摘要
The word “ontology” has gained a good popularity within the AI community. Ontology is usually viewed as a high-level description con- sisting of concepts that organize the upper parts of the knowledge base. However, meaning of the term “ontology” tends to be a bit vague, as the term is used in di?erent ways. In this paper we will attempt to clarify the meaning of the ontology including the philosophical views and show why ontologies are useful and important. We will give an overview of ontology structures in several particular systems. A field proposed within ontological e?orts, “ontological engi- neering”, will be also described. Usage of ontologies in several particular ways will be discussed. These include systems and ideas to support knowledge base sharing and reuse, both for computers and humans, ontology based communication in multi- agent systems, applications of ontologies for natural language processing, applications in documents search and enrichment of knowledge bases, both particularly for the World Wide Web environment and construction of educational systems, particularly intelligent tutoring systems.
本體(ontology)一詞在人工智能界已經(jīng)有相當(dāng)?shù)闹攘恕1倔w通常被認(rèn)為是由概念所組成的高級(jí)描述,概念則是用來(lái)對(duì)知識(shí)庫(kù)進(jìn)行組織的上層部分。然而,當(dāng)“ontology”這個(gè)術(shù)語(yǔ)在不同的場(chǎng)合以不同方式加以應(yīng)用時(shí),其含義往往是有點(diǎn)兒含糊不清的。本文將力圖闡明本體的含義,包括哲學(xué)觀點(diǎn)上的含義,并指明為什么本體是很有用的,也是很重要的。我們將給出幾個(gè)特殊系統(tǒng)中的本體結(jié)構(gòu)總體情況,并對(duì)“本體工程”這一最新被提議的研究領(lǐng)域加以闡述。本文還將討論本體的幾種特定的用法,包括支持人與計(jì)算機(jī)的知識(shí)庫(kù)共享與重用的系統(tǒng)和想法、多主體系統(tǒng)中基于本體的通信、本體在自然語(yǔ)言中的應(yīng)用、本體在文本搜索和知識(shí)庫(kù)濃縮中的應(yīng)用,同時(shí)包括在互聯(lián)網(wǎng)環(huán)境和教育系統(tǒng)中,特別是智能輔導(dǎo)系統(tǒng)。
1 Introduction
The word “ontology” has gained a good popularity within the AI community. Ontology is usually viewed as a high-level description consisting of concepts that organize the upper parts of the knowledge base. However, meaning of the term “ontology” tends to be a bit vague, as the term is used in different ways. In this paper we will attempt to clarify the meaning of the ontology and show why ontologies are useful and important. We will discuss usage of ontologies in several particular ways, such as knowledge base reuse, knowledge sharing, communication in multi-agent systems, applications of ontologies for WWW applications, for natural language processing, and for intelligent tutoring systems.
1 簡(jiǎn)介
“本體”這個(gè)詞在AI領(lǐng)域中廣泛流傳。本體經(jīng)常被視作一個(gè)高層次的描述方法,這個(gè)描述方法由一些概念組成,而這些概念被認(rèn)為組成了知識(shí)庫(kù)的上層結(jié)構(gòu)。但是,由于它被用在許多不同的地方,“本體”一詞的意思似乎很容易被混淆。在這份文件中,我們將嘗試弄清本體的真正意思,并且展示產(chǎn)生本體重要意義和實(shí)用性的原因。我們將用不同的方面討論本體的用處,例如知識(shí)庫(kù)的復(fù)用,知識(shí)庫(kù)的共享,多代理系統(tǒng)內(nèi)部的通訊,用作網(wǎng)絡(luò)應(yīng)用的本體應(yīng)用程序,用作自然語(yǔ)言處理的本體應(yīng)用程序以及用作智能輔助系統(tǒng)內(nèi)的本體應(yīng)用程序。
1.1 動(dòng)機(jī) 在AI研究歷史中,定義了兩種研究類型[31,8]:面向形式的研究(機(jī)制理論)及面向內(nèi)容的研究(內(nèi)容理論)。前者處理邏輯與知識(shí)表達(dá),而后者處理知識(shí)的內(nèi)容。顯然前者時(shí)至今日是AI的勘察范圍,然而在最近,面向內(nèi)容的研究已逐漸引起更多的關(guān)注,因?yàn)樵S多現(xiàn)實(shí)世界的問(wèn)題的解決如知識(shí)的重用、agent通訊的簡(jiǎn)化、通過(guò)理解集成媒體、大規(guī)模的知識(shí)基等等,不僅需要先進(jìn)的理論或推理方法而且還需要對(duì)知識(shí)內(nèi)容進(jìn)行復(fù)雜的處理。 Formal theories such as predicate logic provides us with a powerful tool to guarantee sound reasoning and thinking. It even enables us to discuss the limits of our reasoning in a principled way. However, it cannot answer to any of the questions such as what knowledge we should have for solving given problems, what is knowledge at all, what properties a specific knowledge has, and so on. Sometimes, the AI community gets excited by some mechanisms such as neural nets, fuzzy logic, genetic algorithms, constraint propagation etc. These mechanisms are proposed as the “secret” of making intelligent machines. At other times, it is realized that, however wonderful the mechanism, it cannot do much without a good content theory of the domain on which it is to work. Moreover, we often recognize that once a good content theory is available, many di?erent mechanisms might be used equally well to implement e?ective systems, all using essentially the same content.
Importance of content-oriented research is being recognized more and more nowadays. Unfortunately it seems that there are no widely recognized sophisticated methodologies for content-oriented research now. Major results till later years were only development of knowl- edge bases. 以前的理論比如謂詞邏輯學(xué)提供了一種合理的推理和思考的工具。它甚至使我們可以在一定原則下來(lái)探討推理的局限性。然而,這一理論卻不能回答諸如“解決特定問(wèn)題需要什么知識(shí)”,“究竟什么是知識(shí)”,“一種特定知識(shí)具備怎樣的特征”等等的問(wèn)題。有時(shí),人工智能領(lǐng)域因?yàn)橐恍├碚摍C(jī)制而變得沸沸揚(yáng)揚(yáng),比如神經(jīng)網(wǎng)絡(luò),模糊學(xué),基因運(yùn)算規(guī)則以及選擇性繁殖等。這些理論被認(rèn)為是開(kāi)發(fā)人工智能的“秘密”所在。而又有些時(shí)候,我們意識(shí)到不管這些機(jī)制多么令人贊嘆,如果在其作用領(lǐng)域內(nèi)沒(méi)有一個(gè)完善的內(nèi)容理論,它將難以發(fā)揮巨大作用。更進(jìn)一步,我們常常發(fā)現(xiàn)一旦建立了完備的內(nèi)容理論,許多不同的理論機(jī)制都能良好的實(shí)現(xiàn)有效的系統(tǒng),而這些系統(tǒng)本質(zhì)上都應(yīng)用同樣的內(nèi)容。現(xiàn)在,面向內(nèi)容的研究的重要性已日益為我們所重視。遺憾的是目前還沒(méi)有形成面向內(nèi)容的被廣泛認(rèn)同的精確的方法論,近年來(lái)最大的成果也只是知識(shí)庫(kù)的開(kāi)發(fā)。
The reasons for this can be [31]:
? content-oriented research tends to be ad hoc ? there is no methodology that enables to accumulate research results It is necessary to overcome these di?culties in the content-oriented research. Ontologies are proposed for that purpose. Ontology engineering, as proposed in e.g. [31], is a research methodology which gives us design rationale of a knowledge base, kernel conceptualization of the world of interest, strict definition of basic meanings of basic concepts together with sophis- ticated theories and technologies enabling accumulation of knowledge which is dispensable for modeling the real world. Interest in ontologies has also grown as researchers and system developers have become more interested in reusing or sharing knowledge across systems. Currently, one key imped- iment to sharing knowledge is that di?erent systems use di?erent concepts and terms for describing domains. These di?erences make it di?cult to take knowledge out of one system and use it in another. If we could develop ontologies that could be used as the basis for multi- ple systems, they would share a common terminology that would facilitate sharing and reuse. Developing such reusable ontologies is an important goal of ontology research. Similarly, if we could develop tools that would support merging ontologies and translating between them, sharing would be possible even between systems based on di?erent ontologies. 出現(xiàn)這種情況的原因或許有如下幾點(diǎn):【31】
1.面向內(nèi)容的研究更趨于專業(yè)化
2.對(duì)于研究結(jié)果的聚集尚無(wú)一定的方法論
內(nèi)容研究必須克服這些難點(diǎn),而本體就是基于這個(gè)目的提出的。本體設(shè)計(jì),就像【31】所要求的,是一種內(nèi)容研究的方法論,它提供了知識(shí)庫(kù)設(shè)計(jì)的基本原理,專業(yè)領(lǐng)域的核心概念,對(duì)基本概念含義的嚴(yán)格定義,以及模擬現(xiàn)實(shí)世界所必不可少的知識(shí)聚集的復(fù)雜理論和技術(shù)。
隨著研究人員和系統(tǒng)開(kāi)發(fā)者對(duì)系統(tǒng)內(nèi)的知識(shí)重用和共享越發(fā)感興趣,對(duì)本體論的興趣也日益增長(zhǎng)。目前,阻礙知識(shí)共享的一個(gè)關(guān)鍵問(wèn)題是不同系統(tǒng)使用不同的概念和術(shù)語(yǔ)來(lái)描述其領(lǐng)域。這種不同使得將一個(gè)系統(tǒng)的知識(shí)用于其他系統(tǒng)變得十分復(fù)雜。如果可以開(kāi)發(fā)一些能夠用作多個(gè)系統(tǒng)的基礎(chǔ)的本體,這些系統(tǒng)就可以共享通用的術(shù)語(yǔ)以實(shí)現(xiàn)知識(shí)共享和重用。開(kāi)發(fā)這樣的可重用本體是本體論研究的重要目標(biāo)。類似的,如果我們可以開(kāi)發(fā)一些支持本體合并以及本體間互譯的工具,那么即使是基于不同本體的系統(tǒng)也可以實(shí)現(xiàn)共享。
1.2 Philosophical View
哲學(xué)角度看本體
The term ontology was taken from philosophy. According toWebster’s Dictionary an ontology is ? a branch of metaphysics relating to the nature and relations of being ? a particular theory about the nature of being or the kinds of existence
Ontology (the “science of being”) is a word, like metaphysics, that is used in many di?erent senses. It is sometimes considered to be identical to metaphysics, but we prefer to use it in a more specific sense, as that part of metaphysics that specifies the most fundamental categories of existence, the elementary substances or structures out of which the world is made. Ontology will thus analyze the most general and abstract concepts or distinctions that underlay every more specific description of any phenomenon in the world, e.g. time, space, matter, process, cause and e?ect, system. Recently, the term of “ontology” has been up taken by researchers in Artificial Intelligence, who use it to designate the building blocks out of which models of the world are made.
An agent (e.g. an autonomous robot) using a particular model will only be able to perceive that part of the world that his ontology is able to represent. In this sense, only the things in his ontology can exist for that agent. In that way, an ontology becomes the basic level of a knowledge representation scheme. An example is set of link types for a semantic network representation which is based on a set of ”ontological” distinctions: changing–invariant, and general–specific.
本體這個(gè)術(shù)語(yǔ)來(lái)自于哲學(xué)。根據(jù)韋氏詞典的解釋,本體是
形而上學(xué)的一個(gè)分支,研究關(guān)于自然和存在的關(guān)系;
關(guān)于存在的本質(zhì)的專門(mén)理論。
本體(指關(guān)于存在的科學(xué))是個(gè)詞,就好象形而上學(xué),可以用于各種不同的語(yǔ)境。有時(shí)候把本體等同于形而上學(xué),但我們傾向于在更具體的意義上應(yīng)用它,就像形而上學(xué)詳細(xì)說(shuō)明了存在的最基本的范疇,組成世界的基本物質(zhì)或結(jié)構(gòu)。本體論因此將分析最普遍最抽象的概念或差別,這種差別成為對(duì)世界上各種現(xiàn)象(比如時(shí)間、空間、物質(zhì)、過(guò)程、原因和結(jié)果、系統(tǒng)等)進(jìn)行具體描述的根基。
最近,本體在人工智能領(lǐng)域中得以應(yīng)用,它被認(rèn)為是構(gòu)建世界模型的積木。
一個(gè)使用特定模型的代理(比如一個(gè)自主機(jī)器人),只能理解它內(nèi)部定義的本體所能代表的世界的某部分。在這個(gè)意義上,只有在代理本體里定義的事物對(duì)代理來(lái)說(shuō)才是存在的。這樣,一個(gè)本體就代表了知識(shí)大綱的基本水平。例如對(duì)語(yǔ)義網(wǎng)的鏈接類型的表現(xiàn)是基于一系列“本體論的”定義:變更——固定;普遍——特殊。
2 What is an Ontology?
The term “ontology” is used in many di?erent ways. In this section we will discuss what an ontology is on several definitions that are currently used.
何謂本體論?
本體論這個(gè)術(shù)語(yǔ)應(yīng)用于很多方面。這一節(jié)中我們將在幾個(gè)目前所使用的不同定義的基礎(chǔ)上討論什么是“本體論”。
2.1 Common Definitions
2.1 普遍定義
The most widespread definitions of ontology are given below. 1. Ontology is a term in philosophy and its meaning is “theory of existence”. 2. Ontology is an explicit specification of conceptualization [21]. 3. Ontology is a theory of vocabulary or concepts used for building artificial systems [31]. 4. Ontology is a body of knowledge describing some domain (eg. a common sense knowl- edge domain in CYC [45]) The definition 1 is radically di?erent from all the others (including additional ones dis- cussed below). We will shortly discuss some implications of its meaning for definition of “ontology” for AI purposes. The second definition is generally proposed as a definition of what an ontology is for the AI community. It may be classified as “syntactic”, but its precise meaning depends on the understanding of the terms “specification” and “conceptualization”. The third definition is a proposal for definition within the knowledge engineering community. The last fourth definition di?ers from the previous two ones — it views the ontology as an inner body of knowledge, not as the way to describe the knowledge. Although these definitions are compact, they are not su?cient for in-depth understanding of what an ontology is. We will try to give more comprehensive definitions and insights. 最廣為流傳的本體論定義如下:
1.本體論是一個(gè)哲學(xué)術(shù)語(yǔ),意義為“關(guān)于存在的理論”
2.本體論是關(guān)于概念化的清楚詳細(xì)的說(shuō)明
3.本體論是關(guān)于詞匯或概念的理論,它用于構(gòu)建人工智能系統(tǒng)
4.本體論是用來(lái)定義某一領(lǐng)域的知識(shí)主體(比如:在CYC領(lǐng)域的常識(shí)性知識(shí))
定義1與其他定義(包括下面將要討論的其他定義)有著本質(zhì)不同。我們一會(huì)兒將討論在人工智能領(lǐng)域的“本體論”的深層含義。第二個(gè)定義通常認(rèn)為是“本體論”在人工智能中的定義。它或許可以歸為符合造句法的一類,然而其更準(zhǔn)確的含義要依靠對(duì)“詳細(xì)說(shuō)明”和“概念化”的理解。第三個(gè)定義是知識(shí)工程師團(tuán)體推薦的定義。最后第四個(gè)有別于前兩個(gè)定義——它把本體論看作知識(shí)的內(nèi)主體,而不是描述知識(shí)的途徑。
這些定義雖然簡(jiǎn)潔,但是要深層理解本體論這些是不夠的。我們將試著給出更多的更為全面的定義和觀點(diǎn)。
2.1.1 Ontology as a Philosophical Term
2.1.1 作為哲學(xué)名詞的"本體"
Following [24] we will use the convention that the uppercase initial letter “O” is to distinguish the “Ontology” as a philosophical discipline from other usages of this term. Ontology is a branch of philosophy that deals with the nature and the organization of reality. It tries to answer questions like “what is existence”, “what properties can explain the existence” etc. Aristotle defined Ontology as the science of being as such. Unlike the special sciences, each of which investigates a class of beings and their determinations, Ontology regards “all the species qua being and the attributes that belong to it qua being” (Aristotle, Metaphysics, IV, 1). In this sense Ontology tries to answer the question “what is the being?” or, in a meaningful reformulation “what are the features common to all beings?”. This is what is today called “General Ontology” in contrast with various Special or Re- gional Ontologies (eg. Biological, Social). From this, Formal Ontology is defined as an area that has to determinate the conditions of the possibility of the object in general and the in- dividualization of the requirements that every object’s constitution has to satisfy. According to [24] Formal Ontology can be defined as the systematic, formal, axiomatic development of the logic of all forms and modes of being. From this, Formal Ontology is not concerned so much in the existence of certain objects, but rather in the rigorous description of their forms of being, i.e. their structural features. In practice, Formal Ontology can be intended as the theory of the distinctions, which can be applied independently of the state of the world, i. e. the distinctions: ? among the entities of the world (physical objects, events, regions...) ? among the meta-level categories used to model the world (concept, property, quality, state, role, part...) In this sense, Formal Ontology, as a discipline, may be relevant to both Knowledge Rep- resentation and Knowledge Acquisition [24].
以下,我們使用首字母大寫(xiě)的“O”時(shí),指“Ontology”作為一門(mén)哲學(xué)學(xué)科,以此與它的其他用法進(jìn)行區(qū)別。“Ontology”(哲學(xué)上的本體論)時(shí)哲學(xué)的一個(gè)分支,研究自然存在以及現(xiàn)實(shí)的組成結(jié)構(gòu)。它試圖回答“什么是存在”,“存在的性質(zhì)是什么”等等。亞里士多德也同樣定義“本體論”是存在的科學(xué)。每一門(mén)具體科學(xué)都研究一類事物和它們的性質(zhì),與之不同,本體論涉及的是“所有作為存在的事物以及它們作為存在的特性(亞里士多德, 形而上學(xué),IV, 1). ”在這個(gè)意義上,本體論是試圖回答“存在是什么”的科學(xué),或者這個(gè)問(wèn)題可以表達(dá)為含義更清楚的形式,即“所有的存在有什么共性?”
這就是今天所說(shuō)的“一般本體論”,它與各種特殊的專門(mén)的本體論相對(duì)(如,生物本體論,社會(huì)本體論)。從這個(gè)觀點(diǎn)出發(fā),形式本體論是指這樣一個(gè)領(lǐng)域,它確定客觀事物總體上的可能的狀態(tài),確定每個(gè)客觀事物的結(jié)構(gòu)所必須滿足的個(gè)性化的需求。根據(jù)[24],形式本體論可以定義為有關(guān)存在的一切形式和模式的系統(tǒng),正式,自明的發(fā)展。
由此看來(lái),形式本體論并不是特別關(guān)注特定事物的存在,而是嚴(yán)格描述它們存在的形式,比如它們的結(jié)構(gòu)特征。實(shí)踐中,形式本體論可以看作是區(qū)別理論,可以獨(dú)立應(yīng)用于世界的狀態(tài),如:
世界上不同實(shí)體之間的區(qū)別(物理實(shí)體、事件、地區(qū)等);
模擬世界的元范疇間的區(qū)別(概念、性質(zhì)、質(zhì)量、狀態(tài)、角色、部分等)
2.1.2 Ontology as a Specification of Conceptualization
2.1.2 作為概念化詳細(xì)說(shuō)明的本體論
The second definition of ontology mentioned above, explicit specification of conceptualiza- tion, is briefly described in [20]. The definition comes from work [22] where the ontology is used in context of knowledge sharing. According to Thomas Gruber, explicit specification of conceptualization means that an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general.In this sense, ontology is important for the purpose of enabling knowledge sharing and reuse. An ontology is in this context a specification used for making ontological commitments. Practically, an ontological commitment is an agreement to use a vocabulary (i.e. ask queries and make assertions) in way that is consistent (but not complete) with respect to the theory specified by an ontology. Agents are then built that commit to ontologies and ontologies are designed so that the knowledge can be shared with and among these agents.
上面所提到的本體論第二個(gè)定義——概念化的清楚詳細(xì)的說(shuō)明——在【20】中進(jìn)行了簡(jiǎn)要描述。這一定義來(lái)自【22】的工作,在這里本體用于知識(shí)共享。根據(jù)Thomas Gruber的解釋,概念化的清楚的詳細(xì)說(shuō)明是指:一個(gè)本體是對(duì)概念和關(guān)系的描述(就像程序的詳細(xì)說(shuō)明書(shū)),而這些概念和關(guān)系可能是針對(duì)一個(gè)代理或代理群體而存在的。這個(gè)定義與本體論在概念定義中的描述一致,但它更具普遍意義。在這個(gè)意義上,本體論對(duì)于知識(shí)共享和重用非常重要。此處,一個(gè)本體是用來(lái)進(jìn)行本體委托的詳細(xì)說(shuō)明。事實(shí)上,本體委托就是使用詞匯的一個(gè)協(xié)議(比如進(jìn)行詢問(wèn)和做出聲明),而使用的方法要與某個(gè)本體指定的理論一致(而不必完全的照本宣科)。然后就可以開(kāi)發(fā)應(yīng)用這些本體的代理,而本體設(shè)計(jì)的目的就是讓代理內(nèi)部或者代理之間能夠共享知識(shí)。 The body of a knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationship that hold among them. A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly.
知識(shí)的主體是基于概念化的:客觀事物、概念以及其他實(shí)體存在于特定領(lǐng)域和其所處關(guān)系之中。概念化是對(duì)世界的抽象,是我們?cè)谝欢康南聦?duì)期望表現(xiàn)的世界簡(jiǎn)化觀察。每個(gè)知識(shí)庫(kù),基于知識(shí)的系統(tǒng),或者是知識(shí)水平上的代理都或明顯或潛在地遵照某些概念化的過(guò)程。 For these systems, what “exists” is that which can be represented. When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. This set of objects and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. Thus, in the context of AI, we can describe the ontology of a program by defining a set of representational terms. In such an ontology, definitions associate the names of entities in the universe of discourse (e.g. classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constraint the interpretation and well-formed use of these terms. Formally it can be said that an ontology is a statement of a logical theory [20].
對(duì)這些系統(tǒng)來(lái)說(shuō),存在的就是那些可以被表示的。當(dāng)某個(gè)領(lǐng)域的知識(shí)以聲明的形式表示時(shí),那些可以表示的對(duì)象的集合就稱為universe of discourse。這些對(duì)象集以及它們之間可描述的關(guān)系,可以用描述性詞匯來(lái)表示,這種詞匯被用于基于知識(shí)的系統(tǒng)表達(dá)知識(shí)。因此,在人工智能環(huán)境下,可以通過(guò)定義一套描述性術(shù)語(yǔ)來(lái)描繪程序的本體。在這種本體中,定義與universe of discourse中的實(shí)體名相交互,用人類可讀的文本來(lái)描述這些名字的含義,描述普遍真理,而這些真理規(guī)定了如何理解和正確使用這些術(shù)語(yǔ)。正規(guī)一些,我們可以說(shuō)本體是對(duì)邏輯理論的闡述。 Ontologies are often equated with taxonomic hierarchies of classes without class definitions and the subsumption relation. Ontologies need not to be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world. To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. 本體常常等同于沒(méi)有類的定義也不包括它們之間的關(guān)系的類的分類等級(jí)。然而本體并不局限于此形式。它也不只限于保守的定義,即在傳統(tǒng)邏輯意義上的只包括術(shù)語(yǔ)而不附加任何關(guān)于知識(shí)的定義。要詳細(xì)說(shuō)明概念化,必須說(shuō)明那些對(duì)定義項(xiàng)目的理解進(jìn)行限制的公理。 Pragmatically, a common ontology defines the vocabulary with which queries and as- sertions are exchanged among agents. The agents sharing a vocabulary need not share a knowledge base. An agent that commits to an ontology is not required to answer all queries that can be formulated in the shared vocabulary. In short, a commitment to a common ontol- ogy is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology. 實(shí)際運(yùn)用中,一個(gè)一般性的本體定義代理之間進(jìn)行詢問(wèn)和聲明所用的詞匯表。共享詞匯表的代理之間不需要共享一個(gè)知識(shí)庫(kù)。遵循某個(gè)本體的代理也不需要能夠回答用共享詞匯表所構(gòu)成的所有問(wèn)題。總之,遵循一般性本體是連貫性的保證,但不是完整性的保證。
2.1.3 Ontology as a Representational Vocabulary
2.1.3作為代表性詞匯的本體 The third definition of ontology proposed above says that it is in fact a representational vo- cabulary [8, 31]. The vocabulary can be specialized to some domain or subject matter.
More
precisely, it is not the vocabulary as such that qualifies as an ontology, but the conceptu- alization that the terms in the vocabulary are intended to capture. Thus, translating the terms in an ontology from one language to another, for example from Czech to English, does not change the ontology conceptually.
In engineering design, one might discuss the ontology of an electronic devices domain, which might include vocabulary that describes conceptual
elements — transistors, operational amplifiers, and voltages — and the relations between these elements — operational amplifiers are a type-of electronic device, and transistors are component-of operational amplifiers. Identifying such a vocabulary and the underlying con- ceptualization generally requires careful analysis of the kinds of objects and relations that can exist in the domain.
上述本體的第三個(gè)定義認(rèn)為本體實(shí)際上是一種代表性的詞匯。這種詞匯可以應(yīng)用于特定領(lǐng)域或者主題。更確切的說(shuō),它不是像本體那樣嚴(yán)格定義的詞匯,而是一種概念化,這種概念化是詞匯表中的術(shù)語(yǔ)想要抽取出來(lái)的。因此,將這些術(shù)語(yǔ)用本體的形式在不同語(yǔ)言間翻譯時(shí),比如由捷克語(yǔ)譯成英語(yǔ),并不從概念上改變本體。在工程設(shè)計(jì)中,或許會(huì)討論到電子設(shè)備領(lǐng)域的本體,它包含一些描述基本概念的詞匯,比如晶體管,運(yùn)算放大器,電壓等;也包含這些基本元素間的關(guān)系,運(yùn)算放大器是電子設(shè)備的一種,而晶體管是運(yùn)算放大器的組件。一般來(lái)說(shuō),識(shí)別這種詞匯和潛在的概念需要仔細(xì)分析領(lǐng)域內(nèi)存在的各種對(duì)象和關(guān)系。
The term ontology is sometimes used to refer to a body of knowledge describing some domain (see below), typically a common sense knowledge domain, using a representational vocabulary. For example, CYC [45] often refers to its knowledge representation of some area of knowledge as its ontology. In other words, the representation vocabulary provides a set of terms with which one can describe the facts in some domain, while the body of knowledge using that vocabulary is a collection of facts about a domain. However, this distinction is not as clear as it might first appear. In the electronic-device example, that transistor is a component-of operational amplifier or that the latter is a type-of electronic device is just as much a fact about its domain as a CYC fact about some aspect of space, time or numbers. The distinction is that the former emphasizes the use of ontology as a set of terms for representing specific facts in an instance of the domain, while the latter emphasizes the view of ontology as a general set of facts to be shared.
本體這一術(shù)語(yǔ)有時(shí)候用于指描述某個(gè)領(lǐng)域的知識(shí)主體。比如,CYC常將它對(duì)某個(gè)領(lǐng)域知識(shí)的表示稱為本體。也就是說(shuō),表示詞匯提供了一套用于描述領(lǐng)域內(nèi)事實(shí)的術(shù)語(yǔ),而使用這些詞匯的知識(shí)主體是這個(gè)領(lǐng)域內(nèi)事實(shí)的集合。但是,它們之間的這種區(qū)別并不明顯。在電子設(shè)備的例子中,晶體管是運(yùn)算放大器的一個(gè)組件,或者運(yùn)算放大器是一種電子設(shè)備也可以是領(lǐng)域內(nèi)的一種事實(shí),就像關(guān)于宇宙,時(shí)間或者數(shù)字的CYC事實(shí)一樣。兩者的區(qū)別在于,前者強(qiáng)調(diào)本體作為表現(xiàn)領(lǐng)域內(nèi)特定事實(shí)的術(shù)語(yǔ)集而使用,而后者則強(qiáng)調(diào)本體是可以共享的普遍的事實(shí)的集合。
2.1.4 Ontology as a Body of Knowledge
2.1.4作為知識(shí)主體的本體 Sometimes, ontology is defined as a body of knowledge describing some domain, typically a common sense knowledge domain, using a representation vocabulary as described above. In this case, an ontology is not only the vocabulary, but the whole “upper” knowledge base (including the vocabulary that is used to describe this knowledge base). The typical example of this definition usage is project CYC (http://www.cyc.com/, [45]) that defines its knowledge base as an ontology for any other knowledge based system. CYC is the name of a very large, multi-contextual knowledge base and inference engine. The development of CYC started during the early 1980s headed by Douglas Lenat. CYC is an attempt to do symbolic AI on a massive scale. It is neither based on numerical methods such as statistical probabilities, nor is it based on neural networks or fuzzy logic. All of the knowledge in CYC is represented declaratively in the form of logical assertions. CYC contains over 400; 000 significant assertions [45], which include simple statements of fact, rules about what conclusions to draw if certain statements of fact are satisfied (true), and rules about how to reason with certain types of facts and rules. New conclusions are derived by the inference engine using deductive reasoning. The CYC team doesn’t believe there is any shortcut toward being intelligent or creating an artificial intelligence based agent. Addressing the need for a large body of knowledge with content and context may only be done by manually organizing and collating information.
有時(shí)候,本體被定義為描述某個(gè)領(lǐng)域的知識(shí),通常是一般意義上的知識(shí)領(lǐng)域,它使用上面提到的表示性詞匯。這時(shí),一個(gè)本體不僅僅是詞匯表,而是整個(gè)上層知識(shí)庫(kù)(包括用于描述這個(gè)知識(shí)庫(kù)的詞匯)。這種定義的典型應(yīng)用是CYC工程,它以本體定義其知識(shí)庫(kù),為其他知識(shí)庫(kù)系統(tǒng)所用。CYC是一個(gè)巨型的,多關(guān)系型知識(shí)庫(kù)和推理引擎。CYC的開(kāi)發(fā)早在80年代就已經(jīng)開(kāi)始,重要負(fù)責(zé)人是Douglas Lenat。CYC是大型的符號(hào)型人工智能的一次嘗試。它不是基于數(shù)字方法,比如概率統(tǒng)計(jì),也不是基于神經(jīng)網(wǎng)絡(luò)或者模糊邏輯。 CYC中所有的知識(shí)都以邏輯聲明的形式表示。CYC包含400,000多個(gè)關(guān)鍵聲明,這其中包含對(duì)事實(shí)的簡(jiǎn)單陳述,關(guān)于滿足特定事實(shí)陳述時(shí)得出何種結(jié)論的規(guī)則,以及關(guān)于通過(guò)一定類型的事實(shí)和規(guī)則如何推理的標(biāo)準(zhǔn)。新的結(jié)論由推理引擎通過(guò)演繹推理得到。CYC小組不相信在通往智能化或創(chuàng)造基于人工智能的代理的途中存在什么捷徑。他們強(qiáng)調(diào)需要有大型的內(nèi)容知識(shí)主體,而聯(lián)系只能通過(guò)手工組織和比較信息而獲得。
This knowledge includes heuristic, rule of thumb problem solving strategies, as well as facts that can only be known to a machine if it is told. Much of the useful common sense knowledge needed for life is prescientific and has there- fore not been analyzed in detail. Thus a large part of the work of the CYC project is to formalize common relationships and fill in the gaps between the highly systematized knowl- edge used by specialists. It is not necessary to divide such a large knowledge base into smaller pieces to enable reasoning in reasonable time. Because of this, the CYC knowledge base uses a special context space [29], that is divided by 12 dimensions into smaller pieces (contexts) that have something in common and can be used to reason about a specific problem in that context. It is possible to “lift” assertion from one context to another when the problem requires it. The CYC common sense knowledge can be used as a body of a knowledge base for any knowledge intensive system. In this sense, this body of knowledge can be viewed as an ontology of the knowledge base of the system.
這種知識(shí)包括啟發(fā)、問(wèn)題解決策略的檢索規(guī)則,也包含只能被機(jī)器理解的事實(shí)。生活中需要的常識(shí)知識(shí)大部分是近代科學(xué)以前的,因此尚未詳細(xì)分析。所以CYC很大一部分工作就是格式化一般的關(guān)系并填補(bǔ)它與專家使用的高度系統(tǒng)化的知識(shí)間的空白。為了在合理時(shí)間內(nèi)完成推理而將這樣一個(gè)大型的知識(shí)庫(kù)分割成小部分是不必要的。為此,CYC知識(shí)庫(kù)使用特殊的關(guān)系空間,這一空間被十二個(gè)因素分割成小塊兒(關(guān)系),每個(gè)小塊有共同點(diǎn),可以用來(lái)推理特定的問(wèn)題。在需要的時(shí)候也可以將聲明從一個(gè)關(guān)系塊轉(zhuǎn)換到另一個(gè)關(guān)系塊。CYC常識(shí)知識(shí)庫(kù)可以被用作任何知識(shí)密集型系統(tǒng)的知識(shí)主體。在這個(gè)意義上,知識(shí)主體可以被看成系統(tǒng)知識(shí)庫(kù)的本體。
2.2 Other Ontology Definitions
/* 2.2 其它本體定義*/ 正如我們從上述討論中所見(jiàn),還沒(méi)有明確的對(duì)本體的準(zhǔn)確定義,然而可以看出上述定義有許多共同之處。除了上述定義外還有許多對(duì)本體定義的其它說(shuō)法。[24]中收集的一些其它的定義有:1.非正式的概念體系 2.正式的語(yǔ)義說(shuō)明3. 對(duì)概念體系用邏輯性的理論進(jìn)行描述 (a) 用特定格式的屬性表現(xiàn)其特征(b) 僅按其特定的目標(biāo)進(jìn)行特征描述4. 邏輯性理論所采用的詞匯表5. 邏輯理論的規(guī)范。定義1和定義2將一個(gè)本體視為一個(gè)概念的“語(yǔ)義”實(shí)體,正式或 非正式的,而概念3,4和5的闡述則是一個(gè)具體的“語(yǔ)法”對(duì)象。根據(jù) 定義1,一個(gè)本體是一個(gè)被設(shè)想成能夠由特定知識(shí)庫(kù)支持的概念體系。而定義2則認(rèn)為有知識(shí)庫(kù)支持的本體在語(yǔ)義層根據(jù)適當(dāng)形式的結(jié)構(gòu)予以表示。在上述2定義下,我們都可以說(shuō)“知識(shí)庫(kù)A的本體與知識(shí)庫(kù)B的本體不同”。在定義3下,一個(gè)本體僅是一個(gè)邏輯理論。問(wèn)題在于這樣一個(gè)理論要成為本體是否需要有特殊格式的屬性,或是否以讓人將一個(gè)邏輯理論作為本體考慮為目標(biāo)。 后者可以由一個(gè)本體是關(guān)于事物的加注解和索引的聲明的集合的辯論來(lái)支持: “離開(kāi)注解和索引,它變成一個(gè)聲明的集合:邏輯上何謂理論。(Pat Hayes 在 [24]中闡述的). 根據(jù)定義4,一個(gè)本體不作為一個(gè)邏輯理論,而是作為邏輯理論使用的詞匯表。如果一個(gè)本體被視為一個(gè)包含一系列邏輯定義的詞匯規(guī)范,則此定義轉(zhuǎn)化為3.a。可以預(yù)測(cè)當(dāng)概念化試圖作為詞匯表時(shí)Gruber的定義描述(概念化規(guī)范)也將轉(zhuǎn)化為3.a。最后,在定義5下,基于一種認(rèn)識(shí):它指定了在特定領(lǐng)域的理論中使用的“構(gòu)件”,一個(gè)本體被視為一個(gè)邏輯理論的規(guī)范*/*/As we can see from the above discussions, the exact definition of ontology is not obvious, however it can be seen that the definitions have much in common. In addition to the above definitions there are many other proposals for ontology definitions. Some other definitions collected from [24] are: 1. informal conceptual system 2. formal semantic account 3. representation of a conceptual system via a logical theory (a) characterized by specific formal properties (b) characterized only by its specific purposes 4. vocabulary used by a logical theory 5. (meta-level) specification of a logical theory Definitions 1 and 2 conceive an ontology as a conceptual “semantic” entity, either formal or informal, while according to the interpretations 3, 4 and 5 is a specific “syntactic” object. According to interpretation 1, an ontology is the conceptual system which may be assumed to underlay a particular knowledge base. Under interpretation 2, instead, the ontology, that underlies a knowledge base, is expressed in terms of suitable formal structures at the semantic level. In both cases, we may say that “the ontology of knowledge base A is di?erent from that of knowledge base B”. Under interpretation 3, an ontology is nothing else then a logical theory. The issue is whether such a theory needs to have particular formal properties in order to be an ontology or, rather, whether it is the intended purpose which lets us consider a logical theory as an ontology. The latter position can be supported by arguing that an ontology is an annotated and indexed set of assertion about something: “leaving o? the annotations and indexing, this is a collection of assertions: what in logic is called a theory” (Pat Hayes statement in [24]). According to interpretation 4, an ontology is not viewed as a logical theory, but just as the vocabulary used by a logical theory. Such an interpretation collapses into 3.a if an ontology is thought of as a specification of a vocabulary consisting of a set of logical definitions. We may anticipate that the Gruber’s interpretation (specification of conceptualization) collapses into 3.a as well when a conceptualization is intended as a vocabulary. Finally, under interpretation 5, an ontology is seen as a specification of a logical theory in the sense that it specifies the “architectural components” (or primitives) used within a particular domain theory. */
3 Ontology Structure
From the overview above we can see that an ontology can be perceived in basically two approaches. The first approach is an ontology as a representational vocabulary, where the conceptual structure of terms should remain unchanged during translation. The other ap- proach, that is discussed in this section, is an ontology as the body of knowledge describing a domain, in particular a common sense domain. An ontology can be divided in several ways. We will describe some of the proposals here. Particularly interesting is so called “upper ontology” that is intended to serve as an upper part of ontology of practically all knowledge based systems. Some of the ways of dividing presented here are intended to be used for merging to form an upper ontology standard in the IEEE Standard Upper Ontology Study Group [39]. On pages linked from [39] there are many other examples that could be used as some kind of an upper ontology. 根據(jù)以上看法可以得出一個(gè)本體基本上可以通過(guò)兩個(gè)步聚來(lái)認(rèn)識(shí)。第一個(gè)步驟是本體是一個(gè)抽象詞匯表,在這個(gè)詞匯表里術(shù)語(yǔ)的概念結(jié)構(gòu)在轉(zhuǎn)換的過(guò)程中應(yīng)該保持不變。另一個(gè)步聚就是本節(jié)需要討論的,本體是用來(lái)描述一個(gè)領(lǐng)域,特別是一個(gè)公共領(lǐng)域的一個(gè)知識(shí)體系。本體有幾中劃分方式。我們將在這里來(lái)討論一些劃分的建議。特別有趣的是一種“上層本體”,它試圖用作幾乎所有的基于知識(shí)的系統(tǒng)的本體的上層部分。在IEEE標(biāo)準(zhǔn)上層本體研究組中所描述的一些劃分本體的方式試圖用來(lái)合并成一個(gè)上層本體標(biāo)準(zhǔn)。在[39]的鏈接網(wǎng)頁(yè)上有很多其它的例子可以作為一個(gè)上層本體。(感覺(jué)翻譯不太好!) (figure 1)
Figure 1: How ontologies di?er in their analyses of the most general concepts [8] It is interesting that many authors agree that the upper class1 of the ontology is “thing”, however even in the second level they do not agree on the separation, as can be seen in the figure 1. The initiative [39] tries to unify these views.
3.1 CYC
The ontology of CYC is based on a several terms that form the fundamental vocabulary of the CYC knowledge base. The universal set is #$Thing2 (see figure 1). It is the set of everything. Every CYC constant in the knowledge base is a member of this collection. In the prefix notation of the language CycL [10], we express that fact as (#$isa CONST #$Thing). Thus, too, every collection in the knowledge base is a subset of the collection #$Thing. In CycL, that fact is expressed as (#$genls COL #$Thing). The set #$Thing has some subsets, such as PathGeneric, Intangible, Individual, Sim- pleSegmentOfPath, PathSimple, MathematicalOrComputationalThing, IntangibleIndividual, Product, TemporalThing, SpatialThing, Situation, EdgeOnObject, FlowPath, ComputationalObject, Microtheory, plus about 1500 more public subsets and about 13600 unpublished subsets.
- $Individual is the collection of all things that are not sets or collections. Thus,
- $Individual includes (among other things) physical objects, temporal subabstractions of
physical objects, numbers, relations, and groups (#$Group). An element of #$Individual may have parts or a structure (including parts that are discontinuous), but no instance of
- $Individual can have elements or subsets.
- $Collection is the collection of all CYC collections. CYC collections are natural kinds
or classes, as opposed to mathematical sets. Their elements have some common attribute(s). Each CYC collection is like a set in so far as it may have elements, subsets, and supersets, and may not have parts or spatial or temporal properties. Sets, however, di?er from collections in that a mathematical set may be an arbitrary set of things which have nothing in common (#$Set-Mathematical). In contrast, the elements of a collection will all have in common some feature(s), some ‘intensional’ qualities. In addition, two instances of #$Collection can be co-extensional (i.e. have all the same elements) without being identical, whereas if two arbitrary sets had the same elements, they would be considered equal.
- $Individual and #$Collection are disjoint collections. No CYC constant can be an
instance of both.
- $Predicate is the set of all CYC predicates. Each element of #$Predicate is a truth-
functional relationship in CYC which takes some number of arguments. Each of those argu- ments must be of some particular type. Informally, one can think of elements of #$Predicate as functions that always return either true or false. More formally, when an element of
- $Predicate is applied to the legal number and type of arguments, an expression is formed
which is a well-formed formula (w?) in CycL. Such expressions are called atomic formulas if they contain variables, or ground atomic formulas (gaf) if they contain no variables.
- $isa:<#$ReifiableTerm> <#$Collection> expresses the ISA relationship. (#$isa EL
COL) means that EL is an element of the collection COL. CYC knows that #$isa distributes over #$genls. That is, if one asserts (#$isa EL COL) and (#$genls COL SUPER), CYC will infer that (#$isa EL SUPER). Therefore, in practice one only manually asserts a small fraction of the #$isa assertions — the vast majority are inferred automatically by CYC.
- $genls:<#$Collection> <#$Collection> expresses similar relationship for collections
(generalization). (#$genls COL SUPER) means that SUPER is one of the supersets of COL. Both arguments must be elements of #$Collection. Again, as with the #$isa, CYC knows that #$genls is transitive, therefore, in practice one only manually asserts a small fraction of the #$genls assertions since the rest is inferred inferred automatically. More details about the structure of the CYC ontology and about how the CYC knowledge base is constructed can be found at http://www.cyc.com.
3.2 Russell & Norvig’s General Ontology Russell & Norvig’大本體
Yet another view of general ontology structure is presented in Russell & Norvig’s book [38]. Every category of their ontology (see figure 2) is discussed in detail on example axioms. An example of this ontology in KIF [18] can be found at http://ltsc.ieee.org/suo/ ontologies/Russell-Norvig.txt.
在Russell & Norvig的書(shū) [38] 中提及了另一種關(guān)于大本體結(jié)構(gòu)的觀點(diǎn)。每個(gè)類別都有各自的本體(見(jiàn)圖2),這在例程公理中已詳細(xì)討論過(guò)了。
這種本體的KIF [18]可以在
Russell-Norvig.txt (http://ltsc.ieee.org/suo/ontologies/Russell-Norvig.txt) 找到。
(Figure 2)
Figure 2: Russell & Norvig’s general ontology structure [38] 圖2:Russell & Norvig的大本體結(jié)構(gòu) [38]
3.3 Ontology Engineering
3.3 本體工程
Ontology engineering is a field in artificial intelligence or computer science that is concerned with ontology creation and usage. Report [31], that proposes and comments this field, declares that the ultimate purpose of ontology engineering should be “to provide a basis of building models of all things in which computer science is interested”.
本體工程是人工智能或者計(jì)算機(jī)科學(xué)的一個(gè)領(lǐng)域, 它關(guān)注于本體的建立和使用. 在Report [31]中提出了這一新的領(lǐng)域并對(duì)其進(jìn)行了注解,它宣稱本體工程的終極目標(biāo)應(yīng)該是"為計(jì)算機(jī)科學(xué)感興趣的所有事物提供一個(gè)建立模型的基礎(chǔ)".
3.3.1 Structure of Usage
3.3.1 用法的結(jié)構(gòu)
An ontology can be divided into following subcategories according to [31] from the knowledge reuse and ontology engineering point of view as follows. This is rather a structure of ontologies from a point of view of their usage than a division of one general ontology. Some examples are included.
根據(jù) [31]從知識(shí)重用和本體論工程指出的如下觀點(diǎn),本體論可以被分成以下子類。與其說(shuō)是一個(gè)通用本體的分類,不如說(shuō)是一個(gè)通過(guò)它們的用途劃分的本體結(jié)構(gòu)。包括一些例子。
? Workplace Ontology
工作場(chǎng)所本體
This is an ontology for workplace which a?ects task characteristics by specifying several boundary conditions which characterize and justify problem solving behaviour in the workplace. Workplace and task ontologies collectively specify the context in which domain knowledge is intended and used during the problem solving. Examples from circuit troubleshooting: fidelity, e?ciency, precision, high reliability. ? Task Ontology Task ontology is a system of vocabulary for describing problem solving structure of all the existing tasks domain independently. It does not cover the control structure. It covers components or primitives of unit inferences taking place during performing tasks. Task knowledge in turn specifies domain knowledge by giving roles to each objects and relations between them. Examples from scheduling tasks: schedule recipient, schedule resource, goal, constraint, availability, load, select, assign, classify, remove, relax, add.
? Domain ontology Domain ontology can be either task dependent or task independent. Task independent ontology usually relates to activities of objects. – Task-dependent ontology A task structure requires not all the domain knowledge but some specific domain knowledge in a certain specific organization. This special type of domain knowledge can be called task-domain ontology because it depends on the task. Examples from job-shop scheduling: job, order, line, due date, machine availability, tardiness, load, cost. – Task-independent ontology ? Activity-related ontology ? Object ontology. This ontology covers the structure, behaviour and function of the object. Examples from circuit boards: component, connection, line, chip, pin, gate, bus, state, role. ? Activity ontology. Examples from enterprise ontology: use, consume, produce, release, state, resource, commit, enable, complete, disable. ? Activity-independent ontology ? Field ontology. This ontology is related to theories and principles which govern the domain. It contains primitive concepts appearing in the theories and relations, formulas, and units constituting the theories and principles. ? Units ontology. Examples: mole, kilogram, meter, ampere, radian. ? Engineering mathematics ontology. Examples: linear algebra, physical quantity, physical dimension, unit of measure, scalar quantity, physical components. ? General or Common ontology Examples: things, events, time, space, causality or behaviour, function etc.
3.3.2 Ontology Engineering Subfields
We can also divide the ontology or ontologies from the point of view of ontology engineering as a field. The subjects which should be covered by ontology engineering are demonstrated in [31]. It includes basic issues in philosophy, knowledge representation, ontology design, standardization, EDI, reuse and sharing of knowledge, media integration, etc. which are the essential topics in the future knowledge engineering. Of course, they should be constantly refined through further development of ontology engineering. ? Basic Subfield – Philosophy(Ontology, Meta-mathematics) Ontology which philosophers have discussed since Aristotle is discussed as well as logic and meta-mathematics.
– Scientific philosophy Investigation on Ontology from the physics point of views, e.g., time, space, pro- cess, causality, etc. is made. – Knowledge representation Basic issues on knowledge representation, especially on representation of ontologi- cal stu?, are discussed. ? Subfield of Ontology Design – General(Common) ontology General ontologies such as time, space, process, causality, part/whole relation, etc. are designed. Both in-depth investigation on the meaning of every concept and relation and on formal representation of ontologies are discussed. – Domain ontologies Various ontologies in, say, Plant, Electricity, Enterprise, etc. are designed. ? Subfield of Common Sense Knowledge – Parallel to general ontology design, common sense knowledge is investigated and collected and knowledge bases of common sense are built. ? Subfield of Standardization – EDI (Electronic Data Interchange) and data element specification Standardization of primitive data elements which should be shared among people for enabling full automatic EDI. – Basic semantic repository Standardization of primitive semantic elements which should be shared among people for enabling knowledge sharing. – Conceptual schema modeling facility (CSMF) – Components for qualitative modeling Standardization of functional components such as pipe, valve, pump, boiler, regis- ter, battery, etc. for qualitative model building. ? Subfield of Data or Knowledge Interchange – Translation of ontology Translation methodologies of one ontology into another are developed. – Database transformation Transformation of data in a data base into another of di?erent conceptual schema. – Knowledge base transformation Transformation of a knowledge base into another built based on a di?erent ontology. ? Subfield of Knowledge Reuse – Task ontology Design of ontology for describing and modeling human ways of problem solving.
– T-domain ontology Task-dependent domain ontology is designed under some specific task context. – Methodology for knowledge reuse Development of methodologies for knowledge reuse using the above two ontologies. ? Subfield of Knowledge Sharing – Communication protocol Development of communication protocols between agents which can behave coop- eratively under a goal specified. – Cooperative task ontology Task ontology design for cooperative communication ? Subfield of Media Integration – Media ontology Ontologies of the structural aspects of documents, images, movies, etc. are de- signed. – Common ontologies of content of the media Ontologies common to all media such as those of human behavior, story, etc. are designed. – Media integration Development of meaning representation language for media and media integration through understanding media representation are done. ? Subfield of Ontology Design Methodology – Methodology – Support environment ? Subfield of ontology evaluation – Evaluation of ontologies designed is made using the real world problems by forming a consortium.
posted on 2008-02-01 21:44
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