The crustal dynamics intelligent user interface anthology

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has, as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI is to develop a friendly and intelligent user interface service based on expert systems and natural language processing technologies. The purpose of such a service is to support the large number of potential scientific and engineering users that have need of space and land-related research and technical data, but have little or no experience in query languages or understanding of the information content or architecture of the databases of interest. This document presents the design concepts, development approach and evaluation of the performance of a prototype IUI system for the Crustal Dynamics Project Database, which was developed using a microcomputer-based expert system tool (M. 1), the natural language query processor THEMIS, and the graphics software system GSS. The IUI design is based on a multiple view representation of a database from both the user and database perspective, with intelligent processes to translate between the views

Natural Language for Database Queries: A Laboratory Study

Technical feasibility and promise of practical use for querying databases in natural 1 anguage (for example, English) has been demonstrated by a large number of experimental systems, and the commercial availability of at 1 east one such system. Yet natural 1 anguage continues to be the most controversi al among the 1 anguage interfaces that have been proposed for direct interaction with databases. Most Natural Language Query Systems (NLGS) have focused on a certal n cl ass of users - appl icati on speci al 1 sts not requi red to possess technical skills - and have emphasized easy transportability to a variety of application domains. Based on these principles, and considering the limitations of state-of-the-art natural language processing, these NLGS have adopted particul ar design structures and goal s. Are these query systems meeti ng thei r design goal s? More importantly, are these the appropriate goals? These seem to be the major questions for which no concl usive answers have yet been given. Most experimental research in the area has addressed the first question. Fiel d studies alone are often hampered by implementation limitations, and of course, by the lack of a controlled environment. Thus, a negative answer to the first question, as is usually the case with prototype systems, makes the determination of an answer for the second question very difficult. A recently compl eted study at New York University constitutes a step toward resolving some of the issues pertaining to the use of natural language for database queries. The overall approach involves a. combi nation of expl oratory field evaluations with controlled laboratory studies to examine these issues by comparing performance between subjects using the formal database language SaL and subjects using a prototype natural language query system (NLaS) developed in the IBM Heidelberg Scientific Center. This paper describes in detail a laboratory study which was conducted as part of the project. In the 1 aboratory study, paid subjects were trai ned in the appl ication and the respective languages (SGL and NLOS) and then given an exam

Towards Informing an Intuitive Mission Planning Interface for Autonomous Multi-Asset Teams via Image Descriptions

Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Human-Machine Interface (HMI) team is working to capture and utilize the multitude of ways in which humans are already comfortable communicating mission goals and translate that into an intuitive mission planning interface. Several input/output modalities (speech/audio, typing/text, touch, and gesture) are being considered and investigated in the context human-machine teaming for the ATTRACTOR design reference mission (DRM) of Search and Rescue or (more generally) intelligence, surveillance, and reconnaissance (ISR). The first of these investigations, the Human Informed Natural-language GANs Evaluation (HINGE) data collection effort, is aimed at building an image description database to train a Generative Adversarial Network (GAN). In addition to building an image description database, the HMI team was interested if, and how, modality (spoken vs. written) affects different aspects of the image description given. The results will be analyzed to better inform the designing of an interface for mission planning

Conceptual Modeling: the Linguistic Approach

After more than thirty years of its first introduction, conceptual modeling remains an important research field, which has been recently addressed by the literature on semantic interoperability in its various forms (model integration, service interoperability, knowledge harmonization, taxonomy alignment), domain engineering and the creation of conceptual models through Natural Language Processing (NLP), to name a few. In the database conceptual design, the designer must learn the language used in the Universe of Discourse (UoD) to be modeled, along with its underlying concepts, and then represent such concepts in a modeling language. Thus, the conceptual modeling process can be seen as a translation. For the resulting model to be both detailed and unambiguous, the designer must represent the UoD in a generative language which constructs can convey the same concepts represented in the respective natural language. For the whole process to be effective, we argue that the adoption of modeling languages and methodologies that are based on well-founded ontological theories is required. We propose the use of a linguistic approach for conceptual modeling from natural language texts, and illustrate how it may be applied using the well-founded modeling language OntoUML

An Automatic Dialog System for Student Advising

Automatic dialog systems are an implementation of natural language processing theory with the goal of allowing the use of natural sentences to communicate with a computer system. The general purpose of this project was to design and implement an automatic dialog system for augmenting university student advising. Student advising is a relatively narrow domain of possible questions and responses. The automatic dialog system focused on prescriptive advising rather than developmental advising to further narrow the domain to scheduling and registration matters. A student advisor was interviewed and recorded during a mock advising session in order to model the interaction between students and their advisors. The phrases and advising information have been encoded using Artificial Intelligence Markup Language (AIML) and the dialog system has been implemented in the programming language Python. Future work includes expanding the database to include information directly from the Minnesota State University, Mankato student registration system as well as to implement a spoken language interface

Semantic Search

The thesis describes a Semantic approach towards web search through a stand-alone Java application. An Ontology Web Language(OWL) model is used to build a knowledge database related to different types of Organisms. The goal is to guide the Google web search engine using this OWL model. In the rst approach towards Semantic web search, an inference engine called CLIPS is used and in the second aproach, the Protege-OWL API is used. The thesis goes in detail about the design, working and comparison of these two approaches. The thesis also deals with design approach for enhancement of the OWL model, once the Semantic web search is done through the Protege-OWL API. This is achieved using Natural Language Processing and Parsing technique. Examples and results of the search and enhancement part of the application are described in detail. Future research directions are indicated

Semantic Matching Using Ontology in Multilingual Environment

The tremendous increase in usage of data over the past few years and the ease of availability of things across the globe any time lead to the Advancement of multilingual database. Storage, retrieval and archiving of data for multi lingual system has been a challenge. Our research project “Semantic Matching using Ontology in Multilingual Environment” is an extension is to look into addressing multi-lingual data. The report focuses on providing design and implementation of multilingual system. These comprise of two main components being (i) Cross Lingual Information Retrieval, and (ii) Indian Language to Indian Language Machine Translation. Consider the context of large scale natural language processing applications in the areas of Cross Lingual IR and Machine Translation, wherein such a model for multilingual dictionary is established. When contrasted to traditional single lingual or bilingual dictionary, the model uses the core concept of Synonym Groupings (synsets) that is used as a way to connect different languages in a crisp and efficient manner