An important conceptual modeling activity in the development of database, object-oriented and agent-oriented systems is the capture and expression of domain constraints governing underlying data and object states. UML is increasingly used for capturing conceptual models, as it supports conceptual modeling of arbitrary domains, and has extensible notation allowing capture of invariant constraints both in the class diagram notation and in the separately denoted OCL syntax. However, a need exists for increased formalism in constraint capture that does not sacrifice ease of use for the analyst. In this paper, we codify a set of invariant patterns formalized for capturing a rich category of propositional constraints on class diagrams. We use tools of Boolean logic to set out the distinction between these patterns, applying them in modeling by way of example. We use graph notation to systematically uncover constraints hidden in the diagrams. We present data collected from applications across different domains, supporting the importance of "pattern-finding" for n-variable propositional constraints using general graph theoretic methods. This approach enriches UML-based conceptual modeling for greater completeness, consistency, and correctness by formalizing the syntax and semantics of these constraint patterns, which has not been done in a comprehensive manner before now

This paper compares two representations used for concept modeling: the entity-relationship (ER) approach, and conceptual graphs (CG), and shows how these techniques can be combined for carrying out the task of conceptual analysis for a domain. The two techniques are contrasted with regard to various semantic aspects: the kind of knowledge being modeled, goals, context, primitives, type of semantics, and type of truth. The conceptual levels of semantic structure and semantic content are defined. Conceptual analysis at the level of semantic structure produces a definition of the scope of domain, and specifies the kinds of associations which can occur in the domain as a whole. At the level of semantic content, conceptual analysis supplies the meanings of terms in more detail, and provides a more fine-grained view of the tokens which can instantiate types. The ER approach is appropriate for modeling concepts at the level of semantic structure, and the CG formalism at the level of semantic content An example of concept modeling and text generation for a simple domain is presented to demonstrate how the ER constructs can be augmented by conceptual graphs. The conceptual analysis for a household survey is developed at both the level of semantic structure and of semantic content. A questionnaire for a household survey is generated from the conceptual analysis, and reflects both the semantic structure of the domain (in the overall outline and layout of the questionnaire), and its semantic content (in the wording of individual questionnaire items). The paper concludes with a discussion of the advantages of simultaneously employing both the ER and CG techniques for modeling the semantics of a domain

The authors present a portion of the EARL semantic data model, an augmentation of the entity-relationship model, which uses notational constructs based on propositional logic to allow a greater selection of semantic integrity constraints to be captured during the specification and design of a domain schema. The use of these logic-based constructs is shown to allow more subtle constraints to be specified, so that ambiguity can be reduced when placing constraints on the universe of discourse for database or knowledge-base applications. The notion of what are semantic constraints is discussed, and notational constructs with informal semantics are defined for the EARL model. Several examples are presented.>

An approach is described for enhancing an integrated relational data dictionary in order to capture and faithfully present this high-level enterprise schema in a form which can be incorporated directly into the relational database system. The approach, referred to as EDICT, allows for the specification of the enterprise schema for a database as an extension of the data dictionary by storing the schema as a set of normalized tables. In addition, it facilitates more effective use of existing data dictionaries by providing an extended metaschema which describes the structure and use of the dictionary itself.>

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns, as well as user interests and preferences during system usage, it customizes document retrievals for each user. A two-layer learning system has been implemented for MINDS. The knowledge base used by the query engine is learned at the lower level with the help of heuristics for assigning credit and recommending adjustments; these heuristics are incrementally refined at the upper level. © 1986 John Wiley & Sons, Inc.

This paper presents a novel arithmetic scheme for a high-speed adaptive learning network (ALN) element. An ALN is a self-organizing scheme for implementing the Kolmogorov-Gabor (K-G) polynomial which maps an input vector X into an output scalar Y. In the first layer of an ALN there are n(n-l) / 2 elements. In the next layer the number of elements needed depends upon the number of outputs that are propagated from the first layer. In this paper only the design of a single element is considered. An array of memories (RAMs) and a parallel adder are used to perform multinomial arithmetic for the element. The memory array contains subfunction values which are calculated by an external host computer and downloaded to the memory array. All the memories operate on the input variables concurrently via a common address bus. The subfunction values from the memory array are then summed by a parallel adder to obtain the output of the element. A complete ALN implemented with the proposed ALN elements has advantages in operation speed and less hardware.