Ontology【汉语译文：知识本体（in computer science） 本体论（in philosophy）】 这个术语表达的概念， 不仅在自然人形上哲学领域已经有了显著进展， 而且在计算机人工智能领域更得到了长足发展。 附件1： http://en.wikipedia.org/wiki/Ontology Ontology (from the Greek ὄν , genitive ὄντος : "of that which is", and -λογία , -logia : science , study , theory ) is the philosophical study of the nature of being , existence or reality as such, as well as the basic categories of being and their relations. Traditionally listed as a part of the major branch of philosophy known as metaphysics , ontology deals with questions concerning what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy , and subdivided according to similarities and differences. Contents 1 Overview 1.1 Some fundamental questions 1.2 Concepts 2 History of ontology 2.1 Etymology 2.2 Origins 2.2.1 Parmenides and Monism 2.2.2 Ontological pluralism 2.2.3 Plato 2.2.4 Aristotle 3 Other ontological topics 3.1 Ontological and epistemological certainty 3.2 Body and environment, questioning the meaning of being 4 Prominent ontologists 5 See also 6 References 7 External links 附件2： http://en.wikipedia.org/wiki/Ontology_(information_science ) 附件3 ： http://www-ksl.stanford.edu/kst/what-is-an-ontology.html In computer science and information science , an ontology is a formal representation of knowledge as a set of concepts within a domain , and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain. In theory, an ontology is a "formal, explicit specification of a shared conceptualisation". An ontology provides a shared vocabulary, which can be used to model a domain — that is, the type of objects and/or concepts that exist, and their properties and relations. Ontologies are the structural frameworks for organizing information and are used in artificial intelligence , the Semantic Web , systems engineering , software engineering , biomedical informatics , library science , enterprise bookmarking , and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework . Contents 1 Overview 2 History 3 Ontology components 4 Domain ontologies and upper ontologies 5 Ontology engineering 6 Ontology languages 7 Examples of published ontologies 8 Ontology libraries 9 Examples of applications using ontology engines 10 See also 11 References 12 Further reading 13 External links What is an Ontology? This definition was originally proposed in 1992 and posted as shown below. See an updated definition of ontology (computer science) that accounts for the literature before and after that posting, with links to further readings. Tom Gruber gruber@ksl.stanford.edu Short answer: An ontology is a specification of a conceptualization. The word "ontology" seems to generate a lot of controversy in discussions about AI. It has a long history in philosophy, in which it refers to the subject of existence. It is also often confused with epistemology, which is about knowledge and knowing. In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization . That is, 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. And it is certainly a different sense of the word than its use in philosophy. What is important is what an ontology is for . My colleagues and I have been designing ontologies for the purpose of enabling knowledge sharing and reuse. In that context, an ontology is a specification used for making ontological commitments. The formal definition of ontological commitment is given below. For pragmetic reasons, we choose to write an ontology as a set of definitions of formal vocabulary. Although this isn't the only way to specify a conceptualization, it has some nice properties for knowledge sharing among AI software (e.g., semantics independent of reader and context). Practically, an ontological commitment is an agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology. We build agents that commit to ontologies. We design ontologies so we can share knowledge with and among these agents. This definition is given in the article: T. R. Gruber. A translation approach to portable ontologies. Knowledge Acquisition , 5(2):199-220, 1993. Available on line . A more detailed description is given in T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing. Presented at the Padua workshop on Formal Ontology, March 1993, later published in International Journal of Human-Computer Studies , Vol. 43, Issues 4-5, November 1995, pp. 907-928. Available online . With an excerpt attached. Ontologies as a specification mechanism A body of formally represented knowledge is based on a conceptualization : the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them (Genesereth Nilsson, 1987) . 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. An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. For AI 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 constrain the interpretation and well-formed use of these terms. Formally, an ontology is the statement of a logical theory. We use common ontologies to describe ontological commitments for a set of agents so that they can communicate about a domain of discourse without necessarily operating on a globally shared theory. We say that an agent commits to an ontology if its observable actions are consistent with the definitions in the ontology. The idea of ontological commitments is based on the Knowledge-Level perspective (Newell, 1982) . The Knowledge Level is a level of description of the knowledge of an agent that is independent of the symbol-level representation used internally by the agent. Knowledge is attributed to agents by observing their actions; an agent "knows" something if it acts as if it had the information and is acting rationally to achieve its goals. The "actions" of agents---including knowledge base servers and knowledge-based systems--- can be seen through a tell and ask functional interface (Levesque, 1984) , where a client interacts with an agent by making logical assertions (tell), and posing queries (ask). Pragmatically, a common ontology defines the vocabulary with which queries and assertions are exchanged among agents. Ontological commitments are agreements to use the shared vocabulary in a coherent and consistent manner. The agents sharing a vocabulary need not share a knowledge base; each knows things the other does not, and 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 ontology is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology. Notes Ontologies are often equated with taxonomic hierarchies of classes, but class definitions, and the subsumption relation, but ontologies need not 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 (Enderton, 1972) . To specify a conceptualization one needs to state axioms that do constrain the possible interpretations for the defined terms. 附件:4： http://tomgruber.org/writing/ontology-definition-2007.htm Ontology by Tom Gruber in the Encyclopedia of Database Systems , Ling Liu and M. Tamer zsu (Eds.), Springer- Verlag , 2009. Synonyms computational ontology, semantic data model, ontological engineering Definition In the context of computer and information sciences, an ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals. Ontologies are typically specified in languages that allow abstraction away from data structures and implementation strategies; in practice, the languages of ontologies are closer in expressive power to first-order logic than languages used to model databases. For this reason, ontologies are said to be at the "semantic" level, whereas database schema are models of data at the "logical" or "physical" level. Due to their independence from lower level data models, ontologies are used for integrating heterogeneous databases, enabling interoperability among disparate systems, and specifying interfaces to independent, knowledge-based services. In the technology stack of the Semantic Web standards , ontologies are called out as an explicit layer. There are now standard languages and a variety of commercial and open source tools for creating and working with ontologies. Historical Background The term "ontology" comes from the field of philosophy that is concerned with the study of being or existence. In philosophy, one can talk about an ontology as a theory of the nature of existence (e.g., Aristotle's ontology offers primitive categories, such as substance and quality, which were presumed to account for All That Is). In computer and information science, ontology is a technical term denoting an artifact that is designed for a purpose, which is to enable the modeling of knowledge about some domain, real or imagined. The term had been adopted by early Artificial Intelligence (AI) researchers, who recognized the applicability of the work from mathematical logic and argued that AI researchers could create new ontologies as computational models that enable certain kinds of automated reasoning . In the 1980's the AI community came to use the term ontology to refer to both a theory of a modeled world (e.g., a Nave Physics ) and a component of knowledge systems. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy . In the early 1990's, an effort to create interoperability standards identified a technology stack that called out the ontology layer as a standard component of knowledge systems . A widely cited web page and paper associated with that effort is credited with a deliberate definition of ontology as a technical term in computer science. The paper defines ontology as an "explicit specification of a conceptualization," which is, in turn, "the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold among them." While the terms specification and conceptualization have caused much debate, the essential points of this definition of ontology are An ontology defines (specifies) the concepts, relationships, and other distinctions that are relevant for modeling a domain. The specification takes the form of the definitions of representational vocabulary (classes, relations, and so forth), which provide meanings for the vocabulary and formal constraints on its coherent use. One objection to this definition is that it is overly broad, allowing for a range of specifications from simple glossaries to logical theories couched in predicate calculus . But this holds true for data models of any complexity; for example, a relational database of a single table and column is still an instance of the relational data model. Taking a more pragmatic view, one can say that ontology is a tool and product of engineering and thereby defined by its use. From this perspective, what matters is the use of ontologies to provide the representational machinery with which to instantiate domain models in knowledge bases, make queries to knowledge-based services, and represent the results of calling such services. For example, an API to a search service might offer no more than a textual glossary of terms with which to formulate queries, and this would act as an ontology . On the other hand, today's W3C Semantic Web standard suggests a specific formalism for encoding ontologies (OWL), in several variants that vary in expressive power . This reflects the intent that an ontology is a specification of an abstract data model (the domain conceptualization) that is independent of its particular form. Scientific Fundamentals Ontology is discussed here in the applied context of software and database engineering, yet it has a theoretical grounding as well. An ontology specifies a vocabulary with which to make assertions, which may be inputs or outputs of knowledge agents (such as a software program). As an interface specification , the ontology provides a language for communicating with the agent. An agent supporting this interface is not required to use the terms of the ontology as an internal encoding of its knowledge. Nonetheless, the definitions and formal constraints of the ontology do put restrictions on what can be meaningfully stated in this language. In essence, committing to an ontology (e.g. supporting an interface using the ontology's vocabulary) requires that statements that are asserted on inputs and outputs be logically consistent with the definitions and constraints of the ontology . This is analogous to the requirement that rows of a database table (or insert statements in SQL) must be consistent with integrity constraints, which are stated declaratively and independently of internal data formats. Similarly, while an ontology must be formulated in some representation language, it is intended to be a semantic level specification -- that is, it is independent of data modeling strategy or implementation. For instance, a conventional database model may represent the identity of individuals using a primary key that assigns a unique identifier to each individual. However, the primary key identifier is an artifact of the modeling process and does not denote something in the domain. Ontologies are typically formulated in languages which are closer in expressive power to logical formalisms such as the predicate calculus. This allows the ontology designer to be able to state semantic constraints without forcing a particular encoding strategy. For example, in typical ontology formalisms one would be able to say that an individual was a member of class or has some attribute value without referring to any implementation patterns such as the use of primary key identifiers. Similarly, in an ontology one might represent constraints that hold across relations in a simple declaration (A is a subclass of B), which might be encoded as a join on foreign keys in the relational model. Ontology engineering is concerned with making representational choices that capture the relevant distinctions of a domain at the highest level of abstraction while still being as clear as possible about the meanings of terms. As in other forms of data modeling, there is knowledge and skill required. The heritage of computational ontology in philosophical ontology is a rich body of theory about how to make ontological distinctions in a systematic and coherent manner. For example, many of the insights of "formal ontology" motivated by understanding "the real world" can be applied when building computational ontologies for worlds of data . When ontologies are encoded in standard formalisms, it is also possible to reuse large, previously designed ontologies motivated by systematic accounts of human knowledge or language . In this context, ontologies embody the results of academic research, and offer an operational method to put theory to practice in database systems. Key Applications Ontologies are part of the W3C standards stack for the Semantic Web, in which they are used to specify standard conceptual vocabularies in which to exchange data among systems, provide services for answering queries, publish reusable knowledge bases, and offer services to facilitate interoperability across multiple, heterogeneous systems and databases. The key role of ontologies with respect to database systems is to specify a data modeling representation at a level of abstraction above specific database designs (logical or physical), so that data can be exported, translated, queried, and unified across independently developed systems and services. Successful applications to date include database interoperability, cross database search, and the integration of web services. Cross References data model, data modeling, knowledge base, knowledge engineering Recommended Reading Berners-Lee, T., Hendler , J. and Lassila, O. The Semantic Web , Scientific American , May 2001. Also http://www.w3.org/2001/sw/ Gruber, T. R., A Translation Approach to Portable Ontology Specifications . Knowledge Acquisition , 5(2):199-220, 1993. See also What is an Ontology? http://www-ksl.stanford.edu/kst/what-is-an-ontology.html Gruber, T. R., Toward Principles for the Design of Ontologies Used for Knowledge Sharing . International Journal Human-Computer Studies , 43(5-6):907-928, 1995. Guarino , N. Formal Ontology, Conceptual Analysis and Knowledge Representation , International Journal of Human-Computer Studies , 43(5-6):625–640, 1995. Hayes, P. J. The Second Naive Physics Manifesto, in Hobbs and Moore (eds.), Formal Theories of the Common-Sense World , Norwood: Ablex , 1985. McCarthy, J. Circumscription -- A Form of Non-Monotonic Reasoning , Artificial Intelligence , 5(13): 27-39, 1980. McGuinness, D. L. and van Harmelen , F. OWL Web Ontology Language . W3C Recommendation 10 February 2004. http://www.w3.org/TR/owl-features/ Neches , R., Fikes , R. E., Finin, T., Gruber, T. R., Patil , R., Senator, T., Swartout , W. R. Enabling technology for knowledge sharing . AI Magazine , 12(3):16-36, 1991. Smith, B. and Welty , C. Ontology---towards a new synthesis . Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS2001). ACM Press, 2001. Sowa, J. F. Conceptual Structures. Information Processing in Mind and Machine, Reading, MA: Addison Wesley, 1984. Standard Upper Ontology Working Group (SUO) IEEE P1600.1, http://suo.ieee.org/ Encyclopedia of Database Systems
请进 http://www.nextbio.com/b/nextbio.nb 这个搜索引擎最关键的是对本体语言和语义网的强大应用，类似于标签的概念，制作用户和搜索item的通用词表。该系统利用个人资料，以及用户交互信息，包括短期和长期的信息，提供搜索推荐。 NextBio 提供了一个创新的平台，生命科学研究人员可以在这个平台上检索、发现、分享公共和专用的数据。 NextBio 的平台无缝整合了强大的工具，具有独特的相关内容，能将信息转化为知识，为新的科学发现提供基础。 NextBio能 帮助科研机构提高科研效率，提高不同科研机构和不同地域间的科研合作。 随着跨学科和学科间的研究越来越普遍，对于科研人员而言，能够轻松掌握其他学科领域的新概念将十分重要。爱思唯尔集团在产品内植入了 NextBio 开发的某些新功能，以方便研究人员根据上下文的意思和前后关联来迅速判断出一些新概念的涵义。 http://en.wikipedia.org/wiki/Nextbio Nextbio From Wikipedia, the free encyclopedia Jump to: navigation , search NextBio Type Private Founded California , USA (2004) Founder(s) Saeid Akhtari Ilya Kupershmidt Mostafa Ronaghi Headquarters Cupertino , California , US Area served Worldwide Key people Saeid Akhtari (President CEO) Ilya Kupershmidt (VP of Product Management) Dr. Satnam Alag (VP of Engineering) Employees 50 Website www.nextbio.com NextBio is a privately owned software company that provides a platform for drug companies and life science researchers to search, discover, and share knowledge across public and proprietary data. It was co-founded by Saeid Akhtari, Ilya Kupershmidt and Mostafa Ronaghi in 2004 and based in Cupertino, California , USA . The NextBio Platform is an ontology -based semantic framework that connects highly heterogeneous data and textual information. The semantic framework is based on gene, tissue, disease and compound ontologies. This framework contains information from different organisms, platforms, data types and research areas that is integrated into and correlated within a single searchable environment using proprietary algorithms. It provides a unified interface for researchers to formulate and test new hypotheses across vast collections of experimental data. According to the company, the enterprise version of the NextBio platform is being used in life science research and development and drug development by researchers and clinicians at: Merck Pharmaceutical , Johnson Johnson Pharmaceutical Research Development, L.L.C., Celgene , Genzyme , Eli Lilly and Company , and Regeneron Pharmaceuticals . This enterprise version allows internal, proprietary data to be uploaded and integrated into the NextBio database of publicly-available data. According to the company, scientists are using NextBio to improve their ability to identify relevant prognostic and predictive molecular signatures which are significant in their research. NextBio was a receiver of the Frost Sullivan North American Life Sciences Customer Value Enhancement Award in 2008. Since the release the website has had more than 1,500,000 hits. Articles Business Wire, March 2007 Venture Beat, February 2007 Scientific Computing, May 2008 References ^ stanford.edu | 2008 Release: What is NextBio? ^ bio-itworld.com | 2007 Release: NextBio Life Science Search Engine Advances Systems Biology Approach to Research ^ businesswire.com | 2008 Release: NextBio Announces Public Launch of Its Life Science Search Engine ^ nextbio.com | 2008 Release: NextBio FAQ ^ nextbio.com | 2008 Release: NextBio Testimonials ^ businesswire.com | 2008 Release: NextBio Awarded the 2008 Frost Sullivan North American Life Sciences "Customer Value Enhancement Award" for Innovative Life Science Search Engine Retrieved from " http://en.wikipedia.org/wiki/Nextbio " Categories : Internet search engines | Bioinformatics | Online databases | Companies established in 2004 | Privately held companies of the United States NextBio支持 sciencedirect全文检索平台 实例 通过与 NextBio 合作，ScienceDirect的内容更为丰富， NextBio 采用语义检索功能，使得生命科学、医学和化学领域的研究者可以在进行检索的同时深入了解相关科研信息、数据和知识。 Lancet 柳叶刀杂志 Volume 361, Issue 9366, 19 April 2003, Pages 1319-1325 ISSN: 01406736 CODEN: LANCA DOI: 10.1016/S0140-6736(03)13077-2 PubMed ID: 12711465 Document Type: Article Source Type: Journal View references(15) View at publisher | 论文题目 Coronavirus as a possible cause of severe acute respiratory syndrome Peiris, J.S.M. a g , Lai, S.T. b , Poon, L.L.M. a , Guan, Y. a , Yam, L.Y.C. d , Lim, W. c , Nicholls, J. a , Yee, W.K.S. e , Yan, W.W. b , Cheung, M.T. d , Cheng, V.C.C. a , Chan, K.H. a , Tsang, D.N.C. f , Yung, R.W.H. d , Ng, T.K. b , Yuen, K.Y. a a Dept. of Microbiology and Pathology, Queen Mary Hospital, University of Hong Kong, Hong Kong, Hong Kong b Department of Medicine, Intensive Care and Pathology, Princess Margaret Hospital, Hong Kong, Hong Kong c Government Virus Unit, Department of Health, Hong Kong, Hong Kong d Department of Medicine and Pathology, Pamela Youde Nethersole E. Hospital, Hong Kong, Hong Kong e Department of Medicine, Kwong Wah Hospital, Hong Kong, Hong Kong f Department of Pathology, Queen Elizabeth Hospital, Hong Kong, SAR, Hong Kong g Department of Microbiology, University of Hong Kong, Queen Mary Hospital, Pokfulam Road, Hong Kong, SAR, Hong Kong Cited by since 1996 This article has been cited 1237 times in Scopus: 1996年至今本文已被引用1237次 http://www.sciencedirect.com/science?_ob=ArticleURL_udi=B6T1B-48CW83Y-7_user=9230734_coverDate=04%2F19%2F2003_rdoc=1_fmt=high_orig=gateway_origin=gateway_sort=d_docanchor=view=c_acct=C000059499_version=1_urlVersion=0_userid=9230734md5=ad65d18d9e275165e81c4cfdcf2a6b29searchtype=a Relevant terms from this article 本文相关词汇 Click for Data Correlations, Clinical Trials and more 31diseases 相关疾病 severe acute respiratory syndrome pneumonia atypical pneumonia fever lymphopenia infection coronavirus infection cough influenza disease progression leucopenia thrombocytopenia parainfluenza shortness of breath adenovirus infection adult respiratory distress syndrome anaemia anorexia avian influenza chills common cold convalescence headache inflammation laboratory infections leucocytosis liver dysfunction multiple sclerosis respiratory failure sore throat urinary-tract infection View more... View less... 10compounds 相关化合物 ribavirin alanine hydrocortisone oxygen viral particles chloride ether fluoroquinolones magnesium methylprednisolone View more... View less... 16tissues cells 相关组织和细胞 sera lung chest kidney blood respiratory-tract cytoplasm sputum body fluids brain cell cultured cell lines liver neurons platelet pneumocytes View more... View less... 19organisms 相关生物体 human coronaviridae rhesus murine hepatitis virus adenovirus bacteria bovine coronavirus mycoplasma respiratory syncytial virus rna virus torovirus viral particles virus particles b virus escherichia coli haemophilus influenzae human-metapneumoviru... klebsiella pneumoniae porcine transmissible gastroenteritis virus View more... View less... What is this? 智能信息分析和知识获取原理、方法 ScienceDirect has partnered with NextBio to connect articles with additional research content and experimental data from sources such as PubMed, GEO, and ClinicalTrials.gov. Using life science relevant ontologies, synonym recognition, gene and protein linkages, and tissue and disease nomenclature, links are made between experimental data and peer-reviewed content. This box contains the key terms extracted from this ScienceDirect article. Clicking on a term will take you to an overview page where you will see associated content and will be able to find relevant information fast, thereby accelerating your scientific discovery. 参考资料 中国近十年论文总引用次数超过四百万 http://news.sciencenet.cn/htmlnews/2011/3/245238.shtm 8. 中国（近十年论文总引用数4,227,779） 论文： CORONAVIRUS AS A POSSIBLE CAUSE OF SEVERE ACUTE RESPIRATORY SYNDROME 被引次数：1,025 领域：临床医学 发表期刊：《柳叶刀》（THE LANCET ） Enhanced PubMed Alternative The Scientist http://blog.sciencenet.cn/home.php?mod=spaceuid=280034do=blogid=403931 医学信息智能语义检索平台 www.Quertle.info http://bbs.sciencenet.cn/home.php?mod=spaceuid=280034do=blogid=409307
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