Integrated graphical approach to knowledge representation and acquisition

An approach toward improving the accessbility of the knowledge and information structures of expert systems is described; it is based upon a foundation development environment called the Rule-Based Frame System (RBFS), which forms the kernel of a larger system, IDEAS. RBFS is a knowledge representation language, within which a distinction is drawn between information which represents the world or domain, and knowledge which states how to make conclusions based upon the domain. Information takes the form of frames, for system processing, but is presented to the user/developer as an associative network via a Visual Editor for the Generation of Associative Networks (VEGAN). Knowledge takes the form of production rules, which are connected at suitable points in the domain model, but again it is presented to the user via a graphical interface known as the Knowledge Encoding Tool (KET). KET is designed to assist in knowledge acquisition in expert systems. It uses a combination of decision support trees and associative networks as its representation. A combined use of VEGAN and KET will enable domain experts to interactively create and test their knowledge base with minimum involvement on behalf of a knowledge engineer. An inclusion of learning features in VEGAN/KET is desirable for this purpose. The main objective of these tools, therefore, is to encourage rapid prototyping by the domain expert. VEGAN and KET are implemented in the Poplog environment on SUN 3/50 workstations.     

GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

融合知识表示学习的双向注意力问答模型

知识图谱问答是自然语言处理领域的研究热点之一,近年来受到广泛的关注。知识图谱问答面临需要结合多条三元组进行推理的多跳问题以及知识图谱不完整等挑战,为解决这些问题,提出了一种融合知识表示学习的双向注意力模型（Bidirectional Attention model combining Knowledge Representation,KR-BAT）。引入知识表示学习以提高模型全局建模能力,应对知识图谱不完整的情况;使用双向注意力模型捕捉候选答案和问题间丰富的交互信息,经过分析推理给出答案。在MetaQA数据集上进行了实验,对比VRN、KV-MemNN、GraftNet等基准模型,在完整知识图谱上达到了非常有竞争力的性能,在不完整知识图谱上大幅度优于基准模型。     

知识赋能的新一代信息系统研究现状、发展与挑战

信息系统的发展目前正处于感知智能迈向认知智能的关键阶段,传统信息系统难以满足发展要求,数字化转型势在必行.数字线索(digitalthread)是面向全生命周期的数据处理框架,通过连接生命周期的各阶段数据,实现物理世界与数字空间的映射与分析.知识图谱(knowledgegraph)是结构化的语义知识库,以符号形式描述物理世界中的概念及其相互关系,通过知识驱动形成体系化的构建与推理流程.两者对知识赋能的信息系统研究具有重要意义.综述了知识赋能的新一代信息系统的研究现状、发展与挑战.首先,从数字线索系统出发,介绍数字线索的概念和发展,分析数字线索的六维数据构成和6个数据处理阶段;然后介绍知识图谱系统,给出普遍认同的知识图谱的定义和发展,概括知识图谱的架构与方法;最后,分析和探索数字线索与知识图谱结合的方向,列举KG4DT (knowledge graph for digital thread)和DT4KG (digital thread for knowledge graph)的受益方向,对未来知识赋能的新一代信息系统提出开放问题.     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.     

融合领域特征知识图谱的电网客服问答系统

知识图谱（KG）是实现领域问答系统的关键技术之一,能够降低客服成本,推进客户自助服务的智能化,具有较大的商用价值和研究意义。针对基于KG问答系统中存在的中文问题表达模糊、线上服务运维成本高的问题,融合领域特征知识图谱的电网客服问答系统（HDKG-QA）,其能基于LSTM模型识别实体/断言,基于主题比较的语义增强方法准确寻找外部知识,使用启发式规则优化答案候选集,并定期根据ILP求解器设置全局KG的更新策略。HDKG-QA能够达到较高的实体/断言识别准确率,自动将领域知识映射为本地KG,快速实现服务知识库的在线更新,达到以较低的响应延迟实现高准确率的回答。根据国网重庆市电力公司信息通信分公司的实际客服问答数据集对本系统进行验证,实验结果表明通过引入LSTM和语义增强方法,问答系统的准确率提高了17%;基于启发式规则的优化答案排序策略将准确率提高了8%;通过引入ILP求解器,在保障同样准确率的情况下,问答响应延迟降低了9%。     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

Kave: a tool for knowledge acquisition to support artificial ventilation

A decision support system for artificial ventilation is being developed. One of the fundamental goals for this system is the application of the system when a domain expert is not present. Such a system requires a rich knowledge base. The knowledge acquisition process is often considered to be the bottleneck in acquiring such a complete knowledge base. Since no single available method, for example interviewing domain experts, is sufficient for removing this bottleneck, we have chosen a combination of different methods. The different backgrounds of knowledge engineers and domain experts could cause communication restrictions and difficulties between them, e.g. they might not understand each others knowledge domain and this will affect formulation of the knowledge. To solve this problem we needed a tool which supports both the knowledge engineer and the domain expert already from the initial phase of developing the knowledge base. We have developed a knowledge acquisition system called KAVE to elicit knowledge from domain experts and storing it in the knowledge base. KAVE is based on a domain specific conceptual model which is a result of cooperation between knowledge engineers and domain experts during identification, design and structuring of knowledge for this domain. KAVE includes a patient simulator to help validate knowledge in the knowledge base and a knowledge editor to facilitate refinement and maintenance of the knowledge base.     

Knowledge conceptualization tool

Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process, and few automated support tools exist to help knowledge engineers enhance their performance. The paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process     

A crowdsourcing method to develop virtual human conversational agents

Educators in medicine, psychology, and the military want to provide their students with interpersonal skills practice. Virtual humans offer structured learning of interview skills, can facilitate learning about unusual conditions, and are always available. However, the creation of virtual humans with the ability to understand and respond to natural language requires costly engineering by conversation knowledge engineers (generally computer scientists), and incurs logistical cost for acquiring domain knowledge from domain experts (educators). We address these problems using a novel crowdsourcing method entitled Human-centered Distributed Conversational Modeling. This method facilitates collaborative development of virtual humans by two groups of end-users: domain experts (educators) and domain novices (students). We implemented this method in a web-based authoring tool called Virtual People Factory. Using Virtual People Factory, medical and pharmacy educators are now creating natural language virtual patient interactions on their own. This article presents the theoretical background for Human-centered Distributed Conversational Modeling, the implementation of the Virtual People Factory authoring tool, and five case studies showing that Human-centered Distributed Conversational Modeling has addressed the logistical cost for acquiring knowledge.