robot – which interpret them. With its three branches of syntax, semantics, and pragmatics, semiotics provides guidelines for organizing and using signs to represent something to someone for some purpose. Besides representation, semiotics also supports methods for translating patterns of signs intended for one purpose to other patterns intended for different but related purposes. This article shows how the fundamental semiotic primitives are represented in semantically equivalent notations for logic, including controlled natural languages and various computer languages.

Top-level ontological categories

Abstract Philosophers have spent 25 centuries debating ontological categories. Their insights are directly applicable to the analysis, design, and specification of the ontologies used in knowledge-based systems. This paper surveys some of the ontological questions that arise in artificial intelligence, some answers that have been proposed by various philosophers, and an application of the philosophical analysis to the clarification of some current issues in AI. Two philosophers who have developed the most complete systems of categories are Charles Sanders Peirce and Alfred North Whitehead. Their analyses suggest a basic structure of categories that can provide some guidelines for the design of AI systems.

Implementing a semantic interpreter using conceptual graphs

A parser applies grammar rules to generate a parse tree that shows the syntactic structure of a sentence. This paper describes a semantic interpreter that starts with a parse tree and generates conceptual graphs that represent the meaning of the sentence. To generate conceptual graphs, the interpreter joins canonical graphs associated with each word of input. The result is a larger graph that represents the entire sentence. During the interpretation, the parse tree serves as a guide to show how the graphs are joined. Both the front-end parser and the back-end semantic interpreter are written in the Programming Language for Natural Language Processing (PLNLP).

TOWARD THE EXPRESSIVE POWER OF NATURAL LANGUAGE

The structure of a knowledge representation language depends critically on its ultimate goal. For conceptual graphs, the goal is a system of logic that can express the propositional content of sentences in natural language in as simple and direct a manner as possible. Since there are still many unsolved problems in semantics, the system of conceptual graphs must continue to evolve to accommodate new research. But the central core of the system is stable, and new features have fit into place in a smooth way. This chapter discusses the main features of conceptual graphs, their use in semantics, and their relationship to the predicate calculus. For most sentences in ordinary language, the mapping to conceptual graphs is shorter, simpler, and more direct than the mapping to predicate calculus. For some aspects of language, especially context dependencies, predicate calculus has no way to represent them, but conceptual graphs can represent them in a principled way.

Conceptual Graphs: Draft Proposed American National Standard

This is a copy of the draft proposed American National Standard (dpANS) as established May 3, 1999. It is a draft, with many parts remaining to be completed. The most curent version (updated as the standard approaches completion) is always available at: http://concept.cs.uah.edu/CG/Standard.html.

On controlled natural languages: properties and prospects

This collaborative report highlights the properties and prospects of Controlled Natural Languages (CNLs). The report poses a range of questions concerning the goals of the CNL, the design, the linguistic aspects, the relationships and evaluation of CNLs, and the application tools. In posing the questions, the report attempts to structure the field of CNLs and to encourage further systematic discussion by researchers and developers.

Relating Diagrams to Logic

Although logic is general enough to describe anything that can be implemented on a digital computer, the unreadability of predicate calculus makes it unpopular as a design language. Instead, many graphic notations have been developed, each for a narrow range of purposes. Conceptual graphs are a graphic system of logic that is as general as predicate calculus, but they are as readable as the special-purpose diagrams. In fact, many popular diagrams can be viewed as special cases of conceptual graphs: type hierarchies, entity-relationship diagrams, parse trees, dataflow diagrams, flow charts, state-transition diagrams, and Petri nets. This paper shows how such diagrams can be translated to conceptual graphs and thence into other systems of logic, such as the Knowledge Interchange Format (KIF).

Conceptual graphs as a universal knowledge representation

Abstract Conceptual graphs are a knowledge representation language designed as a synthesis of several different traditions. First are the semantic networks, which have been used in machine translation and computational linguistics for over thirty years. Second are the logic-based techniques of unification, lambda calculus, and Peirces existential graphs. Third is the linguistic research based on Tesnieres dependency graphs and various forms of case grammar and thematic relations. Fourth are the dataflow diagrams and Petri nets, which provide a computational mechanism for relating conceptual graphs to external procedures and databases. The result is a highly expressive system of logic with a direct mapping to and from natural languages. The lambda calculus supports the definitions for a taxonomic system and provides a general mechanism for restructuring knowledge bases. With the definitional mechanisms, conceptual graphs can be used an intermediate stage between natural languages and the rules and frames of expert systems—an important feature for knowledge acquisition and for help and explanations. During the past five years, conceptual graphs have been applied to almost every aspect of AI, ranging from expert systems and natural language to computer vision and neural networks. This paper surveys conceptual graphs, their development from each of these traditions, and the applications based on them.