Showing papers on "Domain knowledge published in 1994"

Method for extracting knowledge from online documentation and creating a glossary, index, help database or the like

Abstract: A method involving computer-mediated linguistic analysis of online technical documentation to extract and catalog from the documentation knowledge essential to, for example, creating a online help database useful in providing online assistance to users in performing a task. The method comprises stripping markup tags from the documentation, linguistically analyzing and annotating the text, including the steps of morphologically and lexically analyzing the text, disambiguating between possible parts-of-speech for each word, and syntactically analyzing and labeling each word. The method further comprises the steps of combining the linguistically analyzed, annotated, and labeled text and previously stripped markup information into a merged file, mining the merged file for domain knowledge, including the steps of identifying and creating a list of technical terminology, mining the merged file for manifestations of domain primitives and maintaining a list of manifestations of such domain primitives in an observations file, analyzing the discourse context of each sentence or phrase in the merged file, analyzing the frequency of manifestations of domain primitives in the observations file to determine those that are important, expanding the list of key terms by searching for terms sanctioned by a domain primitive deemed important in the previous step, and searching the merged file for larger relations by searching for particular lexico-syntactic patterns involving key terms and manifestations of domain primitives previously identified. The method further comprises the steps of structuring the knowledge thus mined and building a domain catalog.

Reward functions for accelerated learning

Abstract: This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. The methodology involves the use of heterogeneous reinforcement functions and progress estimators, and applies to learning in domains with a single agent or with multiple agents. The methodology is experimentally validated on a group of mobile robots learning a foraging task.

Amalgamating knowledge bases

Abstract: The integration of knowledge for multiple sources is an important aspect of automated reasoning systems. When different knowledge bases are used to store knowledge provided by multiple sources, we are faced with the problem of integrating multiple knowledge bases: Under these circumstances, we are also confronted with the prospect of inconsistency. In this paper we present a uniform theoretical framework, based on annotated logics, for amalgamating multiple knowledge bases when these knowledge bases (possibly) contain inconsistencies, uncertainties, and nonmonotonic modes of negation. We show that annotated logics may be used, with some modifications, to mediate between different knowledge bases. The multiple knowledge bases are amalgamated by a transformation of the individual knowledge bases into new annotated logic programs, together with the addition of a new axiom scheme. We characterize the declarative semantics of such amalgamated knowledge bases and study how the semantics of the amalgam is related to the semantics of the individual knowledge bases being combined.—Author's Abstract

How Subject-Matter Knowledge Affects Recall and Interest

Abstract: This research examined the influence of subject-matter knowledge on students’ recall of and interest in scientific exposition. Two forms of subject-matter knowledge were assessed: topic knowledge (i.e., specific subject-matter knowledge referenced in text) and domain knowledge (i.e., knowledge pertinent to a particular field of study). Two hundred and nine college students read two popular-press passages from the domain of physics. Tests of topic knowledge and domain knowledge were administered to students prior to reading the passages. During reading, students rated how interesting they thought each passage and each of its paragraphs were. After reading, students completed a recall measure. Regression analyses showed that subject-matter knowledge, particularly domain knowledge, predicted both recall and interest. Findings tend to support a three-stage model of domain learning that proposes an interactive picture of student knowledge, recall, and interest. Implications for research and practice are discussed.

Automatic parsing of news video

Abstract: Video content parsing is possible when one has an a priori model of a video's structure based on domain knowledge. This paper presents work on using domain knowledge to parse content of news video programs. Approaches to locating and identifying frame structure models based on temporal and spatial structure of news video data, along with algorithms to apply these models in parsing news video, have been developed and are presented in detail in this paper. Experimental results are also discussed in detail to evaluate the approaches and algorithms. Finally, proposals for future work are summarized. >

Mapping domains to methods in support of reuse

Abstract: In this paper, we characterize the relationship between abstract problem-solving methods and the domain-oriented knowledge bases that they use. We argue that, to reuse methods and knowledge bases, we must isolate, as much as possible, method knowledge from domain knowledge. To connect methods and domains, we define declarative mapping relations, and enumerate the classes of mappings. We illustrate our approach to reuse with the PROTEGE-II architecture and a pair of configuration tasks. Our goal is to show that the use of mapping relations leads to reuse with high payoff of saved effort.

Knowledge dissemination and use in science and mathematics education: A literature review

Abstract: A synthesis of the literature on knowledge dissemination and use in education, notably in science and mathematics, is presented. Perspectives have changed in the ways in which knowledge and products are seen to reach potential users. From the top-down, linear models, we have come closer to bottom-up approaches and to the crucial role of linking agents. At present, the most influential approach is a "constructivist" one, whereby research and other kinds of specialized knowledge is exchanged between researchers and professionals in a mutually constructed social context. While there is still debate over the best predictors of successful knowledge use, the scope of the field has been considerably enlarged by including users' perspectives. To some extent then, specialists in this field are now working in a new paradigm.

MERLIN: supporting cooperation in software development through a knowledge-based environment

Abstract: Merlin2 is a prototype Process-centred Software Development Environment (PSDE), developed within the context of the Merlin project carried out at University of Dortmund in cooperation with STZ, a Dortmund based software house This prototype uses a rule-based technique to describe and enact a software process Users of Merlin are either software developers or managers who are involved in a software process to produce a product A further kind of user or group of users respectively called the process engineer(s) are responsible for defining a particular process in terms of Merlin rules (or rather a dedicated process modeling language as will be explained later), ie they customize a Merlin PSDE to a particular software process or project The major benefit of using an environment like Merlin for software production is sophisticated team support, ie support for coordinating access to shared information on different levels of granularity (eg from more or less complete systems of modules or documents down to a procedure definition in the export list of a single module), and dedicated message servers for broadcasting information about project states, (urgent) tasks to do, and getting feedback of completed work packages, etc A further achievement of such an environment is the computer supported integration of development and management activities For example, project managers are able to retrieve on-line information about the current project state at any time and developers are immediately informed about any necessary actions to be taken or any constraints applying to executing activities This paper describes the main concepts behind the implementation of the Merlin prototype and sketches current and further work within the research project evolving from the current experience with the prototype

User modelling in the interactive anatomy tutoring system ANATOM-TUTOR

Abstract: This article is a comparative description of the user modelling component of ANATOM-TUTOR, an intelligent anatomy tutoring system for use at university level. We introduce ITSs in general, discussing some of the psychological and pedagogical issues involved in using computers in education, and ANATOM-TUTOR in parlicular, and locate ANATOM-TUTOR’s user modelling component in the field of existing user models. Details of the user model’s construction and maintenance, the knowledge representation techniques used in it, and its relation to the domain knowledge base are then discussed. Two applications of ANATOM-TUTOR’s user model are described: (1) tailoring hypertext to the level of knowledge of the individual user; and (2) generating explanations and questions in a simulated examination situation, also taking into consideration the individual user’s level of knowledge.

Coordinating distributed viewpoints - the anatomy of a consistency check

Abstract: Support for concurrent engineering must address the ''multiple perspectives problem''-many actors, many representation schemes, diverse domain knowledge, and differing development strategies, all in the context of distributed asynchronous development. Central to this problem is the issue of managing consistency between the various elements of an emerging design. In this paper we argue that striving to maintain complete consistency at all points in the development process is unnecessary, and an approach based on tolerance and management of inconsistency can be adopted instead. We present a scenario which highlights a number of important issues raised by this approach, and we describe how these issues are addressed in our framework of distributed ViewPoints. The approach allows an engineering team to develop independent ViewPoints, and to establish relationships between them incrementally. The framework provides mechanisms for expressing consistency relationships, checking that individual relationships hold, and resolving Inconsistencies if necessary.

Learning Planning Operators by Observation and Practice.

Abstract: The work described in this paper addresses learning planning operators by observing expert agents and subsequent knowledge refinement in a learning-by-doing paradigm. The observations of the expert agent consist of: 1) the sequence of actions being executed, 2) the state in which each action is executed, and 3) the state resulting from the execution of each action. Planning operators are learned from these observation sequences in an incremental fashion utilizing a conservative specific-to-general inductive generalization process. In order to refine the new operators to make them correct and complete, the system uses the new operators to solve practice problems, analyzing and learning from the execution traces of the resulting solutions or execution failures. We describe techniques for planning and plan repair with incorrect and incomplete domain knowledge, and for operator refinement through a process which integrates planning, execution, and plan repair. Our learning method is implemented on top of the PRODIGY architecture(Carbonell, Knoblock, & Minton 1990; Carbonell et al. 1992) and is demonstrated in the extended-strips domain(Minton 1988) and a subset the process planning domain(Gil 1991).

Resolving attribute incompatibility in database integration: an evidential reasoning approach

Abstract: Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel (1989) showed that uncertain information can be generated by the mapping of conflicting attributes to a common domain, based on some domain knowledge. The authors show that uncertain information can also arise when the database integration process requires information not directly represented in the component databases, but can be obtained through some summary of data. They therefore propose an extended relational model based on Dempster-Shafer theory of evidence (1976) to incorporate such uncertain knowledge about the source databases. They also develop a full set of extended relational operations over the extended relations. In particular, an extended union operation has been formalized to combine two extended relations using Dempster's rule of combination. The closure and boundedness properties of the proposed extended operations are formulated. >

Analogy‐Based Solution to Markup Estimation Problem

Abstract: This paper presents a methodology for deriving analogy‐based solutions to a class of unstructured problems in civil engineering. Such problems have identifiable characteristics, including: (1) Problems frequently require simultaneous assessment of a large number of quantitative as well as qualitative factors that influence the solution; (2) traditional algorithmic and reasoning‐intensive techniques are not adequate to model the problem; (3) solutions are devised in practice primarily based on analogy with previous cases coupled with a mixture of intuition and experience; and (4) domain knowledge is mostly implicit and very difficult to be extracted and described. For this class of problems, artificial neural networks (ANNs) are most suited for developing decision aids with analogy‐based problem‐solving capabilities. A methodology is presented and used to develop a practical model for markup estimation using knowledge acquired from contractors in Canada and the U.S. The model design, training, and testing ...

Knowledge-assisted content-based retrieval for multimedia databases

Abstract: In accordance with the progress of data management ability of computers, databases have become able to integrate various types of data in an application domain, and are now called multimedia databases. Unlike conventional databases managing only textual and numerical data, multimedia databases are required to evaluate properties of the data specified in a query. In this paper, a system which enables querying with the facility of acquiring the contents of multiple types of data is presented. Domain Knowledge accommodated in the system gives the information on how the system views the target multimedia data for content-based retrieval attaining application-independency as well as media-independency. >

PREPARE: a tool for knowledge base verification

Abstract: The knowledge base is the most important component in a knowledge-based system. Because a knowledge base is often built in an incremental, piecemeal fashion, potential errors may be inadvertently brought into it. One of the critical issues in developing reliable knowledge-based systems is how to verify the correctness of a knowledge base. The paper describes an automated tool called PREPARE for detecting potential errors in a knowledge base. PREPARE is based on modeling a knowledge base by using a predicate/transition net representation. Inconsistent, redundant, subsumed, circular, and incomplete rules in a knowledge base are then defined as patterns of the predicate/transition net model, and are detected through a syntactic pattern recognition method. The research results to date have indicated that: the methodology ran be adopted in knowledge-based systems where logic is used as knowledge representation formalism; the tool can be invoked at any stage of the system's development, even without a fully functioning inference engine; the predicate/transition net model of knowledge bases is easy to implement and provides a clear and understandable display of the knowledge to be used by the system. >

Machine Learning Methods for Planning

Abstract: From the Publisher: Research on planning systems has shown that domain knowledge is crucial for effectively coping with complex, changing environments. Unfortunately, acquiring and incorporating the necessary domain knowledge can be a significant problem when building a practical planning system. The knowledge engineering process is typically time-consuming and expensive. Furthermore, if a human expert is not available it may be extremely difficult to obtain the necessary knowledge. One solution is for a system to automatically acquire domain-specific knowledge through learning. The idea of a planning system that can improve its performance with experience is very attractive. Furthermore, advances in machine learning have provided a deeper understanding of learning mechanisms relevant to acquiring such knowledge. For this reason, there is a great deal of interest in this area of artificial intelligence. This book brings together, in one volume, a set of chapters from the primary researchers in the field, presenting a picture of its current state and its likely areas for application. The chapters describe a variety of learning methods-including analogical, case-based, explanation-based, decision-tree, and reinforcement techniques-and a wide range of planning architectures, running the gamut from STRIPS-like systems to problem-reduction architectures to reactive agents. It will draw the interest of AI researchers and system developers, especially those in machine learning, planning, and scheduling, as well as researchers from other fields, such as operations research, that focus on automated planning.

Effect of Representation on the Performance of Neural Networks in Structural Engineering Applications

Abstract: The pattern-mapping, pattern-classification, and optimization capabilities of neural networks have been used to solve a number of structural analysis and design problems. Most applications exploit the pattern-mapping capability and are based on the back-propagation paradigm for neural networks. There are a number of factors that influence the performance of these networks. This paper initially discusses these factors and the domain-dependent and -independent techniques presently available for improving performance. The paper then considers the effect of representation, selected for the input/output pattern pairs, on the performance of these networks and demonstrates that representations based on dimensionless terms, derived from dimensional analysis, lead to improved performance. It is shown that dimensional analysis provides a representational framework, with reduced dimensionality and embedded domain knowledge, within which effective learning can take place and that this representational change can be used to enhance the domain-independent and -dependent techniques presently available for improving performance of these networks.

State of the art in automated validation of knowledge-based systems☆

Abstract: Validation of Knowledge-Based Systems (KBS) is an important aspect of the overall KBS development process, which aims to assure the system's ability to reach correct conclusion. The objective of this paper is to discuss the desirable functionality of an automated validation tool and to provide a survey of existing methods and tools supporting that functionality. The scope of our discussion is limited to validating the level performance of the KBS as a problem solver, since this is the aspect in which KBS differ most from conventional software; more conventional aspects of system evaluation, such as assessing the “usability” of the system, are not covered. Automated tools are considered in two categories: dynamic and static. Dynamic validation tools are those that measure and, in some cases, refine the level of performance of a KBS using a suite of test cases. Use of such tools assumes that an adequate set of real test cases is available. Static validation tools are used to create test cases by making use of domain knowledge already embodied in the KBS or meta-knowledge. Such tools are used when an inadequate set of test cases is available.

A model for structured data entry based on explicit descriptional knowledge.

Abstract: Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicans with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Reward Functions for Accelerated Learning

Abstract: This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. The methodology involves the use of heterogeneous reinforcement functions and progress estimators, and applies to learning in domains with a single agent or with multiple agents. The methodology is experimentally validated on a group of mobile robots learning a foraging task.

On formal specification of design tasks

Abstract: In the development of a design system, a formal specification can play an important role providing a precise description of both the static and the dynamic aspects of the system. Static aspects not only include domain knowledge about properties of design objects and relations between these properties, but also domain knowledge about requirements of design objects and relations between these requirements. Dynamic aspects include strategic knowledge about steps undertaken in the design process. In this paper, a logical framework for design tasks is presented which incorporates the notion of a meta-level architecture to explicitly represent declarative knowledge on the dynamics of reasoning processes. Based on this logical framework a generic task model of design is presented, describing the essential structure of the design task, in which the dynamic modification of design object descriptions and requirements is explicitly defined.

Automated program understanding by concept recognition

Abstract: Program understanding can be seen as the process of understanding abstract concepts in the program code. Thus, automated recognition of abstract concepts may greatly assist the human understanding process. This paper describes an approach to automated concept recognition and its implementation. In the approach, we use a concept model and a library of concept recognition rules to describe what the concepts are and how to recognize them from lower-level concepts. Programming language knowledge as well as domain knowledge are used to aid the recognition of abstract concepts.

Knowledge assessment

Abstract: The QUERY procedure is designed to systematically question an expert, and construct the unique knowledge space consistent with the expert's responses. Such a knowledge space can then serve as the core of a knowledge assessment system. The essentials of the theory of knowledge spaces are given here, together with the theoretical underpinnings of the QUERY procedure. A full scale application of the procedure is then described, which consists in constructing the knowledge spaces of five expert-teachers, pertaining to 50 mathematics items of the standard high school curriculum. The results show that the technique is applicable in a realistic setting. However, the analysis of the data indicates that, despite a good agreement across experts concerning item difficulty and other coarse measures, the constructed knowledge spaces obtained for the different experts are not as consistent as one might expect or hope. Some experts appear to be considerably more skillful than others at generating a usable knowledge space, at least by this technique.

Domain analysis and reverse engineering

Abstract: Current reverse engineering technology is typically based on program analysis methods such as parsing and data flow analysis. As such, it is limited in what it can accomplish. Knowledge of the application domain containing a program can help overcome this limit and aid the comprehension process. The paper discusses the relationship of application domain analysis and reverse engineering. Two case studies are presented. The first describes how domain knowledge, expressed as an object-oriented framework, can aid the reverse engineering process for a well-understood domain. The second studies how reverse engineering can be used to build a domain model. Issues raised by the confluence of domain analysis and reverse engineering are discussed, and implications on future work in the area are suggested. >