Enhancing Embedding-Based Chinese Word Similarity Evaluation with Concepts and Synonyms Knowledge

Word similarity(WS)is a fundamental and critical task in natural language processing.Existing approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality corpus.However,it may suffer from poor performance for insufficient corpus in some specific fields,and cannot capture rich semantic and sentimental information.To address these above problems,we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge,namely EWS-CS model,which can provide extra semantic information to enhance word similarity evaluation.The core of our approach contains knowledge encoder and word encoder.In knowledge encoder,we incorporate the semantic knowledge extracted from knowledge resources,including character-word concepts,synonyms and sentiment lexicons,to obtain knowledge representation.Word encoder is to learn enhancing embedding-based word representation from pre-trained model and knowledge representation based on similarity task.Finally,compared with baseline models,the experiments on four similarity evaluation datasets validate the effectiveness of our EWS-CS model in WS task.

Effect of hemovigilance education on nursing students’knowledge:the application of a conceptual map

Objective:Given the importance of having knowledge on the hemovigilance process in nursing care,the present study was conducted to investigate the effect of the hemovigilance education on nursing students’knowledge using a conceptual map.The current research was a semi-experimental study.Methods:The samples consisted of 60 nursing students who were selected based on the inclusion criteria using the census sampling method.Thereafter,these participants were randomly assigned into the two groups:experimental and control groups.The required data were collected before,immediately after,and 1 month after the education using the hemovigilance knowledge questionnaire.Accordingly,the validity of this questionnaire was confirmed,and its reliability using the Cronbach’s alpha coefficient was reported as 0.83.The education process was conducted during a 4-week period.Thereafter,the collected data were analyzed using descriptive and inferential statistics by SPSS v25.Results:The results reveal that a significant difference existed between the knowledge scores of students in the experimental group compared with that of the control group during different times(including before,immediately after,and 1 month after the education)(P<0.0001).Of note,hemovigilance education had a significant effect on the students’knowledge(P<0.0001).Conclusions:Due to the effect of the hemovigilance education on the students’knowledge and by applying the conceptual map in the easy transfer of the educational concepts,it is recommended that the results of the present study be used to strengthen the theoretical and clinical education of nursing students.

Knowledge Automatic Indexing Based on Concept Lexicon and Segm-entation Algorithm

This paper is based on two existing theories about automatic indexing of thematic knowledge concept. The prohibit-word table with position information has been designed. The improved Maximum Matching-Minimum Backtracking method has been researched. Moreover it has been studied on improved indexing algorithm and application technology based on rules and thematic concept word table.

基于Knowledge-Analysis的军事知识联通研究

军事专业知识在军事决策系统、军事智能教学系统、军事知识查询系统等高技术产品中具有重要的作用。知识库是机器智能的重要来源,所以有必要建立一个完善、协调、具有良好联通性的大型军事知识库,而大量实践证明计算机真正具有智能不仅需要大型知识库,而且更需要知识间广泛的联通。本文主要讨论军事概念联通。所谓概念联通是指通过关系链或属性或属性值将两个概念联系起来的过程。概念联通的目的之一就是通过概念联通帮助发现不同知识源的知识之间的不一致性,尽可能保证所获取知识的完备性和精确性,建立一个大型的军事专业知识库。

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Concept-based approach for information retrieval

A concept-based approach is expected to resolve the word sense ambiguities in information retrieval and apply the semantic importance of the concepts, instead of the term frequency, to representing the contents of a document. Consequently, a formalized document framework is proposed. The document framework is used to express the meaning of a document with the concepts which are expressed by high semantic importance. The framework consists of two parts： the ＂domain＂ information and the ＂situation ＆ background＂ information of a document. A document-extracting algorithm and a two-stage smoothing method are also proposed. The quantification of the similarity between the query and the document framework depends on the smoothing method. The experiments on the TREC6 collection demonstrate the feasibility and effectiveness of the proposed approach in information retrieval tasks. The average recall level precision of the model using the proposed approach is about 10% higher than that of traditional ones.

基于Concept-Relation模型的军事文本知识获取

介绍一种从《中国大百科全书》文本中获取军事知识的方法 .该方法由 2部分组成 :第一部分是军事知识描述语言 (MKDL) ,由知识工程师用于将文本知识翻译成基于Concept_Relation_模型的MKDL形式化文本知识 ;第二部分是MKDL编译系统 .该系统通过对MKDL文本的编译、分析和知识连接而最后生成军事知识库 .

Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia

To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.

Concept Lattice Construction through the Composition and Decomposition of Formal Contexts

The purpose of this paper is to study the construction of concept lattice from variable formal contexts.Composition and decomposition theories are proposed for the unraveling of concept lattice from contexts with variable attribute set in the process of information updating.The relationship between the extension sets of the original context and that of its sub-context is analyzed.The composition and decomposition theories are then generalized to the situation involving more than two sub-contexts and the situation with variable attribute set and object set.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

面向知识结构分析的模糊概念格模型

知识空间理论使用数学语言对学习者进行知识评价与学习指导,属于数学心理学的研究范畴.技能与问题是构成知识空间的两个基本要素,深入研究两者之间的关系是知识状态刻画与知识结构分析的内在要求.在当前的知识空间理论研究中,没有明确建立技能与问题之间的双向映射,从而难以提出直观概念意义下的知识结构分析模型,也没有明确建立知识状态之间的偏序关系,不利于刻画知识状态之间的差异,更不利于规划学习者未来的学习路径.此外,现有的成果主要集中在经典的知识空间,没有考虑实际问题中数据的不确定性.为此,将形式概念分析与模糊集引入知识空间理论,建立面向知识结构分析的模糊概念格模型.具体地,分别建立知识空间与闭包空间的模糊概念格模型.首先,建立知识空间模糊概念格,并通过任意两个概念的上确界证明所有概念的外延构成知识空间.引入粒描述的思想定义技能诱导的问题原子粒,由问题原子粒的组合判定一个问题组合是否是知识空间中的一个状态,进而提出由问题组合获取知识空间模糊概念的方法.其次,建立闭包空间模糊概念格,并通过任意两个概念的下确界证明所有概念的外延构成闭包空间.类似地,定义问题诱导的技能原子粒,由技能原子粒的组合判定一个技能组合是否是闭包空间中某一知识状态所需的技能,进而提出由技能组合获取闭包空间模糊概念的方法.最后,通过实验分析问题数量、技能数量、填充因子以及分析尺度对知识空间与闭包空间规模的影响.结论表明知识空间模糊概念不同于现有的任何概念,也不能从其他概念派生而来.闭包空间模糊概念本质上是一种面向属性单边模糊概念.在二值技能形式背景中,知识空间与闭包空间中的状态具有一一对应关系,但这种关系在模糊技能形式背景中并不成立.

知识、想象力与深度学习——与吉莉安·贾德森教授关于基兰·伊根的研究的对话

20世纪80、90年代以来,加拿大西蒙菲莎大学基兰•伊根教授带领团队开展了“富有想象力的教育”“深度学习研究项目”等研究,系统地探讨了课程知识、学习、理解、想象与想象力的本质,以及知识理解的本质与类型、知识与想象力的关系,开发了想象力培养的认知工具,提出了在学科教学中培育学生想象力的教学策略。在深度学习研究中,建立了深度学习的知识标准和教学基本准则。2006年以来,针对我国中小学课程实施中存在的问题,我们组建了深度教学研究团队。围绕教学价值观、课程知识观、教学过程观和学习活动观的转变,采取3K研究方式,探讨了指向核心素养的教与学的理念与策略,并开展了国际合作研究。围绕基兰•伊根教授关于知识、想象力与深度学习研究等方面的创新贡献,对话和访谈西蒙菲莎大学吉莉安•贾德森教授,以期为我国深化课程与教学改革,破解核心素养发展的难题提供借鉴。

中国概念的知识再造与全球旅行:国际传播视角下的概念地理

作为话语基础性要素的概念,由于带有高度凝练性、抽象性等特点,是人类认识世界的锚,也是文化交流的元话语,构成了当下国际传播的底层逻辑。从本质来看,国际传播的过程就是产生自不同地理空间之概念的交流与竞争的过程。中国学界的百年历程,经历了吸纳西方概念、依赖西方概念的过程,但今日逐渐“文化觉醒”,并且重视自身的概念供给。在“概念地理”的视野看来,经历知识再造的中国概念如何出海,成为中国对外传播的核心话语,助力于当下的国际传播实践,将是本文关注的议题所在。

单边区间集模糊半概念的知识表示及提取方法

为了使“区间”形式加以表述的不确定信息的提取具有侧重性,需提取出对象(属性)集对应的属性(对象)区间集。本文在模糊形式背景中,通过引入2个阈值,将单边区间集与经典半概念结合,提取出属性(对象)集对应的对象(属性)区间集,从而提出区间集外延–集合内涵(集合外延–区间集内涵)(interval set extent-set intent(set extent-interval set intent),ISE-SI(SE-ISI))型单边区间集模糊半概念。全体ISE-SI(SE-ISI)型单边区间集模糊半概念构成格,并给出基于格搜寻全体ISE-SI(SE-ISI)型单边区间集模糊半概念的算法。通过与已有成果对比,显示出这2种知识表示形式的多方优势。本文所得结果在知识表示及提取方法上具有适用范围广、实际应用强等优点。

标识性概念与中国体育学自主知识体系构建

把握中国自主知识体系重大命题,对于我国体育学自主知识体系建设具有深刻指导意义。研究认为,元概念及其裂变衍生是体育学知识体系化的原点与驱动力,体育学知识体系的完善助推标识性概念的产生,而标识性概念的发展、整合、传播则为中国体育学自主知识体系建设提供基础动力。然而,由于当前体育学的元概念模糊、移植借鉴多元化、对母学科依附性强、学科设置欠佳,导致我国体育学标识性概念发展面临内生性不足、自给意识弱、创造动力差与衍生难度大等问题,限制了我国体育学自主知识体系的自立根基、自主起点、自决水平与自我整合。对此,应在锚定标识性概念基本建设方位的基础上,植根中国优秀文化提炼标识性概念,聚焦具身实践特质创造标识性概念,依托学术共同体整合标识性概念,回应社会需求创新标识性概念,继而提升我国体育学自主知识体系的主体性、本源性、独立性、系统性与引领性。

学科演变视角下图书馆学概念知识元的识别

文章以学科演变为视角,构建一个可用于学科演进及概念历史演变研究的概念知识元识别模式。选择图书馆学相关词表、专业工具书和期刊论文关键词为语料,通过概念知识元获取和整合,识别其等级关系、等同关系、相关关系;运用模式匹配法、相似度计算、类号中介法3种方法识别其历史演变关系,得到10个可反映我国图书馆学概念知识元演变趋势的概念集合并构建我国图书馆学概念知识元库。文章所构建的学科概念知识元库含有历史演变关系,可以实现检索语言的扩充,拓展信息内容揭示的维度,在学科知识服务中方便学科用户了解概念知识元的“前世今生”、实现特定主题历史文献的聚合。

高等教育自主知识体系建设的逻辑向度与实现路径——基于中国式现代化的分析框架

党的二十大报告对中国式现代化进行了系统的多维解读,初步建构起中国式现代化思想理论体系。自主知识体系建设作为新时代高等教育现代化的重要实践,离不开中国式现代化的价值指引和经验素材。从马克思主义实践观看,高等教育自主知识体系与中国式现代化思想理论体系具有目标耦合、关系耦合、工具耦合三重内在耦合关系。基于耦合分析,发现高等教育自主知识体系建设的价值逻辑表征为从高等教育现代化到人的现代化,理论逻辑表征为以马克思主义指导知识体系现代化,实践逻辑表征为以数字技术赋能自主知识体系建设。加速推进高等教育自主知识体系建设进程,一是完善育才体系,优化知识创新的人才结构和布局;二是坚持系统观念,注重提取标识性概念和完善理论构建;三是把握数字革命,重塑高等教育学科与学术生态。

指向人的内在品质刻画的计算思维框架构建

计算思维作为培养目标,应该指向人的内在品质。为此,文章借鉴胡塞尔认识论思想,在认识发生的视野中分析人的构造过程,形成“它—我”结构,即“知识—素养”结构,以此解释概念与观念的根本相关性。借鉴皮亚杰两个范畴说说明概念与观念的关联是在思维过程中实现的,借鉴布伦南计算思维的框架结构及布鲁姆认知目标分类理论中关于应用的强调,最终给出结构与应用兼备的指向人的内在品质的包括概念(基本概念、核心概念、大概念)、思维(关联、抽象、系统化)、观念(局部、聚合、全局)的计算思维框架。

面向动态学习环境的自适应学习路径推荐模型

自适应学习路径作为实现个性化学习的一项关键技术,受到研究者广泛关注。近年来,强化学习成为自适应学习路径推荐的主流方法,但在动态学习环境表征的完整性和学习路径的适应性方面仍存在不足。基于此,文章提出了融合领域知识特征的自适应学习路径推荐模型。首先,模型将知识点概念覆盖和难度两个特征引入动态学习环境中,使对动态学习环境的表征更完整。其次,采用深度强化学习算法实现学习路径的推荐,提升学习路径的适应性。最后,开展技术对比实验和应用实验。技术对比实验表明,该模型提高了学习路径的有效性和适应性。应用实验表明,该模型可以准确地判断学习者的薄弱知识点概念,并能为学习者推荐适合其认知特征的自适应学习路径。

基于事件思维的课程知识观及知识教学

事件哲学的兴起引发了对课程教学复杂性、创生性、情境性、实践性等特征的综合考量,符合素养导向的课程教学改革阶段对知识教学和学习的诉求。建立在理性主义认识传统和实体性思维理解基础上的传统知识观容易陷入知识与主体割裂、与生活分离、与实践脱节的知识学习困境。根据事件哲学理论,基于事件思维的课程知识观强调知识主体的介入,关注知识的境遇性和知识的实践性特征,建立知识与学生发展的内在关联而赋予知识以生命和意义。在知识教学过程中,关注事件就需要立足知识产生、发展、习得的过程本身,重视学生亲知的学习过程,关注生成性和偶然性事件的积极价值,经历真实涌现的、复杂境遇下的问题解决学习,最终完成师生共同演绎的事件旅程。