Sowa's Conceptual Graphs Research Articles

Using linguistic knowledge in view integration: Toward a third generation of tools

This paper addresses the problem of view integration in a CASE tool environment which is aiming at the elaboration of a conceptual schema of an application. The previous integration tools were mainly based on syntax and structure comparisons. A new generation of intelligent tools is now arising, assuming that view integration algorithms must also capture the deep semantics of the objects represented in the views. Dealing with the semantics of the objects is now a realistic objective, thanks to the research results obtained in the natural language area. This paper presents the definition of a view integration algorithm enhanced by the use of linguistic knowledge. This algorithm mainly consists of a semantic unification of views which are described using an extended entity-relationship model. It is combined with natural language techniques such as Fillmore's semantic cases and Sowa's conceptual graphs, supported by semantic dictionaries.

The linguistic level: Contribution for conceptual design, view integration, reuse and documentation

This article presents the linguistic level of KHEOPS, describing its structure and the information it contains. Even though natural language specification interfaces are a main aspect of the KHEOPS environment we limit our discussion to the paraphrasing, view integration, and schema reuse aspects, presenting their main characteristics and how they make use of the said level. In fact, the major problem in these procedures, is the need to understand the schema semantics. To help in this sense, the linguistic level of KHEOPS offers semantic relations, Fillmore's semantic cases and Sowa's conceptual graphs stored in semantic electronic dictionaries.

Representing Clinical Narratives Using Conceptual Graphs

The analysis of medical narratives and the generation of natural language expressions are strongly dependent on the existence of an adequate representation language. Such a language has to be expressive enough in order to handle the complexity of human reasoning in the domain. Sowa's Conceptual Graphs (CG) are an answer, and this paper presents a multilingual implementation, using French, English and German. Current developments demonstrate the feasibility of an approach to natural Language Understanding where semantic aspects are dominant, in contrast to syntax driven methods. The basic idea is to aggregate blocks of words according to semantic compatibility rules, following a method called Proximity Processing. The CG representation is gradually built, starting from single words in a semantic lexicon, to finally give a complete representation of the sentence under the form of a single CG. The process is dependent on specific rules of the medical domain, and for this reason is largely controlled by the declarative knowledge of the medical Linguistic Knowledge Base.

Modelling knowledge in Electronic Study Books

Abstract Knowledge graphs are a new form of knowledge representation. They are closely related to semantic networks and can be looked upon as in line with Schank's conceptual dependency theory and Sowa's conceptual graphs. The special feature of knowledge graphs is the use of a very restricted set of types of relations, that is considered to be the basic set of primitive relations. The theory of knowledge graphs is outlined in the first part of the paper. In the second part the possibilities of knowledge graphs for solving problems posed by Electronic (Study) Books will be discussed.

Metaphor as a mechanism for reorganizing the type hierarchy

Abstract Metaphor is not just a surface phenomenon, a particular form of speech, but an indication of some underlying cognitive mechanisms. Metaphor provides a way of violating the usual semantic constraints and categories, and joining together ideas that were previously seen as dissimilar, and, in so doing, it extends one's conceptual framework. The paper explores how the mechanisms of metaphor can be used to develop a more flexible means of knowledge representation for artificial intelligence. The theory of metaphor used in the paper is based on Max Black's interaction theory, and has been developed by Way. The knowledge-representation scheme is that of John Sowa's conceptual graphs. The techniques for representing knowledge in machines have provided a new language and a new set of conceptual tools which can make sense of Black's often vague and intuitive theory.

Extending the conceptual graph approach for data conceptual modelling

Information Systems design methodologies include a phase called data conceptual modelling which corresponds to a true knowledge engineering activity. During this phase, relevant objects, their properties and proportions involving these objects are identified and described in a data conceptual schema. Designers often rely on unformal linguistic techniques in order to build data conceptual schemas. Semantics are generally specified by constraints on possible database populations and no meaning is associated with the proposition names, apart from intuitive meaning. The informal level of semantics is open to different interpretations, while the restriction of fact instances does not necessarily convey meaning to any observer. Alternatively, Sowa's conceptual graphs, while providing a linguistic level semantics are not ideal as a data conceptual schema language. This has led us to propose an extended conceptual graphs model. In this paper we discuss the data conceptual modelling process, we present the main features of E-R and NIAM models and we note some difficulties when using them for conceptual modelling. An extended conceptual graph model (ECG) which permits conceptual graphs to be used as a data conceptual schema language is introduced, and the equivalence between the ECG and E-R and NIAM models is discussed.

Conceptual graph matching: a flexible algorithm and experiments

Abstract Graph matching is recognized as a central problem across a variety of application areas, and application-specific matchers have been developed with different simplifying assumptions to reduce the computational complexity. Graph matching is viewed as a form of plausible reasoning when conceptual information contained in graphs are considered, and thus requires an underlying algorithm flexible and general enough to accommodate application-specific matching heuristics and schemes that determine the degree of plausibility. This paper presents such an algorithm, based on the notion of association graphs, developed for matching Sowa's conceptual graphs (CG). While the general subgraph isomorphism problem is known to be NP-complete, matching graphs containing conceptual information appears to be computationally tractable. Following the detailed description of the algorithm, some experimental results are shown to discuss the time complexity of the algorithm and its practicality.