Domain Terminology Research Articles

Management of heterogeneous networks with intelligent agents

Even as statements about the challenges facing managers of heterogeneous networks seem like cliché, scholars keep giving the emerging problems increasing attention. The unpredictable nature and dynamism of networks require that new means be explored to address the consequent nondeterministic behaviors. The studies conducted thus far in our ongoing work on a Lucent-grown agent platform, the Lucent Intelligent Network Agent (LucINA), and its applications indicate that intelligent agents constitute an attractive technology to address these issues. Agents communicate at a high level of abstraction using an agent communication language (ACL). The language provides a universal wrapper for specific content and a capability to negotiate communication means including language, protocol, and domain terminology (ontology) — a characteristic very desirable in heterogeneous, multivendor environments.

The use of domain knowledge in program understanding

Program understanding is an essential part of all software maintenance and enhancement activities. As currently practiced, program understanding consists mainly of code reading. The few automated understanding tools that are actually used in industry provide helpful but relatively shallow information, such as the line numbers on which variable names occur or the calling structure possible among system components. These tools rely on analyses driven by the nature of the programming language used. As such, they are adequate to answer questions concerning implementation details, so called what questions. They are severely limited, however, when trying to relate a system to its purpose or requirements, the why questions. Application programs solve realdworld problems. The part of the world with which a particular application is concerned is that application’s domain. A model of an application’s domain can serve as a supplement to programmingdlanguagedbased analysis methods and tools. A domain model carries knowledge of domain boundaries, terminology, and possible architectures. This knowledge can help an analyst set expectations for program content. Moreover, a domain model can provide information on how domain concepts are related. This article discusses the role of domain knowledge in program understanding. It presents a method by which domain models, together with the results of programmingdlanguagedbased analyses, can be used to answers both what and why questions. Representing the results of domaindbased program understanding is also important, and a variety of representation techniques are discussed. Although domaindbased understanding can be performed manually, automated tool support can guide discovery, reduce effort, improve consistency, and provide a repository of knowledge useful for downstream activities such as documentation, reengineering, and reuse. A tools framework for domaindbased program understanding, a dowser, is presented in which a variety of tools work together to make use of domain information to facilitate understanding. Experience with domaindbased program understanding methods and tools is presented in the form of a collection of case studies. After the case studies are described, our work on domaindbased program understanding is compared with that of other researchers working in this area. The paper concludes with a discussion of the issues raised by domaindbased understanding and directions for future work.

Data mining applied to in vitro fertilization, tumor and diabetic patient data

We currently develop a technology to combine knowledge acquisition from domain experts and data mining thus allowing an easy and focused approach to intelligent knowledge discovery. By enabling the domain expert to enter data mining queries in his/her domain terminology and by returning findings in the same terms as well as measuring the interestingness of findings, user support is provided throughout the knowledge discovery process. In the biomedical area a project on the discovery of interesting dependencies in patient data for the purpose of quality management has been carried out within several clinics. In this sector, data mining precedes confirmative analyses by generating first hypotheses. In addition to that, a number of studies concerning the analysis of medical data, e.g. in the fields of in vitro fertilization, classification of tumors, fusion of sensor data, and diabetic patient profiling have been worked on with local university hospitals. By fusing complex medical data from various Sources and analyzing them, new discoveries, e.g. decision trees for tumors, could be made.

Moral Descriptors and the Assessment of Children

In the world outside schools, the public clamours for more character education; inside schools, psychologists, responsible for evaluating children, neither assess the moral domain nor use moral terminology to describe children. There are powerful reasons to resist the reintroduction of moral language into psychological assessments: such inclusion could promote invidious labelling, burden children with shame and guilt and open the diagnostic system to increased subjectivity, capriciousness and stigmatization‐‐the substitution of “illness” for what is mere social and cultural difference. Despite these serious objections, it is arguable that the moral domain should be included in our psychological appraisals of children, and that the absence of moral dictates in a child could even be considered a “mental disorder”. If children are endowed with a native moral propensity (as they are endowed with a native attachment propensity), and if that propensity is inadequately nurtured, then the consequent deprivation may compromise healthy mental development. Although officially extending the existing psychiatric diagnostic nomenclature may be an overreaction to the current state of silence, the individual and social yield from including moral descriptors in child evaluations should not be dismissed lightly. These benefits are discussed.

Acquisition and validation of software requirements

This paper presents a knowledge-based software engineering tool, DASERT (Detection of Anomalies in Software Engineering Requirements Texts), to acquire and validate functional requirements in natural language. The user describes the functional specifications through informal methods, using graphics with comments in natural language. During this elaboration step the system validates the document by processing the comments semantically to detect ambiguities or inconsistencies. To do so it uses natural language processing and knowledge base engineering.DASERT's kernel is a KL-ONE-like semantic network, which helps the semantic parsing of the comments and their semantic representation. This knowledge base is first initialized by the acquisition of the lexical domain knowledge, then progressively enriched with the domain terminology given by the user and with the requirements knowledge extracted from the user's graphics and texts.During initialization and enrichment, the network manager validates the knowledge structurally. This ensures the logical consistency of the base which is then checked for inconsistencies and ambiguities specific to the domain of software requirements.From a software engineering point of view, the originality of DASERT is that it provides a semantic checking of an informal specification by interpreting the natural language comments. From a knowledge acquisition point of view, DASERT allows acquisition from texts to build the kernel of a knowledge base which is then used to guide the semantic parsing of texts during the acquisition of the specification itself. Moreover, the representation formalism provides a unified view of acquisition and validation.

The BACK system—an overview

The BACK project was begun at the Technical University Berlin in January 1985 as part of a larger project within the ESPRIT programme 1 . Our task in the project group was the specification, design and implementation of a knowledge representation system which we called BACK ("Berlin Advanced Computational Knowledge Representation System"). It is a based on a terminological logic (term description language) and supports complex representation of a domain terminology, description of domain objects using that terminology, and database access via a uniform interface language.

Role of Breast Cancer Organizations and an Informed Public in Chemoprevention

AbstractThe year 1987 has been marked by many things besides the Iran Scam hearings, First Family surgery, and the stock market crash. There has been a tremendous information explosion in all fields, especially in the field of health and specifically preventive medicine. One no longer asks one's doctor what to eat to prevent diseases because one can read about it in Wednesday's food section, Sunday's magazine, or in womens' magazines, or you can send for or pick up an NCI or an ACS booklet in any doctor's office. Terms like saturated fats, dietary fibers, HDLs, omega-3 fatty acids, and selenium, are public domain terminology, reflecting the fest-moving changes in all areas of medicine and the public's hunger to be informed. This is the yield of consumerism for which the American appetite is endless.

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem‐solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.