常识问答研究综述

常识问答是一项重要的自然语言理解任务, 旨在利用常识知识对自然语言问句进行自动求解, 以得到准确答案. 常识问答在虚拟助手或社交聊天机器人等领域有着广泛的应用前景, 且其蕴涵了知识挖掘与表示、语言理解与计算、答案推理和生成等关键科学问题, 因而受到工业界和学术界的广泛关注. 首先介绍常识问答领域的主要数据集; 其次, 归纳不同常识知识源在构建方式、常识来源和表现形式上的区别; 同时, 重点分析并对比前沿常识问答模型, 以及融合常识知识的特色方法. 特别地, 根据不同问答任务场景中常识知识的共性和特性, 建立包含属性、语义、因果、语境、抽象和意图6大类的知识分类体系. 以此为支撑, 针对常识知识数据集建设, 感知知识融合和预训练语言模型的协作机制, 以及在此基础上的常识知识预分类技术, 进行前瞻性的研究, 并具体报告上述模型在跨数据集迁移场景下的性能变化, 及其在常识答案推理中的潜在贡献. 总体上, 包含对现有数据和前沿技术的回顾, 也包含面向跨数据知识体系建设、技术迁移与通用化的预研内容, 借以在汇报领域技术积累的前提下, 为其理论和技术的进一步发展提供参考意见.     

怎样利用语言知识资源进行语义理解和常识推理

该文讨论怎样利用语言知识资源来帮助机器进行语义理解和常识推理。首先,指出人类生活在常识和意义世界中,人工智能机器人必须理解自然语言的意义,能够在此基础上进行常识推理。接着,简单梳理了基于知识和基于统计两种自然语言处理路线各自的优长和短缺。然后,说明完全绕开知识的统计方法和深度学习,都不能真正理解概念和语言。该文通过具体案例说明,《实词信息词典》已经配备了有关词项的语义角色关系及其句法配置信息;把这种语言知识加入知识图谱和内容计算中,可以为人工智能提供理解和解释从而造就一种可解释的人工智能。由于“物性角色”描述了名词所指事物的百科知识,可用以回答相关事物是什么(形式角色)、有哪些部件(构成角色)、用什么做的(材料)、怎么形成的(施成)、有什么用途(功用)等常识性问题。     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.     

一个模糊常识库的模型

常识的表示及推理是人工智能的一个核心难题。文章提出了一个模糊常识库的模型,描述了模糊概念的表示方法以及常识的模糊推理,并给出了模型解题的两个例子。     

一种交互式事件常识知识的获取方法

赋予机器常识知识是使机器具有真正智能的必备条件之一,而获得这些常识一直是人工智能研究的一个重要课题。该文提出了一种通过交互的方式来引导知识贡献者给出关于事件的常识知识的方法。方法获取过程是一个机器与贡献者的交互过程: 机器动态地生成问题,对知识贡献者进行提问;知识贡献者通过回答问题给出常识知识。交互过程通过包含提示信息的提问问题对知识贡献者进行提示,运用七种类型问题层层递进地引导知识贡献者思考,以此唤醒他们大脑中的常识知识;通过动态变化的问题改善知识贡献者贡献常识知识过程的趣味性。同时,该文还引入可接受性和有效性两个定量标准评价提问问题,用于进一步改善交互过程。实验结果表明,知识贡献者运用此方法给出的知识量增加了451.61%,同时知识的正确率也达到了92.5%。
     

基于常识的亲属关系推理模型

常识知识的研究与发展得到了人工智能界的很大重视。文章建立了一个基于常识的人物亲属关系推理模型,研究了亲属关系常识以及人物信息的表示与存储。此外,对实际所要解决的问题进行了总结。     

初等数学应用题题意理解中的常识库系统构建

类人计算领域, 题意的机器理解是数学应用题自动求解的难点. 常识性知识的缺失直接影响到题意理解的准确性. 本研究以常识为研究对象, 收集了历年初等数学古典概型的典型案例, 分析了古典概型类应用题的常识特征, 并进行了常识类型划分; 设计了XML结构存储常识性知识, 构建常识库系统实现古典概型常识的分类、表征及存储, 辅助计算机进行题意理解. 通过典型案例的应用, 其结果显示本研究构建的常识库对古典概型应用题的题意正确理解是十分有帮助的.     

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.     

Progress in the Development of National Knowledge Infrastructure

This paper presents the recent process in a long-term research project,called National Knowledge Infrastructure(or NKI).Initiated in the early 2000,the project aims to develop a multi-domain shareable knowledge base for knowledge-intensive applications.To develop NKI,we have used domain-specific ontologies as a solid basis,and have built more than 600 ontologies.Using these ontologies and our knowledge acquisition methods,we have extracted about 1.1 millions of domain assertions.For users to access our NKI knowledge,we have developed a uniform multi-modal human-knowledge interface.We have also implemented a knowledge application programming interface for various applications to share the NKI knowledge.     

基于神经网络的知识推理研究综述

知识推理是知识图谱补全的重要手段,一直以来都是知识图谱领域的研究热点之一。随着神经网络不断取得新的发展,其在知识推理中的应用在近几年逐渐得到广泛重视。基于神经网络的知识推理方法具备更强的推理能力和泛化能力,对知识库中实体、属性、关系和文本信息的利用率更高,推理效果更好。简要介绍知识图谱及知识图谱补全的相关概念,阐述知识推理的概念及基本原理,从语义、结构和辅助存储三个维度展开,综述当下基于神经网络的知识推理最新研究进展,总结了基于神经网络的知识推理在理论、算法和应用方面存在的问题和发展方向。     

Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution

Answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods/tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory.     

基于知识工程的船体焊接工艺研究

船舶制造过程中船体焊接领域工艺知识缺乏有效的归纳与整理,导致其重用性和 共享性差。针对上述问题,基于知识工程技术,研究了船体焊接工艺领域知识的获取、分类、 表示及推理应用的方法,并开发了工艺知识库系统,有效实现了知识的共享与重用。首先提出 了船体焊接工艺知识获取途径及分类模式;其次以此为基础,将基于本体的知识表示方法的研 究成果应用于船体焊接工艺,建立领域本体;然后提出焊接工艺推理模式,以国家标准、行业 标准等规范性文件和专业书籍、专家意见等指导性文件为基础构建工艺规则库,设计模糊规则 推理系统;最终设计开发面向船体焊接工艺的知识库系统,规范化实现知识获取、知识表示、 知识推理及知识管理功能,为船体焊接领域相关工作人员提供知识共享及重用的支撑平台,为 船舶制造其他领域中知识工程技术的利用提供参考。     

专家系统中背景研究的意义

人工智能中不同领域的研究表明：新一代的智能辅助系统是与背景相关的。虽然人们普遍接受知识应有一个背景部件的观点，但在可用的知识表态方法及随后的知识处理中极少显式表达和利用背景知识。本文着眼于探讨 背景研究在专家系统开发中的意义，以期阐明：背景知识的显式识别、表达与利用有助于专家系统听知识获取、知识表示、推
推理、学习和解释，从而提高专家系统自适应能力和解决问题的智能性。     

心理常识概念的表示、获取和分析

常识知识是一类重要的人类知识，对自然语言分析、机器智能研究和自动推理研完等都有重要的意义。本文围绕心理常识，主要讨论与心理相关的常识概念的表示、获取和分析的方法。针对现有的概念模型中的概念主要由手工获取．缺乏自动方法，使得概念获取的非冗余性、一致性不能得到保证的问题，本文提出了获取心理常识概念的基本策略：依据心理学中的心理范畴手工获取心理常识的基础概念及概念间关系；根据属性的心理特征手工得到心理属性和属性问关系；以心理常识基础概念和心理常识属性为语义成分，通过“子类生成规则”自动完成获取和组织心理常识概念的任务。并且通过“子类检查规则”检查和分析新加入概念库的常识概念的冗余性和一致性。     

知识图谱可解释推理研究综述

面向知识图谱的知识推理旨在通过已有的知识图谱事实,去推断新的事实,进而实现知识库的补全.近年来,尽管基于分布式表示学习的方法在推理任务上取得了巨大的成功,但是他们的黑盒属性使得模型无法为预测出的事实做出解释.所以,如何设计用户可理解、可信赖的推理模型成为了人们关注的问题.从可解释性的基本概念出发,系统梳理了面向知识图谱的可解释知识推理的相关工作,具体介绍了事前可解释推理模型和事后可解释推理模型的研究进展;根据可解释范围的大小,将事前可解释推理模型进一步细分为全局可解释的推理和局部可解释的推理;在事后解释模型中,回顾了推理模型的代表方法,并详细介绍提供事后解释的两类解释方法.此外,还总结了可解释知识推理在医疗、金融领域的应用.随后,对可解释知识推理的现状进行概述,最后展望了可解释知识推理的未来发展方向,以期进一步推动可解释推理的发展和应用.     

Formalizing dialectical explanation support for argument-based reasoning in knowledge-based systems

The concept of explanation has received attention from different areas in Computer Science, particularly in the knowledge-based systems and expert systems communities. At the same time, argumentation has evolved as a new paradigm for conceptualizing commonsense reasoning, resulting in the formalization of different argumentation frameworks and the development of several real-world argument-based applications. Although the notions of explanation and argument for a claim share many common elements in knowledge-based systems their interrelationships have not yet been formally studied in the context of the current argumentation research in Artificial Intelligence. This article explores these ideas by providing a new perspective on how to formalize dialectical explanation support for argument-based reasoning. To do this, we propose a formalization of explanations for abstract argumentation frameworks with dialectical constraints where different emerging properties are studied and analyzed. As a concrete example of the formalism introduced we show how it can be fleshed out in an implemented rule-based argumentation system.     

Knowledge engineering issues in developing a case-based reasoning application

Case-Based Reasoning (CBR) has emerged from research in cognitive psychology as a model of human memory and remembering. It has been embraced by researchers of AI applications as a methodology that avoids some of the knowledge acquisition and reasoning problems that occur with other methods for developing knowledge-based systems. In this paper we propose that, in developing knowledge based systems, knowledge engineering addresses two tasks. There is a problem analysis task that produces the problem representation and there is the task of developing the inference mechanism. CBR has an impact on the second of these tasks but helps less with the first. We argue that in some domains this problem analysis process can be significant and propose an iterative methodology for addressing it. To evaluate this, we describe the application of case-based reasoning to the problem of aircraft conflict resolution in a system called ISAC. We describe the application of this iterative methodology and assess the knowledge engineering impact of CBR.     

Intelligent information processing for building university knowledge base

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than document-centric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.     

PRIMEROSE: PROBABILISTIC RULE INDUCTION METHOD BASED ON ROUGH SETS AND RESAMPLING METHODS

Automated knowledge acquisition is an important research issue in machine learning. Several methods of inductive learning, such as ID3 family and AQ family, have been applied to discover meaningful knowledge from large databases and their usefulness is assured in several aspects. However, since their methods are of a deterministic nature and the reliability of acquired knowledge is not evaluated statistically, these methods are ineffective when applied to domains essentially probabilistic in nature, such as medical domains. Extending concepts of rough set theory to a probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory (PRIMEROSE) and develop a program that extracts rules for an expert system from a clinical database, using this method. The results show that the derived rules almost correspond to those of the medical experts.     

深度学习与知识推理相结合的研究综述

知识推理作为知识图谱的重要一环,一直处于重点研究热门对象之中。随着深度学习的不断发展,多种深度学习模型与知识推理的结合引起了很大的重视,得到了大量国内外学者的热捧。为了提高从已有知识中推理出新知识的正确率,二者的结合被广泛研究。基于深度学习的知识推理可以挖掘得更深、更仔细、更精确,有效提高了丰富知识库中的实体、关系、属性和文本信息等的利用率,使推理效果更佳。通过简单介绍知识图谱以及知识补全概念,重点叙述知识推理的概念及基本原理,从知识表示学习、知识获取和知识计算应用三个方向展开,综述了基于深度学习的知识推理CTransR、PTransE、TKRL、HAAT、AMNRE、CLSP、HDSA和SDLM模型的最新研究进展;总结了基于深度学习的知识推理在理论、算法和应用方面尚未克服的问题、研究方向和未来发展前景。