Blasting Open a Choice Space: Learning Inflectional Morphology for NLP

This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A running example throughout the text is taken from the paradigmatic morphology elicitation module of a system called Boas, which elicits knowledge to support a machine translation system. The main point of the article is an argument about the necessity to analyze the design choice space for complex natural language processing (NLP) systems early, comprehensively, and overtly.     

Laddering

Abstract: There is still a need for new knowledge elicitation techniques and tools. Laddering is a technique which has a long history in a wide range of disciplines and which has proved extremely useful in knowledge elicitation. There have, however, been few attempts to describe and survey the technique per se.
This paper describes the technique, its background, its use, analysis, and automation, with particular reference to knowledge elicitation. The advantages and disadvantages of the technique are described. It is concluded that laddering is a valuable technique which could be used in a wide range of settings.     

Knowledge engineering and psychology: Towards a closer relationship

Knowledge engineering projects deal with a wide range of domains within organizational and academic contexts. A number of elicitation techniques are used to acquire knowledge from experts. Most of these techniques originated within psychology but have been developed by knowledge engineers to become more structured, efficient and systematic. Until now, nobody has tried to re-apply these modified techniques back into psychology. This paper describes work that addresses this matter. It focuses on the psychological knowledge possessed by all people that enables them to deal with everyday problems and make life decisions. We refer to this as ‘personal knowledge’. To take a knowledge engineering approach to personal knowledge, we investigated the use of knowledge elicitation techniques to capture personal knowledge. We describe an empirical study involving ten participants and 80 knowledge acquisition sessions that assessed eight elicitation techniques in this context. The results revealed that each of the techniques showed promise at efficiently capturing and structuring aspects of an individual's personal knowledge. A content analysis of the acquired knowledge led to the construction of a meta-model (a primitive ontology) of personal knowledge and to the design for a new methodology for psychological research. From the perspective of psychology, the paper shows that knowledge engineering methods can be of value to psychologists. From the perspective of knowledge engineering and the wider computer science community, the paper shows that empirical methods used by psychologists can benefit the development and evaluation of ontologies and elicitation techniques.     

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.     

Eunomos,a legal document and knowledge management system for the Web to provide relevant,reliable and up-to-date information on the law

This paper describes the Eunomos software, an advanced legal document and knowledge management system, based on legislative XML and ontologies. We describe the challenges of legal research in an increasingly complex, multi-level and multi-lingual world and how the Eunomos software helps users cut through the information overload to get the legal information they need in an organized and structured way and keep track of the state of the relevant law on any given topic. Using NLP tools to semi-automate the lower-skill tasks makes this ambitious project a realistic commercial prospect as it helps keep costs down while at the same time allowing greater coverage. We describe the core system from workflow and technical perspectives, and discuss applications of the system for various user groups.     

The role of domain knowledge in requirements elicitation via interviews: an exploratory study

Requirements elicitation is the first activity in the requirements engineering process. It includes learning, surfacing, and discovering the requirements of the stakeholders of the developed system. Various elicitation techniques exist to help analysts elicit the requirements from the different stakeholders; the most commonly used technique is the interview. Analysts may have domain knowledge prior to the elicitation process. Such knowledge is commonly assumed to have positive effects on requirements engineering processes, in that it fosters communication, and a mutual understanding of the needs. However, to a minor extent, some negative effects have also been reported. This paper presents an empirical study in which the perceived and actual effects of prior domain knowledge on requirements elicitation via interviews were examined. The results indicate that domain knowledge affects elicitation via interview in two main aspects: communication with the customers and understanding their needs. The findings provide insights as to both the positive and negative effects of domain knowledge on requirements elicitation via interview, as perceived by participants with and without domain knowledge, and show the existence of an actual effect on the course of the interviews. Furthermore, these insights can be utilized in practice to support analysts in the elicitation process and to form requirements analysis teams. They highlight the different contributions that can be provided by analysts with different levels of domain knowledge in requirements analysis teams and the synergy that can be gained by forming heterogeneous teams of analysts.     

Applications of mathematical models: An expert system for selecting solution methods for solving nonlinear programming problems

This paper presents an expert system for selecting solution methods for solving nonlinear programming problems. No general method exists for solving nonlinear programming problems in the same manner as the Simplex algorithm solves linear programming problems. Hence to some extent nonlinear programming even today exists as an experimental field of research. It has advanced to this date through the proposal and programming of particular algorithms, examinations of the results of the implementation of the algorithms to problems of interest, and the construction of better algorithms based on the experience gained. Computational experiences of many numerical methods such as direct search methods, method of multipliers, and linear approximation methods have been widely reported in literature int he past two decades. However, there is no unified approach to solve a genral NLP problem.

A small scale expert system has been developed using the Texas Instruments Personal Consultant Plus (PCPLUS) software to provide an organised approach for solving NLP problems. A knowledge base has been constructed to represent the past computational experiences of different solution methods on different classes of NLP problems. The system can be effectively used in conjunction with OPTLIB (1), which is a optimization program library for solving NLP problems. The purpose of the system is to guide inexperienced engineers to choose a proper NLP solution method and to serve as teaching aid. In selecting the methods more stress has been laid on the sure-footedness of the method, than on computational time and storage space requirement to make it more reliable for users. This paper presents the knowledge organisation, rules and the testing results.     

ViStA: a visualization system for exploring Arabic text

Text visualization has become a significant tool that facilitates knowledge discovery and insightful presentation of large amounts of data. This paper presents a visualization system for exploring Arabic text called ViStA. We report about the design, the implementation and some of the experiments we conducted on the system. The development of such tools assists Arabic language analysts to effectively explore, understand, and discover interesting knowledge hidden in text data. We used statistical techniques from the field of Information Retrieval to identify the relevant documents coupled with sophisticated natural language processing (NLP) tools to process the text. For text visualization, the system used a hybrid approach combining latent semantic indexing for feature selection and multidimensional scaling for dimensionality reduction. Initial results confirm the viability of using this approach to tackle the problem of Arabic text visualization and other Arabic NLP applications.     

Tracking latent domain structures: An integration of pathfinder and Latent Semantic Analysis

Standard psychological scaling methods have been widely used as knowledge elicitation tools to uncover structural characteristics of a given domain. However, these methods traditionally rely on relatedness ratings from human experts, which is often time-consuming and tedious. We describe an integrated approach to knowledge elicitation and representation using Latent Semantic Analysis and Pathfinder Network Scaling techniques. The semantic structure of a subject domain can be automatically characterised from a collection of published documents in the domain. The method is illustrated with an example of organising a digital library in accordance to latent semantic structures.     

Taking up the situated cognition challenge with ripple down rules

Situated cognition poses a challenge that requires a paradigm shift in the way we build symbolic knowledge-based systems. Current approaches require complex analysis and modelling and the intervention of a knowledge engineer. They rely on building knowledge-level models which often result in static models that suffer from the frame of reference problem. This approach has also resulted in an emphasis on knowledge elicitation rather than user requirements elicitation. The situated nature of knowledge necessitates a review of how we build, maintain and validate knowledge-based systems. We need systems that are flexible, intuitive and that interact directly with the end-user. We need systems that are designed with maintenance in mind, allowing incremental change and on-line validation. This will require a technique that captures knowledge in context and assists the user to distinguish between contexts. We take up this challenge with a knowledge acquisition and representation method known as Ripple-down Rules. Context in Ripple-down Rules is handled by its exception structure and the storing of the case that prompted a rule to be added. A rule is added as a refinement to an incorrect rule by assigning the correct conclusion and picking the salient features in the case that differentiate the current case from the case associated with the wrong conclusion. Thus, knowledge acquisition and maintenance are simple tasks, designed to be performed incrementally while the system is in use. Knowledge acquisition, maintenance and inferencing are offered in modes that can be performed reflexively without a knowledge engineer. We further describe the addition of modelling tools to assist the user to reflect on their knowledge for such purposes as critiquing, explanation, “what-if” analysis and tutoring. Our aim is to provide a system that lets the user choose the mode of interaction and view of the knowledge according to the situation in which they find themselves and their own personal preferences.     

Knowledge elicitation using more than one expert to cover the same domain

Knowledge elicitation is accepted as being one of the most problematic areas in the creation of a knowledge-based system.A large amount of research has already concentrated on finding more efficient and effective techniques for eliciting knowledge from an individual expert. However, little attention has been given to the involvement of more than one source of expertise in knowledge-based system development.This paper is based on the authors' practical experience gained when developing a knowledge-based system for the conceptual design of bridges. It shows that the use of more than one expert throughout the knowledge elicitation process can improve both the efficiency of the approach and the quality of the knowledge acquired.     

智能需求获取与建模研究综述

需求获取和建模是指从需求文本或记录中获取显式和隐式的需求,并通过表格化、图形化、形式化等方法构建相应模型的过程,是软件开发过程中极为关键的一步,为后续系统设计与实现铺平道路,提高软件开发效率和质量,提升软件系统稳定性和可行性.研究者们在需求获取与建模方面获得了一系列研究成果,根据其关注阶段不同,可以将它们分为需求知识提...     

A specialized framework for medical diagnostic knowledge-based systems.

For a knowledge-based system (KBS) to exhibit an intelligent behavior, it must be endowed with knowledge enabling it to represent the expert's strategies. The elicitation task is inherently difficult for strategic knowledge, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form which may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge-Based Systems that can help an expert in the process of building KBSs in a medical domain. The framework is based on an epistemological model of diagnostic reasoning which has proven to be helpful in describing the diagnostic process in terms of the tasks that it is composed of. It allows a straightforward modeling of diagnostic reasoning at the knowledge level by the domain expert, thus helping to convey domain-dependent strategies into the target KBS.     

From process experts to a real-time knowledge-based system

This paper describes the development and implementation of a real-time knowledge-based system (RTKBS) carried out in collaboration with Eli Lilly in the UK. The RTKBS was developed in order to provide consistent advice to the operators of a large-scale antibiotic production plant in real time. The ultimate objective was to reduce variability and improve the process yield. A wealth of process information exists in the experience of the process operators, engineers and scientists, and it was capitalized upon in this application. However, this information tends primarily to be qualitative in nature and its elicitation traditionally represents a bottleneck in developing an RTKBS. The paper demonstrates that this type of process knowledge can be effectively and rapidly captured and then coded within an RTKBS. The project was undertaken in two stages. During the knowledge elicitation stage the specific application area was identified in consultation with the industrial partner and qualitative knowledge in the selected area was extracted from experts/operators using KAT™ (a structured technique for knowledge elicitation). Subsequently this knowledge was converted into a rule base and implemented within G2, which then acted as an advisory/decision support system.     

The knowledge elicitation problem: a psychological perspective

The knowledge elicitation problem arises from the need to acquire the knowledge of human experts in an explicit form suitable for encoding in a computer program such as an expert system. This is very difficult to perform successfully because of the size and complexity of knowledge structures in the human brain, and because much procedural knowledge is tacit and unavailable to conscious verbal report via interview methods. The present paper draws upon an extensive review of research in the field of cognitive psychology in an attempt to offer a practical approach to this problem. First, a wide range of cognitive theories concerning the nature of knowledge representation in humans is considered, and a synthesis of the current state of theory is provided. Second, attention is drawn to a number of performance factors which may constrain the exhibition of a person's underlying cognitive competence. There then follows a review and discussion of a number of alternative psychological methodologies that might be applied to the elicitation of different types of human knowledge. Finally, some suggestions are made for the application of the psychological work discussed to the practical problem of knowledge elicitation.     

Fuzzy sets based knowledge systems and knowledge elicitation

Fuzzy sets are adequate forms of knowledge representation when the information is uncertain due to vagueness and imprecision. Knowledge structures using fuzzy sets are similar to those implemented in non-fuzzy systems. Classical knowledge elicitation methods can be used in combination with techniques to develop membership functions. The fuzzy set representation has several advantages, including flexibility in expressing uncertain knowledge during elicitation, representation of the knowledge and its uncertainty as a unique entity, easy interfacing with classical systems, and a more robust system in ill-defined domains. These advantages result in increased system reliability.     

Stakeholder analysis as a front-end to knowledge elicitation

Knowledge elicitation is one of the more problematic phases of knowledge based systems development. Two specific problems that have received inadequate attention in the literature are the process of expert selection and the use of a broader, socially and politically informed, frame of reference for knowledge elicitation. This paper builds on the few attempts to consider these problems. It contributes to a novel interpretation of the broader knowledge acquisition context using the powerful notion of stakeholders. More specifically, it proposes the application of an interpretive stakeholder analysis approach previously developed in information systems research. It is argued that the identification of knowledge based systems’ and knowledge elicitation stakeholders and the investigation of their viewpoints not only enriches the knowledge elicitation process, it also contributes to a broader understanding of knowledge based systems development.     

知识感知的预训练语言模型综述

随着自然语言处理（NLP）领域中预训练技术的快速发展,将外部知识引入到预训练语言模型的知识驱动方法在NLP任务中表现优异,知识表示学习和预训练技术为知识融合的预训练方法提供了理论依据。概述目前经典预训练方法的相关研究成果,分析在新兴预训练技术支持下具有代表性的知识感知的预训练语言模型,分别介绍引入不同外部知识的预训练语言模型,并结合相关实验数据评估知识感知的预训练语言模型在NLP各个下游任务中的性能表现。在此基础上,分析当前预训练语言模型发展过程中所面临的问题和挑战,并对领域发展前景进行展望。     

Control of nonlinear, continuous, dynamic systems via finite elements, sensitivity analysis, and optimization

A general methodology to design open loop controllers for nonlinear, dynamic, continuous systems is presented and applied to control a single flexible link (SFL). In this application, the partial differential equations that describe the beam system are first analyzed via the finite element method (FEM) and Newmark integration method. Two open loop control inputs to achieve specified system performance criteria are then computed by posing and solving inverse dynamics problems. These analyses use nonlinear programming (NLP) algorithms and analytical gradients that are computed by the direct sensitivity method. The open loop control is verified experimentally. Closed loop controller synthesis for linear time invariant (LTI) and linear time varying systems (LTV) is relatively well understood. To apply this knowledge base to the control of the SFL, the nonlinear finite element plant model is linearized and recast in standard state space form.