GATOR: Requirements capturing of telephony features

We are developing a natural language-based, requirements gathering system called GATOR (for the GATherer Of Requirements). GATOR assists in the development of more accurate and complete specifications of new telephony features. GATOR interacts with a feature designer who describes a new feature, set of features, or capability to be implemented. The system aids this individual in the specification process by asking for clarifications when potential ambiguities are present, by identifying potential conflicts with other existing features, and by presenting its understanding of the feature to the designer. Through user interaction with a model of the existing telephony feature set, GATOR constructs a formal representation of the new, 'to be implemented' feature. Ultimately GATOR will produce a requirements document and will maintain an internal representation of this feature to aid in future design and specification. This paper consists of three sections that describe (1) the structure of GATOR, (2) POND, GATOR's internal knowledge representation language, and (3) current research issues

A Knowledge-based Clinical Toxicology Consultant for Diagnosing Multiple Exposures

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances. Methods: The system is automatically trained using data mining techniques to extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center (FPIC). When supplied with observed clinical effects, the system produces a ranked list of the most plausible toxic exposures. During testing, the system diagnosed toxins at three levels: identifying the substance, identifying the toxin’s major and minor categories, and identifying the toxin’s major category alone. To enable comparison between these three levels, accuracy was calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses. Results: System evaluation utilized a dataset of 8,901 multiple exposure cases and 37,617 single exposure cases. Initial system testing using only multiple exposure cases yielded poor results, with diagnosis accuracies ranging from 18.5-50.1%. Further investigation revealed that the system’s inability to diagnose multiple disorders resulted from insufficient data and that the clinical effects observed in multiple exposures are dominated by a single substance. Including single exposures when training, the system achieved accuracies as high as 83.5% when 2 diagnosing the primary contributors in multiple exposure cases by substance, 86.9% when diagnosing by major and minor categories, and 79.9% when diagnosing by major category alone. Conclusions: Although the system failed to completely diagnose exposures to multiple toxins, the ability to identify the primary contributor in such cases may prove valuable in aiding medical personnel as they seek to diagnose and treat patients. As time passes and more cases are added to the FPIC database, we believe system accuracy will continue to improve, producing a viable decision support system for clinical toxicology

A Knowledge-based Clinical Toxicology Consultant for Diagnosing Single Exposures

Objective: Every year, toxic exposures kill twelve hundred Americans. To aid in the timely diagnosis and treatment of such exposures, this research investigates the feasibility of a knowledge-based system capable of generating differential diagnoses for human exposures involving unknown toxins. Methods: Data mining techniques automatically extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center. Using observed clinical effects, the trained system produces a ranked list of plausible toxic exposures. The resulting system was evaluated using 30,152 single exposure cases. In addition, the effects of two filters for refining diagnosis based on a minimum number of exposure cases and a minimum number of clinical effects were also explored. Results: The system achieved accuracies (calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses) as high as 79.8% when diagnosing by substance and 78.9% when diagnosing by the major and minor categories of toxins. Conclusions: The results of this research are modest, yet promising. At this time, no similar systems are currently in use in the United States and it is hoped that these studies will yield an effective medical decision support system for clinical toxicology

Rule Discovery using Patterns from Joined Table of Relational Databases

Several problems exist for data mining in real world databases: the difficulty of determining a decision attribute when limited domain knowledge exists, the difficulty in selecting a decision attribute from a new table formed from joining several relations, and the problem of elaborate data selection in the knowledge discovery process. This paper presents algorithms to solve these problems using methods that 1) determine a good decision attribute based on an approach developed from rough set theory and decision tree generation and 2) find meaningful frequent patterns based on attributes and dependencies in relational databases which are used to cluster values and generate tables. Moreover, our methods take advantage of the fact that more general concepts occur more frequently, making stepwise refinement possible. 1 Introduction Prediction and description are two distinct and different goals for data mining. To achieve these goals involves the following principal data mining tasks [4]:..

POND: A Knowledge Representation Language which Facilates Requirements Capturing

POND, a KL-ONE based knowledge representation language, provides the underlying structure for GATOR, a system used to gather requirements of telephony features. POND differs from a standard KL-ONE language by dividing knowledge into three levels: a Concept Level, an Instance Level, and a Model Level. The Concept and Instance Levels correspond, respectively, to KL-ONE's TBox and ABox. The Model Level represents specialized ABox knowledge (simulations of combining instances). This paper details the types of knowledge represented within each of these levels and how knowledge defined on the higher levels influences the knowledge structure of the lower levels. These details are provided by specific examples from the GATOR system's knowledge representation. AREA: Technology AI TOPICS: Knowledge Representation, KLONE Languages DOMAIN: Software Engineering -- Requirements Capturing LANGUAGE: Lisp STATUS: Under research development EFFORT: 1/2 person-years POND: A Knowledge Representation Lan..

An Architecture for Defining Features and Exploring Interactions

. The last decade has seen an explosive growth in the development of telephony features. The description and design of new features are fraught with errors due to this growth's impact on our ability to recognize interactions and the current practice of describing a feature's requirements using natural language. While the use of natural language eases the communication of requirements between the designer, customer, and developer, it introduces the potentially fatal flaw of ambiguity. Additionally, these requirements documents are rarely updated to reflect interactions with newly developed features. This paper presents an overview of a natural language-based system currently in development that converts English-based telephony requirements into a knowledge-based representation. The goals of this conversion are to create an unambiguous understanding of the requirements of the described telephony feature, to create less ambiguous written requirements documents, to automatically update exi..