Modeling degrees of conceptual overlap in Semantic Web ontologies
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《多模态翻译理论与实践研究》读书笔记目录一、内容综述 (2)1. 研究背景与意义 (2)2. 翻译学科的发展趋势 (4)二、多模态翻译理论 (5)1. 多模态翻译的定义 (6)2. 多模态翻译的理论框架 (7)a. 结构主义视角 (8)b. 功能主义视角 (9)c. 概念式翻译学视角 (11)3. 多模态翻译的理论模型 (12)a. 以翻译为中心的模型 (13)b. 以信息为中心的模型 (14)c. 综合性模型 (16)三、多模态翻译实践 (17)1. 多模态翻译的应用领域 (18)a. 文化交流与传播 (20)b. 语言教学 (20)c. 人工智能辅助翻译 (22)2. 多模态翻译的案例分析 (23)a. 跨文化交际中的多模态翻译 (25)b. 语言教学中的多模态翻译实践 (26)c. 人工智能辅助翻译系统的设计与应用 (27)四、多模态翻译的挑战与对策 (28)1. 技术挑战 (29)a. 自然语言处理技术 (31)b. 计算机辅助翻译工具 (32)2. 理论挑战 (33)a. 翻译理论的创新与发展 (34)b. 多模态翻译的哲学思考 (36)3. 实践挑战 (37)a. 教育培训的需求分析 (38)b. 企业跨文化交流与合作 (39)五、结论 (40)1. 研究成果总结 (41)2. 研究展望与建议 (42)一、内容综述《多模态翻译理论与实践研究》是一本深入探讨多模态翻译理论和实践的学术著作。
本书通过对多模态翻译的全面分析,揭示了其在跨文化交流中的重要作用和广阔的应用前景。
在内容综述部分,作者首先回顾了多模态翻译的基本概念和理论框架,包括多模态翻译的定义、特点、分类以及多模态翻译的理论基础等。
作者详细介绍了多模态翻译在不同领域的应用实践,如翻译学术文献、广告语、影视作品等,并分析了多模态翻译所面临的挑战和问题,如跨文化交际障碍、语言结构差异等。
作者还探讨了多模态翻译与语义翻译、交际翻译等翻译理论的关系,强调了多模态翻译在促进跨文化交流、增进不同文化理解和尊重方面的重要作用。
Geometric ModelingGeometric modeling is a fundamental concept in computer graphics and design, playing a crucial role in various industries such as architecture, engineering, and entertainment. It involves creating digital representations of physical objects or environments using mathematical and computational techniques. Geometric modeling allows designers and engineers to visualize, analyze, and manipulate complex shapes and structures, leading to the development of innovative products and solutions. However, it also presents several challenges and limitations that need to be addressed to ensure its effectiveness and efficiency. One of the key challenges in geometric modeling is the accurate representation of real-world objects and environments. This requires the use of advanced mathematical algorithms and computational methods to capture the intricate details and complexities of physical entities. For example, creating a realistic 3D model of a human face or a natural landscape involves precise measurements, surface calculations, and texture mapping to achieve a lifelike appearance. This level of accuracy is essential in industries such as animation, virtual reality, and simulation, where visual realism is critical for creating immersive experiences. Another challenge in geometric modeling is the efficient manipulation and editing of geometric shapes. Designers and engineers often need to modify existing models or create new ones to meet specific requirements or constraints. This process can be time-consuming and labor-intensive, especially when dealing with large-scale or highly detailed models. As a result, there is a constant demand for more intuitive and user-friendly modeling tools that streamline the design process and enhance productivity. Additionally, the interoperability of geometric models across different software platforms and systems is a persistent issue that hinders seamless collaboration and data exchange. Moreover, geometric modeling also faces challenges in terms of computational resources and performance. Generating and rendering complex 3D models requires significant computing power and memory, which can limit the scalability and accessibility of geometric modeling applications. High-resolution models with intricate geometries may strain hardware capabilities and lead to slow processing times, making it difficult for designers and engineers to work efficiently. This is particularly relevant in industries such as gamingand virtual reality, where real-time rendering and interactive simulations are essential for delivering engaging and immersive experiences. Despite these challenges, geometric modeling continues to evolve and advance through technological innovations and research efforts. The development of advanced modeling techniques such as parametric modeling, procedural modeling, and non-uniform rational B-spline (NURBS) modeling has significantly improved the accuracy and flexibility of geometric representations. These techniques enable designersand engineers to create complex shapes and surfaces with greater precision and control, paving the way for more sophisticated and realistic virtual environments. Furthermore, the integration of geometric modeling with other disciplines such as physics-based simulation, material science, and machine learning has expanded its capabilities and applications. This interdisciplinary approach allows for the creation of interactive and dynamic models that accurately simulate physical behaviors and interactions, leading to more realistic and immersive experiences. For example, in the field of architecture and construction, geometric modeling combined with structural analysis and environmental simulation enables the design and evaluation of sustainable and resilient buildings and infrastructure. In conclusion, while geometric modeling presents several challenges and limitations, it remains an indispensable tool for innovation and creativity in various industries. The ongoing advancements in geometric modeling techniques and technologies continue to push the boundaries of what is possible, enabling designers and engineers to create increasingly realistic and complex digital representations of the physical world. As computational power and software capabilities continue to improve, the future of geometric modeling holds great promise for revolutionizing the way we design, visualize, and interact with the world around us.。
高等代数与解析几何陈跃的英语专业词汇Delving into the Realms of Higher Algebra and Analytic Geometry: Exploring the English Vocabulary LandscapeThe study of higher algebra and analytic geometry encompasses a vast and intricate tapestry of concepts, principles, and terminology that form the foundation of advanced mathematical reasoning. As an English language learner exploring these domains, navigating the richlexical landscape can present both exhilarating opportunities and formidable challenges.Let us embark on a journey to unravel the nuances and complexities of the English vocabulary associated with higher algebra and analytic geometry. This endeavor will not only broaden our understanding of these mathematical disciplines but also cultivate a deeper appreciation for the precision and elegance inherent in the language used to articulate them.At the heart of higher algebra lies the exploration ofabstract algebraic structures, such as groups, rings, and fields. The very nomenclature of these constructs, with terms like "homomorphism," "isomorphism," and "subgroup," demands a keen grasp of the English language to comprehend their intricate relationships and underlying principles. The fluent articulation of these concepts is paramount, as it enables seamless communication within the mathematical community and facilitates the exchange of ideas and advancements.Analytic geometry, on the other hand, seamlessly integrates the realms of algebra and geometry, giving rise to a rich tapestry of linguistic expressions. From the precise delineation of conic sections, including ellipses, parabolas, and hyperbolas, to the elegant formulations of vector spaces and transformations, the English vocabulary serves as a vital conduit for conveying the visual and abstract nature of these geometric constructs.Navigating the intricacies of this specialized discourse demands not only a solid grasp of mathematical concepts but also a nuanced understanding of the English language. Theability to fluidly transition between the symbolic representations of equations and the descriptive power of words is a hallmark of mathematical proficiency. This versatility allows for the clear and concise articulationof complex ideas, enabling effective collaboration,problem-solving, and the dissemination of knowledge.Moreover, the English vocabulary employed in higher algebra and analytic geometry often exhibits a unique blend of technical terminology and linguistic elegance. Terms like "eigenvalue," "eigenvector," and "orthogonal" seamlessly integrate Greek and Latin roots, showcasing the richheritage and global influences that have shaped thelanguage of mathematics. Mastering the pronunciation, spelling, and contextual usage of these specialized termsis essential for ensuring clear communication and avoiding misunderstandings.Beyond the technical lexicon, the English language also plays a pivotal role in the formulation of mathematical proofs, theorems, and conjectures. The precise and logical flow of ideas, the careful use of modifiers and quantifiers,and the ability to navigate the nuances of mathematical notation all contribute to the effective expression of mathematical reasoning. Developing a command of these linguistic skills is paramount for success in the realm of higher algebra and analytic geometry.As we delve deeper into the interplay between English and the realms of higher algebra and analytic geometry, we must also acknowledge the importance of maintaining conceptual clarity. The English language, with its inherentflexibility and contextual ambiguity, can sometimes present challenges in conveying the unambiguous and rigorous nature of mathematical thinking. Navigating this balance, where the precision of mathematical concepts is harmonized with the expressive power of the English language, is a hallmark of true mastery.In conclusion, the exploration of the English vocabulary associated with higher algebra and analytic geometry is a multifaceted endeavor that encompasses not only the technical terminology but also the broader linguisticskills required for effective communication and reasoningwithin the mathematical domain. By embracing this challenge, we not only deepen our understanding of these captivating mathematical disciplines but also cultivate a versatile command of the English language that transcends the boundaries of academia, ultimately empowering us to engage with the world of mathematics with confidence and clarity.。
made in terms of three levels -回复题目:三个层次的维度,解析现代社会的发展与变革导语:现代社会的发展与变革是一个复杂而又庞大的课题,我们可以从三个层次的维度来深入剖析。
这三个层次分别是个人层面、组织层面和全球层面。
通过对这三个层次的分析,我们可以更好地理解和解决现代社会发展中所面临的诸多问题。
第一层次:个人层面的发展与变革在个人层面,社会的发展与变革主要以个体的成长和变化为基础。
首先,个人的教育和培训是推动社会发展的重要因素。
现代社会对人才的需求日益增长,个人必须通过学习和不断提升自己的能力,才能适应社会的发展和变化。
其次,个人的意识和价值观的转变也是社会发展与变革的重要方面。
现代社会对公平、正义、环境保护等价值观念的追求,需要个人逐步转变其传统的观念和思维方式,以适应当代社会的需求。
第二层次:组织层面的发展与变革在组织层面,社会的发展与变革主要取决于企业和组织的发展和变革。
首先,企业的创新能力是推动社会发展的关键。
现代社会对科技、产业和管理等各个方面的创新能力有着越来越高的要求,只有不断创新才能在激烈的市场竞争中取得优势。
其次,企业的社会责任也是社会发展与变革的重要方面。
现代社会对企业的社会责任要求越来越高,企业需要认识到自己与社会的相互关系,积极履行社会责任,为社会做出积极的贡献。
第三层次:全球层面的发展与变革在全球层面,社会的发展与变革是全球化进程的产物。
首先,全球化使得不同国家和地区之间的联系更加紧密,推动了信息、人员和资金的流动,加速了社会的发展与变革。
其次,全球层面上的合作和治理也对社会的发展与变革起到重要作用。
在全球化的背景下,各国需要加强合作,共同应对全球性的挑战,制定并实施更加有效的全球治理措施。
结语:通过对个人层面、组织层面和全球层面的发展与变革进行分析,我们可以更加深入地理解现代社会的发展动力和变革机制。
只有站在不同层次的视角上,我们才能更好地应对现代社会发展中面临的挑战,推动社会的持续进步与繁荣。
相同点英文作文英文回答:When it comes to thinking about the similarities between two different topics, it is important to consider the context and the specific aspects being compared. In general, there are a few key areas where different topics can overlap and share common ground.Conceptual Overlap:One potential area of similarity is conceptual overlap. This occurs when two topics share underlying ideas or principles. For example, both psychology and sociology study human behavior, albeit from different perspectives. They may share concepts such as motivation, cognition, and social interactions.Methodological Similarities:Another area of similarity can be found in methodological approaches. Certain research methods and techniques can be applicable across different fields. For instance, statistical analysis is used in both economics and biology to draw inferences from data. This shared methodology allows researchers to employ similar tools and techniques to solve problems in their respective disciplines.Interdisciplinary Connections:Furthermore, interdisciplinary connections can lead to similarities between topics. When different fields collaborate and exchange ideas, they can identify commonalities and develop new insights. For example, the field of bioinformatics combines biology and computer science to analyze biological data. This interdisciplinary approach creates a new area of research that shares characteristics from both parent disciplines.Historical Influences:Historical influences can also contribute tosimilarities between topics. As knowledge and ideas evolve over time, they can spread across different disciplines and shape their development. For instance, the influence of Newtonian physics on engineering and astronomy has led to shared concepts and principles in these fields.Common Applications:Finally, topics may share similarities based on their practical applications. For example, both marketing and public relations aim to communicate messages to specific audiences. Despite their different purposes, they may employ similar strategies and techniques to achieve their goals.中文回答:共同点:概念重叠,不同学科可能共享底层思想或原理。
小学上册英语第3单元期末试卷英语试题一、综合题(本题有100小题,每小题1分,共100分.每小题不选、错误,均不给分)1.What is the sound a dog makes?A. MeowB. BarkC. RoarD. Hiss2.Learning about plants can inspire ______ (环保) efforts.3.What is the term for a young fox?A. CubB. KitC. PupD. Calf答案:B Kit4.What do you call the person who writes books?A. AuthorB. EditorC. PublisherD. Journalist答案:A5.I can _____ my shoes by myself. (put on)6.What do we call a song that tells a story?A. PoemB. BalladC. NovelD. Play答案:B7.The _______ (The Harlem Renaissance) was a cultural movement in the 1920s.8.The movie is ______ (based) on a true story.9.The ________ is a famous painting by Leonardo da Vinci.10.What is the name of the ancient civilization that built pyramids in Egypt?A. MayansB. AztecsC. EgyptiansD. Greeks答案:C Egyptians11.What is the opposite of "hot"?A. WarmB. ColdC. CoolD. Spicy12.Which season comes after winter?A. SpringB. SummerC. FallD. Autumn13.The tree has green ________.14.The ________ chirps in the morning.15.The tropical fish in aquariums come in various ________________ (颜色) and patterns.16.My sister loves __________ (动物).17.I enjoy playing musical instruments. I play __________.18.What do we call a person who studies the weather?A. MeteorologistB. ClimatologistC. GeologistD. Astronomer19.I saw a ladybug on a ______.20.The clock says it is ________ (三点).21.I like eating ________ (chocolate) cake.22.ts are _____ (多年生) and come back every year. Some pla23.What is the capital of Brazil?A. Rio de JaneiroB. BrasíliaC. São PauloD. Salvador答案:B24.What do we call the process of writing down thoughts or ideas?A. DrawingB. PaintingC. WritingD. Sculpting答案:C25.The _____ (fish/bird) is swimming.26. A polymer is a large molecule made up of many ______ units.27.I saw a _______ (小猩猩) swinging from tree to tree.28. A plateau is an elevated flat ______.29.The bird builds a nest in the _______ (鸟在_______中筑巢).30. A ______ reaction releases energy, often as heat or light.31.What do we call the central part of an atom?A. ElectronB. ProtonC. NucleusD. Neutron答案:C32.What do plants need to grow?A. LightB. WaterC. SoilD. All of the above答案:D33.My dog wags its ______ (尾巴) when happy.34.The study of chemicals and their reactions is called ______.35.What is the name of the famous Greek philosopher who taught Alexander the Great?A. PlatoB. SocratesC. AristotleD. Epicurus答案:C36.I enjoy _______ (培养兴趣) in different subjects.37.The girl loves to ________.38.The main gas produced during respiration is __________.39.The ancient Greeks made significant advancements in ________ (医学).40.The process of condensation occurs when a gas turns into a __________.41.Which shape has four equal sides?A. RectangleB. TriangleC. SquareD. Circle42.What is the capital of Brunei?A. Bandar Seri BegawanB. Kuala BelaitC. TutongD. Seria答案:A43.What is the name of the famous wizarding school in Harry Potter?A. HogwartsB. NarniaC. Middle-earthD. Oz44. A __________ is a famous city for its festivals.45.The ______ is a measure of how far light travels in one year.46. A __________ is a famous site for camping.47.The _______ is where the roots grow.48.The ______ (植物生长) cycle is fascinating.49.I like to ride my ______ (horse).50.I saw a _______ (小鸟) building a nest.51. A __________ is the measure of how much matter is in an object.52.The _____ (peony) blooms in late spring.53.The ______ (植物的适应机制) is a subject of study.54.How do you say "thank you" in Japanese?A. GraciasB. MerciC. ArigatoD. Danke55.The _______ of a tree is called its trunk.56.The cake is _____ (delicious/yummy).57.My favorite toy is a ________ (拼图). I enjoy putting the pieces ________ (在一起).58.The Milky Way is just one of billions of _______ in the universe.59.I want to be a _______ when I grow up.60.My family goes camping in the ______.61.What is the opposite of "hot"?A. WarmB. ColdC. CoolD. Mild答案:B62.What do you call the female counterpart of a rooster?A. HenB. DuckC. GooseD. Chicken答案:A63.What is the capital of Kyrgyzstan?A. BishkekB. TashkentC. DushanbeD. Almaty答案:A64.I love ________ on the beach.65.The invention of ________ has revolutionized personal communication.66.The fish swims in the ______ (池塘). It is very ______ (活泼).67.My _____ (奶奶) makes delicious cookies.68.Shadows are formed when light is ______ (blocked).69.The Earth's crust is constantly being shaped by ______ forces.70.What is the main ingredient in popcorn?A. WheatB. CornC. RiceD. Barley答案:B71.In a polymerization reaction, monomers join together to form _____.72. A rabbit can hop across the ______ (草地).73.The peacock displays its feathers to attract ________________ (配偶).74.My dad takes us to ____.75.My dad is a __________ (商业分析师).76.The __________ (历史的说明) clarifies misconceptions.77.I have a special ________ that shines in the dark.78.The capital city of Djibouti is ________ (吉布提的首都城市是________).79.What is the color of an eggplant?A. RedB. PurpleC. GreenD. Yellow答案:B80.What is the name of the fairy tale character who had long hair?A. CinderellaB. RapunzelC. Snow WhiteD. Belle答案:B81. A _____ is a fun toy to ride.82. A molecule made of two identical atoms is called a _______ molecule.83.I saw a ________ flying over the lake.84.The puma is also called a _________ (美洲狮).85.How many colors are there in a rainbow?A. FiveB. SixC. SevenD. Eight86.The concept of "light years" helps us measure distances in _______.87.The capital city of Vietnam is ________ (河内).88.I like to ride my _______ (我喜欢骑我的_______).89.What do you call a baby cat?A. PuppyB. KittenC. CubD. Foal90.The ancient Romans spoke ________.91.My cat enjoys chasing ______ (小虫) in the sunlight.92.What is the name of the famous singer known as the "King of Pop"?A. Elvis PresleyB. Michael JacksonC. Freddie MercuryD. Prince答案:B93.What is the longest river in the world?A. AmazonB. NileC. MississippiD. Yangtze答案:B94.What do we call a baby dog?A. KittenB. PuppyC. CalfD. Chick95.An insulator does not allow ______ to pass through.96.Which animal can fly?A. ElephantB. DogC. BirdD. Fish答案:C97.What is the capital of Uganda?A. KampalaB. EntebbeC. JinjaD. Gulu98.Which fruit is yellow and curved?A. AppleB. BananaC. GrapeD. Orange答案:B99.How many days are in February during a leap year?A. 28B. 29C. 30D. 31100. A volcano that has not erupted in a long time is called a ______ volcano.。
I。
Put the following English terms into Chinese. (1'×10=10’)所指对象referent所指论Referential theory专有名词 proper name普通名词 common nouns固定的指称记号 rigid designators指称词语deixical items确定性描述语definite descriptions编码时间 coding—time变异性variability表示反复的词语 iterative表述句 constative补救策略redressive strategies不可分离性 non—detachability不确定性indeterminacy不使用补救策略,赤裸裸地公开施行面子威胁行bald on record without redressive actions 阐述类言语行为 representatives承诺类言语行为 commissives指令类言语行为directives表达类言语行为expressives,宣告类言语行为declarations诚意条件 sincerity condition次要言外行为 secondary illocutionary act等级含义 scalar implicature等级划分法 rating scales副语言特征 paralinguistic features非公开施行面子威胁行为 off record非规约性non—conventionality非规约性意义 non-conventional implicature非论证性的 non—demonstrative非自然意义non—natural meaning (meaning—nn)否定测试法negation test符号学 semiotics构成性规则 constitutive rules古典格莱斯会话含义理论 Classical Gricean theory of conversational implicature关联论Relevance Theory关联原则Principle of Relevance归属性用法 attributive use规约性含义conventional implicature人际修辞 interpersonal rhetoric篇章修辞textual rhetoric含蓄动词 implicative verbs合适条件 felicity conditions呼语 vocatives互相显映 mutually manifest会话含义 conversational implicature话语层次策略 utterance-level strategy积极面子positive face间接言语行为 indirect speech acts间接指令 indirect directives结语 upshots交际意图communicative intention可撤销性 cancellability可废弃性 defeasibility可推导性 calculability跨文化语用失误cross—cultural pragmatic failure跨文化语用学cross—cultural pragmatics命题内容条件 propositional content condition面子保全论 Face-saving Theory面子论 Face Theory面子威胁行为 Face Threatening Acts (FTAs)蔑视 flouting明示 ostensive明示-推理模式ostensive—inferential model摹状词理论Descriptions粘合程度 scale of cohesion篇章指示 discourse deixis前提 presupposition前提语 presupposition trigger强加的绝对级别absolute ranking of imposition确定谈话目的 establishing the purpose of the interaction确定言语事件的性质 establishing the nature of the speech event 确定性描述语 definite descriptions认知语用学 cognitive pragmatics上下文 co—text社会语用迁移sociopragmatic transfer社交语用失误 sociopragmatic failure施为句 performative省力原则 the principle of least effort实情动词 factive verbs适从向 direction of fit手势型用法 gestural usage首要言外行为 primary illocutionary act双重或数重语义模糊 pragmatic bivalence/ plurivalence顺应的动态性 dynamics of adaptability顺应性adaptability语境关系的顺应(contextual correlates of adaptability)、语言结构的顺应(structural objects of adaptability)、顺应的动态性(dynamics of adaptability)和顺应过程的意识程度(salience of the adaptation processes)。
网络流行语“中国大妈”和“中国式X”解析--基于认知及社会文化维度徐敏【期刊名称】《铜陵学院学报》【年(卷),期】2014(000)004【摘要】伴随着新媒体的发展,“中国大妈”和与其结构类似的“中国式X”的网络流行语正在受到越来越多的关注。
代表一种独特的文化现象的“中国式X”,本身就是一个语言模因,它的各种次生语言形式在模因的作用下得以在社会大众之间广泛繁殖和衍生。
我们既可以从认知语言学的概念重叠和概念整合角度对“中国大妈”的形成及其所隐含的调侃语义进行解读,也可以从社会文化学领域的模因论视角对“中国式X”的传播进行探讨。
%With the development of new med ia, more attention is being paid to the network buzzwords. This paper focuses on“Zhongguo Dama”and similar structure “Zhongguoshi X”, and argues that “Zhongguo”in “Zhongguo Dama” is not just a noun represent-ing the country, but possesses the modification function like adjectives, which can be understood as “Zhongguo style” that refers to a special Chinese cultural phenomenon. Besides, this paper makes an explanation on the formation of “Zhongguo Dama” and its implied teasing meaning using conceptual overlap and conceptual integration proposed in cognitive linguistics. Finally, this paper discusses the spread of “Zhongguoshi X” from the perspective of “meme” in the domain of socio-culture. “Zhongguoshi X” is a linguistic meme, and its varioussecondary language forms can be reproduced and derived among the social public under the effect of meme.【总页数】4页(P83-86)【作者】徐敏【作者单位】澳门大学,澳门 999078【正文语种】中文【中图分类】H136【相关文献】1.中国互联网公益捐赠监管体制的多维度解析r——基于整体性治理视角 [J], 朱虹;吴楠2.中国式财政分权与地区收入差距——基于不同分权维度的分析 [J], 焦健;罗鸣令3.基于文化语言学的中国式英语现象多维度研究 [J], 杨永青4.基于文化语言学的中国式英语现象多维度研究 [J], 杨永青5.霍夫斯泰德文化维度理论视角下的中国式教育观——基于《中国式家长》游戏的分析 [J], 康健秋;禹娜因版权原因,仅展示原文概要,查看原文内容请购买。
Modeling Degrees of Conceptual Overlap in SemanticWeb OntologiesMarkus Holi and Eero HyvönenHelsinki Institute for Information Technology(HIIT),Helsinki University of Technology,Media Technology and University of HelsinkiP.O.Box5500,FI-02015TKK,FINLAND,http://www.cs.helsinki.fi/group/seco/email:fistname@tkk.firmation retrieval systems have to deal with uncertain knowledgeand query results should reflect this uncertainty in some manner.However,Se-mantic Web ontologies are based on crisp logic and do not provide well-definedmeans for expressing uncertainty.We present a new probabilistic method to ap-proach the problem.In our method,degrees of subsumption,i.e.,overlap be-tween concepts can be modeled and computed efficiently using Bayesian net-works based on RDF(S)ontologies.Degrees of overlap indicate how well an in-dividual data item matches the query concept,which can be used as a well-definedmeasure of relevance in information retrieval tasks.1Ontologies and Information RetrievalA key reason for using ontologies in information retrieval systems,is that they enable the representation of background knowledge about a domain in a machine understand-able format.Humans use background knowledge heavily in information retrieval tasks [7].For example,if a person is searching for documents about Europe she will use her background knowledge about European countries in the task.She willfind a doc-ument about Germany relevant even if the word’Europe’is not mentioned in it.With the help of an appropriate geographical ontology also an information retrieval system could easily make the above inference.Ontologies have in fact been used in a number of information retrieval system in recent years[15,10,11].Ontologies are based on crisp logic.In the real world,however,relations between entities often include subtleties that are difficult to express in crisp ontologies.For ex-ample,consider the case of representing the relationships between geohgraphical areas in the world with a partonomy where each concept represents an area in the world. We will run into difficulties,because RDFS[2]and OWL[1]do not provide standard ways to express the facts that Germany covers a much larger part of the area of Europe than Andorra,or that Russia is a part of both Asia and Europe,for example.In addi-tion,the information system itself can be a source of uncertainty,as the indexing of the documents is often inexact.These representational shortcomings could hinder the performance of the information retrieval system and produce wrong search results.This paper presents a new method to approach the above problems.The method is based on the modeling of degrees of overlap between concepts.In the following wefirst introduce the principles of our method.Then a notation that enables the represen-tation of degrees of overlap between concepts in an ontology is presented after which a method for doing inferences based on the notation will be described.Then our im-plementation of the method is discussed,and finally conclusions are drawn and related work discussed.These paper is an extended version of [9].For a more detail presenta-tion of the method see [8].2Modeling Uncertainty in OntologiesWorld 37*23 = 851Europe 15*23 = 345Asia 18*23 = 414EU 8*21 = 168Sweden 4*9 = 36Finland 4*9 = 36SwedenFinland EUEuropeAsia WorldNorway 4*9 = 36Lapland 13*2 = 26RussiaRussia 18*19 = 342 Russia&Europe = 57Russia&Asia = 285Lapland&(Finland | Sweden | Norway) = 8Lapland&Russia = 2Lapland&EU = 16LaplandNorwayFig.1.A Venn diagram illustrating countries,areas,their overlap,and size in the world.The Venn diagram of figure 1illustrates some countries and areas in the world.A crisp partonomy cannot represent the partial overlap between the geographical area Lapland and the countries Finland,Sweden,Norway,and Russia,for example.Further-more,it is not possible to quantify the coverage and the overlap of the areas involved.According to figure 1,the size of Lapland is 26units,and the size of Finland is 36units.The size of the overlapping area between Finland and Lapland is 8units.Thus,8/26of Lapland belongs to Finland,and 8/36of Finland belongs to Lapland.On the other hand,Lapland and Asia do not have any overlapping area,thus no part (0)of Laplan is part of Asia,and no part of Asia is part of Lapland.If we want a partonomy to be an accurate representation of the ’map’of figure 1,there should be a way to make this kind of inferences based on the partonomy.Our method enables the representation of overlap in concept hierarchies,including class hierarchies and partonomies,and the computation of overlap between a selected concept and every other,i.e.referred concept in the hierarchy.The overlap value is defined as follows:Overlap=|Selected∩Ref erred|concepts are nodes,and a number called mass is attached to each node.The mass of con-cept A is a measure of the size of the set corresponding to A,i.e.m(A)=|s(A)|,where s(A)is the set corresponding to A.A solid directed arc from concept A to B denotes crisp subsumption s(A)⊆s(B),a dashed arrow denotes disjointness s(A)∩s(B)=∅, and a dotted arrow represents quantified partial subsumption between concepts,which means that the concepts partially overlap in the Venn diagram.The amount of overlap is represented by the partial overlap value p=|s(A)∩s(B)|relation,between concepts.If there is a directed solid path from A(selected)to B(re-ferred),then overlap o=|s(A)∩s(B)|m(B).If the solid path is directed from B toA,then o=|s(A)∩s(B)|m(B)=1.If there is not a directed path between A and B,then o=|s(A)∩s(B)|m(B)=0.If there is a mixed path of solid and dotted arcs between A and B,then the calcu-lation is not as simple.Consider,for example,the relation between Lapland and EU infigure2.To compute the overlap,we have to follow all the paths emerging from Lapland,take into account the disjoint relation between Lapland and Asia,and sum up the partial subsumption values somehow.To exploit the simple solid arc case,a hierarchy with partial overlaps isfirst trans-formed into a solid path structure,in which crisp subsumption is the only relation be-tween the concepts.The transformation is done according to the following principle: Transformation Principle1Let A be the direct partial subconcept of B with overlap value o.In the solid path structure the partial subsumption is replaced by an additional middle concept,that represents s(A)∩s(B).It is marked to be the complete subconcept of both A and B,and its mass is o·m(A).Fig.3.The taxonomy offigure2as a solid path structure.For example,the partonomy offigure2is transformed into the solid path structure offigure3.The original partial overlaps of Lapland and Russia are transformed into crisp subsumption by using middle concepts.The transformation algorithm processes the overlap graph in a breadth-first manner starting from the root concept.A concept is processed only after all of its super concepts(partial or complete)are processed. Because the graph is acyclic,all the concepts will eventually be processed.For a more detailed description of the transformation algorithm see[8].5Computing the OverlapsRecall from section2that if A is the selected concept and B is the referred one,then the overlap value o can be interpreted as the conditional probability|s(A)∩s(B)|P(B =true|A =true)=(2)m(A)The value for the complementary case P(A =false|B 1=b1,...B n=b n) is obtained simply by subtracting from1.The above formula is based on the above definition of conditional probability.The intuition behind the formula is the following. If a user is interested in Sweden and in Finland,in the Bayesian network both Finland and Sweden will be set“true”.Thus,the bigger the number of European countries that the user is interested in,the bigger the probability that the annotation“Europe”matches her query,i.e.,P(Europe =true|Sweden =true,F inland =true)> P(Europe =true|F inland =true).If A has no parents,then P(A =true)=λ,whereλis a very small non-zero probability,because we want the posterior probabilities to result from conditional prob-abilities only,i.e.,from the overlap information.The whole overlap table of a concept can now be determined efficiently by using the Bayesian network with its conditional and prior probabilities.By instantiating the nodes corresponding to the selected concept and the concepts subsumed by it as evidence (their values are set“true”),the propagation algorithm returns the overlap values as posterior probabilities of nodes.The query results can then be ranked according to these posterior probabilities.First,to be able to easily use the the solid path structure as the topology of the Bayesian network.The CPTs can be calculated directly based on the masses of the con-cepts.Second,with this definition the Bayesian evidence propagation algorithm returns the overlap values readily as posterior propabilities.We experimented with various ways to construct a Bayesian network according to probabilistic interpretations of the Venn diagram.However,none of these constructions answered to our needs as well as the construction described above.Third,in the solid path structure d-separation indicates disjointness between con-cepts.We see this as a useful characteristic,because it makes the simultaneous selection of two or more disjointed concepts possible.6ImplementationThe presented method has been implemented as a proof-of-concept.In the implemen-tation overlap graphs are represented as RDF(S)ontologies in the following way.Con-cepts are represented as RDFS classes1The concept masses are represented using a special Mass class.It has two properties,subject and mass that tell the concept resource in question and mass as a numeric value,respectively.The subsumption relation can be implemented with a property of the users choice.Partial subsumption is implemented by a special PartialSubsumption class with three properties:subject,object and overlap.The subject property points to the direct partial subclass,the object to the direct partial superclass,and overlap is the partial overlap value.The disjointness arc is implemented by the disjointFrom property used in OWL.The input to the system is an RDF(S)ontology,the URI of the root node of the overlap graph,and the URI of the subsumption property used in the ontology.The output is the overlap tables for every concept in the taxonomy extracted from the input RDF(S)ontology.Next,each submodule in the system is discussed briefly.The preprocessing module transforms the taxonomy into a predefined standard form.The transformation module implements the transformation algorithm,and de-fines the CPTs of the resulting Bayesian network.In addition to the Bayesian network, it creates an RDF graph with an identical topology,where nodes are classes and the arcs are represented by the rdf:subClassOf property.This graph will be used by the selection module that expands the selection to include the concepts subsumed by the selected one,when using the Bayesian network.The Bayesian reasoner does the evi-dence propagation based on the selection and the Bayesian network.The selection and Bayesian reasoner modules are operated in a loop,where each concept in the taxonomy is selected one after the other,and the overlap table is created.The preprocessing,transformation,and selection modules are implemented with SWI-Prolog2.The Semantic Web package is used.The Bayesian reaoner module is implemented in Java,and it uses the Hugin Lite6.33through its Java API.7Discussion7.1Lessons LearnedOverlap graphs are simple and can be represented in RDF(S)ing the notation does not require knowledge of probability theory.The concepts can be quantified auto-matically,based on data records annotated according to the ontology,for example.Thenotation enables the representation of any Venn diagram,but there are set structures, which lead to complicated representations.Such a situation arises,for example,when three or more concepts mutually par-tially overlap each other.In these situations some auxiliary concepts have to be used. However,we have not met such situations in geospatial ontologies.The Bayesian network structure that is created with the presented method is only one of the many possibilities.This one was chosen,because it can be used for computing the overlap tables in a most direct manner.7.2Related WorkThe problem of representing uncertain or vague inclusion in ontologies and taxonomies has been tackled also by using methods of fuzzy logic[21]and rough sets[19,17].With the rough sets approach only a rough,egg-yolk representation of the concepts can be created[19].Fuzzy logic,allows for a more realistic representation of the world.Straccia[18]presents a fuzzy extension to the description logic SHOIN(D)corresponding to the ontology description language OWL DL.It en-ables the representation of fuzzy subsumption for example.Widyantoro and Yen[20]have created a domain-specific search engine called PASS.The system includes an interactive query refinement mechanism to help tofind the most appropriate query terms.The system uses a fuzzy ontology of term associ-ations as one of the sources of its knowledge to suggest alternative query terms.The ontology is organized according to narrower-term relations.The ontology is automati-cally built using information obtained from the system’s document collections.The fuzzy ontology of Widyantoro and Yen is based on a set of documents,and works on that document set.However,our focus is on building taxonomies that can be used,in principle,with any data record set.The automatic creation of ontologies is an interesting issue by itself,but it is not considered in this paper.At the moment,better and richer ontologies can be built by domain specialists than by automated methods.The fuzzy logic approach is critisized because of the arbitrariness infinding the numeric values needed and mathematical indefiniteness[19].In addition,the represen-tation of disjointness between concepts of a taxonomy seems to be difficult with the tools of fuzzy logic.For example,the relationships between Lapland,Russia,Europe, and Asia are very easily handled probabilistically,but in a fuzzy logic based taxonomy, this situation seems complicated.There is not a readily available fuzzy logic operation that could determine that if Lapland partly overlaps Russia,and is disjoint from Asia, then the fuzzy inclusion value between Europe and Lapland∩Russia is1even though Russia is only a fuzzy part of Europe.We chose to use crisp set theory and Bayesian networks,because of the sound mathematical foundations they offer.The set theoretic approach also gives us means to overcome to a large degree the problem of arbtrariness.The calculations are simple, but still enable the representation of overlap and vague subsumption between concepts. The Bayesian network representation of a taxonomy is useful not only for the matching problem we discussed,but can also be used for other reasoning tasks[14].Ding and Peng[3]present principles and methods to convert an OWL ontology into a Bayesian network.Their methods are based on probabilistic extensions to de-scription logics[13,5].The approach has some differences to ours.First,their aim is to create a method to transform any OWL ontology into a Bayesian network.Our goal is not to transform existing ontologies into Bayesian networks,but to create a method by which overlap between concepts could be represented and computed from a taxonomi-cal structure.However,we designed the overlap graph and its RDF(S)implementation so,that it is possible,quite easily,to convert an existing crisp taxonomy to our extended notation.Second,in the approach of Ding and Peng,probabilistic information must be added to the ontology by the human modeler that needs to know probability theory.In our approach,the taxonomies can be constructed without virtually any knowledge of probability theory or Bayesian networks.Also other approaches for combining Bayesian networks and ontologies exist. Gu[6]present a Bayesian approach for dealing with uncertain contexts.In this ap-proach probabilistic information is represented using OWL.Probabilities and condi-tional probabilities are represented using classes constructed for these purposes.Mitra [16]presents a probabilistic ontology mapping tool.In this approach the nodes of the Bayesian network represents matches between pairs of classes in the two ontologies to be mapped.The arrows of the BN are dependencies between matches.Kauppinen and Hyvönen[12]present a method for modeling partial overlap be-tween versions of a concept that changes over long periods of time.The approach differs from ours in that we are interested in modelling degrees of overlap between different concepts in a single point of time.8ACKNOWLEDGEMENTSOur research was funded mainly by the National Technology Agency Tekes. 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