Natural Language Query Processing for Model Management

The communication between an MIS and 1ts users would be greatly facilitated if the users could query and instruct the system in a sufficiently large subset of their natural language that the system appears to be conversing in the\u27 1 anguage. In large measure this has been accomplished for MISs that retrieve and display stored data and that perform simple calcul ations (summations, plots, regressions) with the data. A number of natural 1 anguage database query sys-· terns have been developed, a few of which are now commerci ally available. However, little attention has been , pald to the development of natural language interfaces for systems containing decision models. This paper examines the issues that may arise in the development of natural language , query processors for model management systems. In this paper we address four topics. The first ls the state-of-the-art in natural language database query processing. The principal issues here are the parsing of sentences and the resolution of ambiguities. The ambiguities may be those internal to a sentence (such as misspelllngs, ambiguities in the meanings of words, and ambiguities inherent in the syntax of the language in which the query is written), ambiguities resulting from explicit or implicit reference to previous queries (such as the use of pronouns whose referents must be identified), and ambiguities that arise when several flles must be combined to respond to a single query. These issues have been examined in detail and may provide a foundation for natural language model query processing. The second issue is the development of a high-level target language -- a well-structured, user-friendly, machineindependent language into which natural language queries will be translated prior to model execution. A target language for model management, called Mal (Model Query Language), has been designed, and its linguistic properties have been investigated. The language is described, and some exampl es are given. The thi rd issue is the structure of the model query transl ator. The transl ator w111 consist of five components. The Parsing Component receives the query from the user and analyzes it. It identifies the functions to be performed (e.g., optimization, sensitivity analysis), identifies the inputs and outputs of the models to be used, and attempts to resolve ambiguities. The Model Definition Component is used by the model buil der to define the inpu© and outputs of the model s l n the model bank. The Memory r_Aponent contai ns the model definitions, any previous queries (ln case reference, such as pronoun reference, is made to them), and information about possibl e spel 1 ing errors and synonyms. The Model Processing Component executes the model or model s needed to prepare a response, and the Report Writing Component formats the response. The final issue is the possible integration of data management and model management in a way that allows users to enter a natural 1 anguage query that requires access to both databases and model banks. Such an integration may eventual ly 1 ead to the devel opment of systems that provide a comprehensive range of decision support services.

EXPERT SYSTEMS AS AN ORGANIZATIONAL PARADIGM

During the past 75 years a number of paradigms have been put forward to explain the behavior of organizations. These include economic, behavioral, and information processing paradigms. We introduce here an extension of the latter. a paradigm drawn from the knowledge representation and processing procedures used in artificial intelligence

Metagraphs and their applications

The graph is a critical and useful concept in designing many information processing systems. Systems such as transaction processing systems, decision support systems, and workflow systems are all helped immensely by a graphical structure. Simple graphs and digraphs allow for the construction of a variety of system design tools that provide a convenient and appealing format for illustrating information infrastructures, while allowing any subsequent analyses to be performed by the user. However, the metagraph, a new graphical structure that is developed in this book, goes beyond the representational and provides Information Systems with a robust, analytical modeling graphic tool. METAGRAPHS AND THEIR APPLICATIONS is a presentation of metagraph theory and its applications that begins by defining a metagraph and its uses. They are more complex than a simple graph structure, but they allow for representation and analysis of more complex systems. The material contained in this book is presented in two parts. The first develops the theoretical results with the emphasis on the development of a metagraph algebra. In the second part of the book, four promising applications of metagraphs are examined: 1) modeling of data relations, 2) the modeling of decision models, 3) the modeling of decision rules, and 4) the modeling of workflow tasks. Hence, the theoretical results in the initial chapters lay the foundation for the application areas in the second part of the book. The book concludes by examining several possible extensions of this work. Of special interest is the structuring of the metagraphs modeling process, which may enhance the body of work on systems analysis and design (including software engineering), the development of a metagraphs workbench to support such a process, and the possible application of the results presented here, suitably enhanced, to social networks

Metagraphs in Hierarchical Modeling

When using a decision support system (DSS) containing a large model base, a user has to decide which specific models are relevant for any particular task. When the model base is large and diverse, this task can be quite difficult. Furthermore, the number and variety of models available can be confusing. One way to simplify the user's interaction with the DSS, and facilitate more effective use of the system, is through the use of "views" of the model base. A view is an abstraction of a model base that limits attention to relationships between certain variables, and its purpose is to guide the development of DSS software that executes lower-level models in response to higher-level requests for information or analysis. Single views may be of use to individual decision makers who wish to abstract information from a model base, and multiple views may be of use to collections of decision makers who are using a model base to integrate their analyses to make a collective decision. In this paper, we show how representation of models using a graph theoretic structure called a metagraph can facilitate the construction and maintenance of model base views. In particular, we show how useful views can be constructed using a projection operation on a metagraph, and also present conditions under which views can be combined. The concepts developed in the paper are illustrated using an example from life cycle costing.model abstraction, hierarchical modeling, decision support systems, graph theory