Extracting causation knowledge from natural language texts

SEKE is a semantic expectation‐based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.     

Eliciting symptom-diagnosis knowledge from online medical Q&A

With the objective to automatically detect diseases from symptoms in free-text data, a methodology to extract symptom-diagnosis knowledge from online medical textual data in Q&A domain is proposed in this paper: (1) a term frequency-inverse document frequency and PRECISION method is adopted to retrieve symptom words from unstructured text; (2) a variable precision rough set based genetic algorithm is applied to reduce redundant symptom words, and a rough set based rule is utilized for adding discriminative symptom words assisting to discriminate diseases sharing similar symptoms; (3) by employing fuzzy linguistic variables to express the risk level of disease or severity level of symptoms, a knowledge base with fuzzy belief structure is generated. Using data extracted from a Chinese medical Q&A forum for training and testing, some classical gastrointestinal diseases serve as a case study to evaluate the efficiency of the proposed methodology. Subsequently performance comparisons are made between the proposed methodology and some other classifiers, such as the decision tree algorithms including ID3 and J45, and the Bayesian network classifier. The comparative results demonstrate that the proposed methodology outperforms the decision tree algorithms and the Bayesian network classifier.     

从《中国大百科全书》中获取教育知识

教育知识获取是国家知识基础设施研制中知识获取的一项重要工作。《中国大百科全书》中包含着教育学、教育心理学、学习理论、教育管理和各国教育等丰富的教育知识。该文研制了一种知识获取方法,并采用此方法成功地获取了大量的教育知识。该方法由两部分组成:一是高度类自然的教育知识描述语言(PKDL),由知识工程师用于形式化《中国大百科全书》中的教育知识文本;二是PKDL编译系统,该系统对PKDL文本进行编译、分析和知识连接,最终生成教育知识库。     

模糊概念图知识表示及其推理机制研究*

通过对现有模糊概念图的研究,针对概念的所指域与模糊信息间的冗余问题和用模糊度表示模糊概念问题,提出一种改进的模糊概念图知识表示方法。在改进的模糊概念图中,用模糊集合表示概念图中的模糊概念和模糊关系,并将模糊概念的所指域同模糊集合合并,减少信息冗余。根据改进的模糊概念图,重点研究了模糊概念图的匹配推理机制,设计了基于语义约束的匹配推理算法,并定量分析了算法的时间复杂度和空间复杂度。经过在《计算机文化基础》课程中实验测试,算法反映了考生主观题的答卷情况,同人工阅卷结果基本一致。     

数字化图书馆知识共享策略研究

数字化图书馆知识共享,有利于提高图书馆的竞争力、促进隐性知识显性化、降低图书的投资成本.促进数字化图书馆知识共享需要多措并举：营造图书馆知识共享的文化氛围,建立图书馆知识共享联盟,搭建有利于知识共享的运作平台.     

Frame知识的不一致性分析与修正

知识分析在知识库创建过程中十分重要,而知识的不一致分析是知识分析中的重要内容之一。基于框架表示法,分析了多种不一致的情况,并提出了相应的处理策略,用以检测和修正不一致的知识,以提高所获取知识的精度。Visual C++环境下的实验结果表明所提出的方法是可靠和可行的,为知识库的创建奠定了基础。     

Representing temporal knowledge in conceptual graphs

This study was motivated by some difficulties encountered by the authors when trying to express temporal knowledge using Sowa's conceptual graph (CG) approach. An overview of Sowa's approach is given and the difficulties encountered when trying to model temporal knowledge are outlined: the disparity of notations allowed by CG theory for expressing temporal information; the ambiguity and incompleteness of tense sspecification; the difficulty of harmonizing tenses and intergraph temporal relations. Various approaches suggested for representing time both in artificial intelligence and linguistics are presented, and an extension to Sowa's approach is proposed in which temporal and nontemporal knowledge are differentiated. In this model points in time are represented as well as time intervals. A semantic interpretation of verbs is provided based on an extension of Reichenbach's model of temporal markers. The authors show how their approach enables the representation of tenses as well as the aspectual properties of natural language sentences.     

Multiple-vector user profiles in support of knowledge sharing

This paper describes an algorithm to automatically construct expertise profiles for company employees, based on documents authored and read by them. A profile consists of a series of high dimensional vectors, each describing an expertise domain, and provides a hierarchy between these vectors, enabling a structured view on an employee’s expertise. The algorithm is novel in providing this layered view, as well as in its high degree of automation and its generic approach ensuring applicability in an industrial setting.The profiles provide support for several knowledge management functionalities that are difficult or impossible to achieve using existing methods. This paper in particular presents the initialization of communities of practice, bringing together both experts and novices on a specific topic. An algorithm to automatically discover relationships between employees based on their profiles is described. These relationships can be used to initiate communities of practice. The algorithms are validated by means of a realistic dataset.     

有限理性下知识团队知识共享的演化博弈分析

为提高知识团队成员共享知识的意愿,通过运用演化博弈论,对知识团队知识共享的内在机理与动态演化过程进行了分析.结果表明,影响知识团队知识共享的因素有知识员工的知识拥有量差距、风险系数、知识共享度、互补性知识比例、激励系数、协同性影响程度等,通过调整这些参数的大小,可以有效提高知识团队成员选择知识共享策略的概率.     

一个基于本体主题的中文知识获取方法

中文语言自身的特点决定了从中文自然语言文档中获取知识是非常困难的。尽管目前对中文的命名实体识别(简称为NER)已经取得了较好的效果,但是如果不借助同义词表或者类似WordNet的中文语言知识库,几乎无法正确地抽取已经识别出的实体之间的关系。文章提出了一个基于本体主题的思想进行中文知识获取的方法,该方法首次将主题思想引入领域本体,由领域专家对原始的领域本体中的概念和属性按照主题进行划分,建立起概念到主题、主题到属性的关联关系。在对一句话进行知识抽取时,通过简单的NER和直接与本体映射的方法可以识别出一句话中的部分概念、个体和属性,利用这些准确识别出的信息可以判定该句话所属的主题;该主题则进一步提供了寻找关系的线索。初步的实验结果表明与没有利用主题信息的方法相比,该方法可以取得更好的召回率和准确率。     

Towards a cognitive foundation for knowledge representation

Abstract. Knowledge engineering, knowledge management and conceptual modelling are concerned with representing knowledge of business and organizational domains. These research areas use ontologies for knowledge representation. Ontologies are understood either in the philosophical sense as firm metaphysical commitments or in the looser sense of dictionaries or taxonomies.
This paper critically examines the understanding and use of ontologies and knowledge representation languages in information systems (IS) research and application. As ontologies are intended to be conceptualizations of a perceived reality, they should reflect the empirically observed reality. This motivates proposing psychology of language as a reference discipline for knowledge engineering and knowledge management. Natural language is argued to reflect the cognitive concepts we use to think about and perceive the world around us. These cognitive concepts are the relevant terms with which to structure and represent knowledge about the world.
Psychology of language can provide empirical justification for a particular set of concepts to represent knowledge. This paper draws on psycho-linguistic research to develop a proposal for a system of cognitive structures. This is argued to provide the relevant concepts on which to found knowledge representation schemata for knowledge engineering, knowledge management and conceptual modelling.     

基于场景及规则获取模板的知识获取方法

产生式规则作为知识库系统进行推理的常用的、可读性好的知识表示形式,在构建知识库系统时有极大的优越性.提出一种基于场景及规则获取模板的知识获取方法,并以某高分子复合材料的加工专家为知识获取对象.该方法通过分析、记录领域专家进行设计的过程、解决问题的过程和动作,将领域问题按层次细化为一系列子问题,并在子问题场景下结合场景模型及知识获取模板来获取规则性知识.采用该方法可以辅助领域专家在明晰领域知识结构的基础上,逐步挖掘领域中细粒度的规则性知识.     

基于多文本知识源的历史知识修正与获取

知识获取是知识工程中关键的一环,而从文本知识源中获取专业知识是一种重要而常用的途径。而不同文本知识源对同一对象描述往往有所不同,为了获取完备的高精度和细粒度的知识,给出了一种从多种文本知识源中获取历史知识的方法。其基本思想是：将不同文本知识源的知识自动翻译成框架知识;对描述相同对象的不同框架进行不一致性检测并修正;合并知识框架。实验表明,该方法能获取到高精度和细粒度的历史知识,是可行和有效的,并为下一步的知识服务奠定重要的基础。     

国内垂直领域知识图谱发展现状与展望

为了充分展现国内在垂直知识图谱领域研究的现状,以垂直领域知识图谱为研究对象对其发展现状和趋势进行综述.对垂直领域知识图谱的定义和分类、架构和关键技术的发展现状进行了详细论述;针对垂直领域知识图谱的具体应用进行了论述,并以学术信息知识图谱和医药卫生知识图谱为例进行了详细介绍.最后对垂直领域知识图谱发展中存在的问题和对策以及未来的趋势进行了探讨.     

A unified model of knowledge sharing behaviours: theoretical development and empirical test

Research and practice on knowledge management (KM) have shown that information technology alone cannot guarantee that employees will volunteer and share knowledge. While previous studies have linked motivational factors to knowledge sharing (KS), we took a further step to thoroughly examine this theoretically and empirically. We developed a unified model that is comprehensive and yet parsimonious, based on the decomposed theory of planned behaviour (DTPB) with three sets of critical antecedents: psychological, organisational and technological that are theorised to influence KS behaviours. Results of a field survey of knowledge workers support the majority of hypothesised relationships, and explained 41.3% of the variance in the actual KS behaviours and 60.8% of the variance in the intention to share knowledge. These results far exceed the predictive powers achieved by previous studies. Among our significant findings include a strong positive influence of perceived enjoyment in helping others (PEH) and a strong negative influence of perceived loss of knowledge power (PLK). Based on the findings, we discussed the study's implications for research and practice.     

KMS中用户知识共享行为模型的构建

用户知识共享行为是影响知识管理系统KMS（knowledge management systems,KMS）能否取得预期运行效果的关键因素。现有的一些KMS还不具有根据用户日常的知识活动自动评估用户知识共享行为的功能,限制了KMS在知识共享文化形成中的作用。在此从行为和动作的表示入手,基于BNF的行为表示体系、知识共享行为ontology和行为树映射方法,提出了一个动态、实时、自适应、交互式用户知识共享行为模型,该模型包括基于BNF的行为和动作的表示、知识共享行为ontology、用户知识共享行为树及其动态创建过程等内容,能够实现从数量上对用户知识共享的参与度做出评价,可以作为开发实现该功能的Agent程序的一般框架。     

Formal Ontology: Foundation of Domain Knowledge Sharing and Reusing

Domain analysis is the activity of identifying and representing the relevant information in a domain,so that the information can be shared and reused in similar systems.But until now,no efficient approaches are available for capturing and representing the results of domain analysis and then for sharing and reusing the domain knowledge.This paper proposes an ontology-oriented approach for formalizing the domain models.The architecture for the multiple-layer structure of the domain knowledge base is also discussed.And finally,some genetic algorithm-based methods have been given for supporting the knowledge sharing and reusing.     

Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

基于广义专家群思考模型的Web知识获取研究

将Web中面向特定问题的权威网页看作特殊专家针对问题的观点,特殊专家与人类专家一起构成了广义专家群体。如何获取与人类专家思维和认知状态相一致的来源于特殊专家群体的知识,是实现广义专家群体思维创新的关键问题之一。在广义专家群思考模型的基础上,提出基于意群的特殊专家知识获取方法,获得了能够较好反映特殊专家观点的信息和知识。并以产品设计创新过程中的特殊专家知识获取为例进行了验证。     

Individual characteristics influencing the sharing of knowledge on social networking services: online identity,self-efficacy,and knowledge sharing intentions

ABSTRACT

Social media have become an important place for individuals with similar interests to exchange opinions and share resources. Ubiquitous collaborative learning environments in SNS have great educational and organisational potential and offer a platform for sharing knowledge. Rising interest in personal factors in knowledge management research calls for a better understanding of how knowledge is shared in SNS-based collaborative learning. Grounded on the theories of planned behaviour and social identity, this study investigates individual characteristics that affect the sharing of knowledge in SNS learning communities. The study also examines whether intent to share knowledge mediates those personal characteristics and sharing of knowledge. Eighty individuals from five active Facebook groups participated in this study. The findings indicate that individual characteristics, such as online personal identity, web-specific self-efficacy, and knowledge-creation self-efficacy significantly predict sharing of knowledge. In addition, intent to share knowledge mediates between the sharing of knowledge and online identity, web-specific self-efficacy, or knowledge-creation self-efficacy. The results reveal a need for customised support and environmental design focusing on online personal identity, web-specific self-efficacy, knowledge-creation self-efficacy, and intent to share knowledge in a knowledge-sharing community on SNS. Implications for the SNS communities and suggestions for further research are discussed.