Julia E. Hodges

Extraction of object-oriented structures from existing relational databases

Due to the wide use of object-oriented technology in software development and the existence of many relational databases, reverse engineering of relational schemas to object-oriented schemas is gaining in interest. One of the major problems with existing approaches for this schema mapping is that they fail to take into consideration many modern relational database design alternatives (e.g., use of binary data to store multiple-valued attributes). This paper presents a schema mapping procedure that can be applied on existing relational databases without changing their schema. The procedure maps a relational schema that is at least in 2NF into an object-oriented schema by taking into consideration various types of relational database design optimizations.

Increased Retention of Early Computer Science and Software Engineering Students Using Pair Programming

An important problem faced by many Computer Science and Software Engineering programs is declining enrollment. In an effort to reverse that trend at Mississippi State University, we have instituted pair programming for the laboratory exercises in the introductory programming course. This paper describes a study performed to analyze whether using pair programming would increase retention. An important goal of this study was not only to measure increased retention, but to provide insight into why retention increased or decreased. The results of the study showed that retention significantly increased for those students already majoring in Computer Science, Software Engineering, or Computer Engineering. In addition, survey results indicated that the students viewed many aspects of pair programming to be very beneficial to their learning experience.

Document Clustering with Semantic Analysis

Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. In the traditional vector space model, the unique words occurring in the document set are used as the features. But because of the synonym problem and the polysemous problem, such a bag of original words cannot represent the content of a document precisely. In this paper, we investigate using the sense disambiguation method to identify the sense of words to construct the feature vector for document representation. Our experimental results demonstrate that in most conditions, using sense can improve the performance of our document clustering system. But the comprehensive statistical analysis performed indicates that the differences between using original single words and using senses of words are not statistically significant. In this paper, we also provide an evaluation of several basic clustering algorithms for algorithm selection.

An automated system that assists in the generation of document indexes

In this article, we describe AIMS (Assisted Indexing at Mississippi State), a system intended to aid human document analysts in the assignment of indexes to physical chemistry journal articles. The two major components of AIMS are a natural language processing (NLP) component and an index generation (IG) component. We provide an overview of what each of these components does and how it works. We also present the results of a recent evaluation of our system in terms of recall and precision. The recall rate is the proportion of the ‘correct’ indexes (i.e. those produced by human document analysts) generated by AIMS. The precision rate is the proportion of the generated indexes that is correct. Finally, we describe some of the future work planned for this project.

The development of an expert system for the characterization of containers of contaminated waste

Abstract Scientists at the Mississippi State University Diagnostic Instrumentation and Analysis Laboratory and the Idaho National Engineering and Environmental Laboratory are developing an expert system to aid in the determination of the proper disposition of containers of transuranic and low-level-alpha-contaminated waste generated as a byproduct of Department of Energy defense-related programs. This system will consider a variety of information such as real-time radiography and the data from both passive and active neutron assay systems to classify the containers into one of the two categories—those that meet the requirements for being shipped to the Waste Isolation Pilot Plant in New Mexico and those that do not. We describe the development of a prototype of the expert system. We also describe the approach used to represent and reason with information from a variety of sources as well as the uncertainty that is inherent in such information. We discuss the strengths and weaknesses of the various artificial intelligence techniques that were considered for use in the expert system.

Automated knowledge derivation: Domain-independent techniques for domain-restricted text sources

This article provides a description of the major components of a system that builds and updates a knowledge base by extracting the knowledge from natural language text. the knowledge extraction is done in a domain‐independent manner and does not rely on particular vocabulary or grammar constructions. the only restriction is that the input text must be technical text from some specific problem domain. an important capability of the system is that it can bootstrap itself. That is, beginning with only a description of the types of object and relationships to be stored in the knowledge base, the system can start with an empty knowledge base and build the knowledge base as it processes the text. the knowledge extraction systems success in extracting knowledge from various input texts was evaluated using scoring metrics reported by Lehnert and Sundheim [AI Mag., 12(3), 81–94 (1991)]. the initial results indicate that the knowledge extraction mechanism is both effective and independent of a particular authors writing style or a particular domain.

Automatically Building a Knowledge Base Through Natural Language Text Analysis

The knowledge representation and acquisition system described in this article is one of two major components in a project intended to provide expert systems with the ability to build and update their own knowledge bases by processing natural language technical material that is in machine‐readable form. the particular aspect of the system described in this article is the building of an initial knowledge base using only syntactic structures (which include semantic information about domain‐dependent object classes) provided by the natural language processing component and a schema‐like description of the knowledge base. Descriptions of the knowledge structures that make up the knowledge base and the approach used to extract the appropriate information from the syntactic structures are provided. the approach taken during the development of the knowledge representation and acquisition component was motivated by two key factors: keeping the algorithms as general (i.e., domain independent) as possible, and making the process as fully automated as possible. the little human intervention that has been used has been limited to certain stages of the natural language processing.

Knowledge Discovery in an Oceanographic Database

Knowledge discovery from image data is a multi-step iterative process. This paper describes the procedure we have used to develop a knowledge discovery system that classifies regions of the ocean floor based on textural features extracted from acoustic imagery. The image is subdivided into rectangular cells called texture elements (texels); a gray-level co-occurence matrix (GLCM) is computed for each texel in four directions. Secondary texture features are then computed from the GLCM resulting in a feature vector representation of each texel instance. Alternatively, a region-growing approach is used to identify irregularly shaped regions of varying size which have a homogenous texture and for which the texture features are computed. The Bayesian classifier Autoclass is used to cluster the instances. Feature extraction is one of the major tasks in knowledge discovery from images. The initial goal of this research was to identify regions of the image characterized by sand waves. Experiments were designed to use expert judgements to select the most effective set of features, to identify the best texel size, and to determine the number of meaningful classes in the data. The region-growing approach has proven to be more successful than the texel-based approach. This method provides a fast and accurate method for identifying provinces in the ocean floor of interest to geologists.

Generation and Evaluation of Indexes for Chemistry Articles

This paper describes AIMS (Assisted Indexing atMississippi State), a system that aids human document analystsin the assignment of indexes to physical chemistry journalarticles. There are two major components of AIMS—a naturallanguage processing (NLP) component and an index generation (IG)component. The focus of this article is the IG. We describethe techniques and structures used by the IG in the selection ofappropriate indexes for a given article. We also describe theresults of evaluations of the system in terms of recall,precision, and overgeneration. We provide a description of agraphical user interface that we have developed for AIMS.Finally, we discuss future work.

The automatic initialization of an object-oriented knowledge base

This paper describes mechanisms for automatically building an initial set of knowledge base objects by processing natural language text in a restricted domain. A separate component assigns syntactic and domain-specific semantic tags to each of the words in the text. The syntactic and semantic tags are used to generate names of objects to be added to specific semantic classes in the knowledge base. An algorithm to ensure that the knowledge base objects created are not redundant is specified. The definition and organization of metaknowledge structures is also discussed. To ensure that the name generation and knowledge management techniques are domain-independent, any domain-specific information is isolated in a knowledge base definition file.