"Ontological analysis clarifies the structure of knowledge. Given a domain, its ontology forms the heart of any system of knowledge representation for that domain. Without ontologies, or the conceptualizations that underlie knowledge, there cannot be a vocabulary for representing knowledge....Second, ontologies enable knowledge sharing."

- from What Are Ontologies, and Why Do We Need Them? B. Chandrasekaran, Jorn R. Josephson, V. and Richard Benjamins

Imagine that you have a beaker capable of holding 10cc of a liquid and you pour 5cc of water into it. Some people would say that the beaker is half-full ... others would say that it is half-empty ... and some would say that it is filled to one-half of its capacity or that it merely contains 5cc of water.

The point of this exercise is to demonstrate that we can portray the same item in various ways, with each representing a different perspective or mindset. Thus, when selections are made from the set of possible portraits and are then gathered together in the conceptual framework known as an ontology, the result is but one of several possible vistas.

This concept was eloquently explained in What Is a Knowledge Representation, where under the caption of Role 2: A Knowledge Representation Is a Set of Ontological Commitments, the authors state:

If, as we argue, all representations are imperfect approximations to reality, each approximation attending to some things and ignoring others, then in selecting any representation, we are in the very same act unavoidably making a set of decisions about how and what to see in the world. That is, selecting a representation means making a set of ontological commitments. The commitments are, in effect, a strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus at the expense of blurring other parts.

These commitments and their focusing-blurring effect are not an incidental side effect of a representation choice; they are the essence. [p. 19]

Introductory Readings

What is an Ontology? A concise explanation from Tom Gruber and the Knowledge Systems Laboratory at Stanford University. "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. ... 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. ... 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."

What is an ontology? From OWL Web Ontology Language Use Cases and Requirements. W3C Recommendation (10 February 2004). Jeff Heflin, editor. "An ontology defines the terms used to describe and represent an area of knowledge. Ontologies are used by people, databases, and applications that need to share domain information (a domain is just a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc.). Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them (note that here and throughout this document, definition is not used in the technical sense understood by logicians). They encode knowledge in a domain and also knowledge that spans domains. In this way, they make that knowledge reusable.... Ontologies are usually expressed in a logic-based language, so that detailed, accurate, consistent, sound, and meaningful distinctions can be made among the classes, properties, and relations."

• For examples of actual ontologies, see Section 2: Use cases - "Ontologies can be used to improve existing Web-based applications and may enable new uses of the Web. In this section we describe six representative use cases of web ontologies. Note that this is not an exhaustive list, but instead a cross-section of interesting use cases."
• Also see: OWL Web Ontology Language Overview. W3C Recommendation (10 February 2004). Deborah L. McGuinness and Frank van Harmelen, editors."This document describes the OWL Web Ontology Language. OWL is intended to be used when the information contained in documents needs to be processed by applications, as opposed to situations where the content only needs to be presented to humans. OWL can be used to explicitly represent the meaning of terms in vocabularies and the relationships between those terms. This representation of terms and their interrelationships is called an ontology."
• And if you need help with the terminology, check out their OWL Glossary.

Ontological Development. Section 3.7 in The Role of Intelligent Systems in the National Information Infrastructure. The American Association for Artificial Intelligence. Daniel S. Weld, editor. (1995). "The goal of research in ontologies is to create explicit, formal catalogs of knowledge that can be used by intelligent systems. An ontology is a theory of a particular domain or sphere of knowledge, describing the kinds of entity involved in it and the relationships that can hold among different entities. An ontology for finance, for example, would provide working definitions of concepts like money, banks, and stocks. This knowledge is expressed in computer-usable formalisms; for example, an agent for personal finances would draw on its finance ontology, as well as knowledge of your particular circumstances, to look for appropriate investments. Ontologies are broad , in that they cover a wide range of phenomena and situations. They are multi-purpose in that the same ontology can be used in different programs to accomplish a variety of tasks. Building ontologies is difficult for three reasons. First, articulating knowledge in sufficient detail that it can be expressed in computationally effective formalisms is hard. Second, the scope of shared background knowledge underlying interactions of two agents can be enormous. For example, two doctors collaborating to reach a diagnosis might combine commonsense conclusions based on a patient’s lifestyle with their specialized knowledge. Third, there are unsolved problems in using large bodies of knowledge effectively, including selecting relevant subsets of knowledge, handling incomplete information, and resolving inconsistencies."

Translator lets computers "understand" experiments. By Tom Simonite. New Scientist Tech News (June 7, 2006). "A framework for translating the write-ups of experiments into a format that can be processed by computers has been developed by academics. The new tool could revolutionise the way scientific papers are written and help scientists make creative leaps, researchers say. ... 'Computers are not very good with natural language, they need to have things as formalised as possible,' says Ross King, a researcher at Aberystwyth University in Wales, who developed the framework with colleague Larisa Soldatova. Called EXPO, it can be used to translate scientific experiments into a format that can be interpreted by a computer. The researchers have published the software code online so that anyone can use and modify it. ... EXPO provides a descriptive framework, or ontology, to represent different stages of an experiment and the relationships between these stages. It also includes ways to define the hypothesis tested, the way results are analysed, and the conclusion drawn."

Multi-agent technology: removing the 'artificial' from AI. By Fran Howarth. IT-Director.com (March 18, 2004). "Based on principles first described by Aristotle, ontology is that part of metaphysics that deals with the nature and essence of being or existence. In the context of multi-agent systems, ontology is a computer-readable description of knowledge about the resources in an enterprise's network. ... The software agents become intelligent because they can make use of the knowledge contained in ontology to use in the process of negotiation and decision-making."

Is There an Intelligent Agent in Your Future? By James A. Hendler. (This wonderful paper received the AAAI-2000 Effective Expository Writing Award.) "While [ontology] is a part of the technical jargon of artificial intelligence researchers, the basic concept is simple - an ontology is a formal definition of a body of knowledge. The most typical type of ontology used in building agents involves a structural component. Essentially a taxonomy of class and subclass relations coupled with definitions of the relationships between these things."

AI Knows It’s Out There. Red Herring (August 22, 2005 print issue). "The idea behind the semantic web is to catalog information in web documents according to the higher meaning of the words -- their ontology -- rather than the mere presence of text. 'I believe the semantic web is the Next Big Thing,' says Jim Hendler, a computer science professor at the University of Maryland. To explain the concept of the semantic web, he cites the example of a Google search for the median age at which people start smoking in Baltimore. 'I'm interested in the concept of age, not specific digits,' he says."

Ontology Research. Guest Editorial by Christopher Welty. AI Magazine 24(3): Fall 2003, 11-12. "Ontology is a discipline of philosophy whose name dates back to 1613 and whose practice dates back to Aristotle. It is the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality. ... [W]hat the field of ontology research attempts to capture is a notion that is common to a number of disciplines: software engineering, databases, and AI to name but a few. In each of these areas, developers are faced with the problem of building an artifact that represents some portion of the world in a fashion that can be processed by a machine. ... To represent our field, I present six articles that cover several of the major thrusts of ontology research from the past decade."

Ontology. By John F. Sowa. "The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D."

• Glossary. "This glossary summarizes the terminology of methods and techniques for defining, sharing, and merging ontologies.

The Semantic Web. By Tim Berners-Less, James Hendler, and Ora Lassila. Scientific American (May 2001). "In philosophy, an ontology is a theory about the nature of existence, of what types of things exist; ontology as a discipline studies such theories. Artificial-intelligence and Web researchers have co-opted the term for their own jargon, and for them an ontology is a document or file that formally defines the relations among terms. The most typical kind of ontology for the Web has a taxonomy and a set of inference rules." Be sure to read the entire article for a clear explanation of these (and many other) concepts and terms.

• Also see:
  • The Semantic Web In Action - Corporate applications are well under way, and consumer uses are emerging. By Lee Feigenbaum, Ivan Herman, Tonya Hongsermeier, Eric Neumann and Susie Stephens. Scientific American (December 2007; subscription req'd). "Six years ago in this magazine, Tim Berners-Lee, James Hendler and Ora Lassila unveiled a nascent vision of the Semantic Web: a highly interconnected network of data that could be easily accessed and understood by any desktop or handheld machine. ... The enabling technologies have come of age. A vibrant community of early adopters has agreed on standards that have steadily made the Semantic Web practical to use. Large companies have major projects under way that will greatly improve the efficiencies of in-house operations and of scientific research. Other firms are using the Semantic Web to enhance business-to-business interactions and to build the hidden data-processing structures, or back ends, behind new consumer services. And like an iceberg, the tip of this large body of work is emerging in direct consumer applications, too."
  • A Smarter Web - New technologies will make online search more intelligent--and may even lead to a "Web 3.0." By John Borland. Technology Review (March / April 2007 issue). "The Semantic Web community's grandest visions, of data-surfing computer servants that automatically reason their way through problems, have yet to be fulfilled. But the basic technologies that [Eric] Miller shepherded through research labs and standards committees are joining the everyday Web. They can be found everywhere--on entertainment and travel sites, in business and scientific databases--and are forming the core of what some promoters call a nascent 'Web 3.0.' ... Since 1998, researchers at W3C, led by [Tim] Berners-Lee, had been discussing the idea of a 'semantic' Web, which not only would provide a way to classify individual bits of online data such as pictures, text, or database entries but would define relationships between classification categories as well. Dictionaries and thesauruses called 'ontologies' would translate between different ways of describing the same types of data, such as 'post code' and 'zip code.' All this would help computers start to interpret Web content more efficiently. In this vision, the Web would take on aspects of a database, or a web of databases. ... In articles and talks, Berners-Lee and others began describing a future in which software agents would similarly skip across this 'web of data,' understand Web pages' metadata content, and complete tasks that take humans hours today. ... At the beginning of 2001, the effort to realize this vision became official. The W3C tapped Miller to head up a new Semantic Web initiative, unveiled at a conference early that year in Hong Kong."
  • Tim Berners-Lee on the Semantic Web (video: 8min 24sec). Technology Review Videos (March 2007). "The inventor of the World Wide Web explains how the Semantic Web works and how it will transform how we use and understand data."
  • Tiny Circuits: Tim Berners-Lee discusses the future of the Web. NPR Talk of the Nation: Science Friday With Ira Flatow. [Radio Interview; November 1, 2002]
  • W3C Semantic Web resources

The Semantic Web. From Semaview Inc. "Designed as a one minute overview of the Semantic Web, this illustration discusses a half dozen key points in language that can be understood by managers and techies alike."

General Readings

What is A Knowledge Representation? Randall Davis, Howard Shrobe, and Peter Szolovits. AI Magazine 14(1): Spring 1993, 17-33. "What is a knowledge representation? We argue that the notion can best be understood in terms of five distinct roles it plays, each crucial to the task at hand: * A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. * It is a set of ontological commitments, i.e., an answer to the question: In what terms should I think about the world? * It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends. * It is a medium for pragmatically efficient computation, i.e., the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences. * It is a medium of human expression, i.e., a language in which we say things about the world."

Ontology Building: A Survey of Editing Tools. By Michael Denny. XML.com (November 6, 2002). "As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. ... These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information. ... The semantic structuring achieved by ontologies differs from the superficial composition and formatting of information (as data) afforded by relational and XML databases. With databases virtually all of the semantic content has to be captured in the application logic. Ontologies, however, are often able to provide an objective specification of domain information by representing a consensual agreement on the concepts and relations characterizing the way knowledge in that domain is expressed."

Building The Web of Tomorrow - Creators Say 'Semantic' Web Will Be Smarter. By Sophia Kingman. ABCNEWS.com (December 21, 2001). "Semantic means 'of or relating to meaning,' and this new Web will have content better identified so that, for example, future search engines will be able to understand context and discard the irrelevant. ... 'Ontologies are nothing but names with standard meanings. And in a world of data exchange names are incredibly important, because you and I cannot exchange information about a thing unless we agree on the name for the thing,' [R.V.] Guha said."

Computational Intelligence - A Logical Approach. By David Poole, Alan Mackworth and Randy Goebel. 1998. Oxford University Press, New York. "In order to use knowledge and reason with it, you need what we call a representation and reasoning system (RRS). ... An important and fundamental prerequisite to using an RRS is to decide how a task domain is to be described. This requires us to decide what kinds of things the domain consists of, and how they are to be related in order to express task domain problems. ... An ontology is a commitment to what exists in any particular task domain." - excerpt from Chapter 1 (pages 9 - 11).

Sharing a joke could help man and robot interact. By Michael Reilly. New Scientist (August 4, 2007; Issue 2615: page 26). "A man walks into a bar: 'Ouch!' You might not find it funny, but at least you got the joke. That's more than can be said for computers, which, despite radical advances in artificial intelligence, remain notably devoid of a funny bone. Previously AI researchers have tended not to try mimicking humour, largely because the human sense of humour is so subjective and complex, making it difficult to program. Now Julia Taylor and Lawrence Mazlack of the University of Cincinnati in Ohio have built a computer program or 'bot' that is able to get a specific type of joke - one whose crux is a simple pun. ... Taylor presented the bot at the American Association for Artificial Intelligence conference in Vancouver, Canada, last week but stresses that it does still miss some puns. ... Meanwhile Rada Mihalcea and colleagues at the University of North Texas in Denton have built a different kind of humour-spotting bot."

• See: An Investigation into Computational Recognition of Children’s Jokes. Julia M. Taylor, Lawrence J. Mazlack. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 1904-1905. Menlo Park, Calif.: AAAI Press.
  • Abstract: "This paper presents an overview of a model for computational recognition of two-sentence-long jokes that are based on phonological similarity of words. The model takes into account orthographic, phonological and semantic representation of words. The joke recognition is based on knowledge that is provided by an ontology. The ontology is created by using entries from a children’s dictionary. Each noun in the dictionary is an instance of a concept in a concept hierarchy. An instance belongs to each concept to a certain degree. The concept hierarchy is modeled from WordNet. Semantic relationships between concepts are added from a collection of children’s texts and definitions in a children’s dictionary. Any two-sentence-long text is considered a joke if it contains two scripts that both overlap and oppose; and if there is a pair of similar sounding words (w1, w2), in which w1 is an instance of a concept of one script, and w2 is an instance of a concept of another."

Knowledge Portals: Ontologies at Work. By Steffen Staab and Alexander Maedche. AI Magazine 22(2): 63-75 (Summer 2001). "Knowledge portals provide views onto domain-specific information on the World Wide Web, thus helping their users find relevant, domain-specific information. The construction of intelligent access and the contribution of information to knowledge portals, however, remained an ad hoc task, requiring extensive manual editing and maintenance by the knowledge portal providers. To diminish these efforts, we use ontologies as a conceptual backbone for providing, accessing, and structuring information in a comprehensive approach for building and maintaining knowledge portals. We present one research study and one commercial case study that show how our approach, called seal (semantic portal), is used in practice."

Knowledge Representation. A sub-chapter from Chris Welty's Dissertation, 1996. "Ontological Analysis: The word ontology means 'the study of the state of being.' An ontology describes the states of being of a particular set of things. This description is usually made up of axioms that define each thing. In knowledge representation, an ontology has become the defining term for the part of a domain model that excludes the instances, yet describes what they can be. Ontological analysis is the process of defining this part of the model." (from Section 3)

Semantic Network, an illustrated definition from Bill Wilson's AI Dictionary.

Related Resources

Cycorp, Inc. Here's just a sample of what you'll find there:

• The Semantic Web. "The success of the Semantic Web hinges on solving two key problems: (1) enabling novice users to create semantic markup easily, and (2) developing tools that can harvest the semantically rich but ontologically inconsistent web that will result. ... Once end-users are empowered by the Semantic Web to create their own ontologies, there will be an urgent need to interrelate those ontologies in a useful way. The key to harvesting this new semantic information will be the creation of the Semantic Web-aware agents that can cope with a diversity of meanings and inconsistencies across local ontologies. These agents will need the capability to interpret, understand, elaborate, and translate among the many heterogeneous local ontologies that will populate the the Semantic Web. ... Cycorp's effort is targeted at the situation where the ontologies to be translated are not richly specified, where a novice has quickly created a 'light weight' ontology, just to get started."
  • "OpenCyc is the open source version of the Cyc technology, the world's largest and most complete general knowledge base and commonsense reasoning engine. Cycorp set up an independent organization, OpenCyc.org, to disseminate and administer OpenCyc.... Release 1.0 of OpenCyc will include: 6,000 concepts: an upper ontology for all of human consensus reality; 60,000 assertions about the 6,000 concepts, interrelating them, constraining them, in effect (partially) defining them...."
  • The Syntax of CycL. "CycL is a formal language whose syntax derives from first-order predicate calculus (the language of formal logic) and from Lisp. In order to express common sense knowledge, however, it goes far beyond first order logic."

A Guided Tour to Developing Ontologies Using Ontolingua. "This tour is for people who would like an introduction both to developing ontologies and to using the Ontolingua Ontology Editor provided by the Stanford KSL Network Services for creating and modifying ontologies."

KBS/Ontology Projects and Groups, and Related Material. An extensive collection from Peter Clark of Boeing Research and Technology.

Laboratory for Applied Ontology, Institute of Cognitive Sciences and Technology, Italian National Research Council, "performs basic and applied research on the ontological foundations of conceptual modeling, exploring the role of ontologies in different fields, such as: knowledge representation, knowledge engineering, database design, information retrieval, natural language processing, and the semantic web. ... On the application side, special emphasis is given to the use of ontologies for electronic commerce, medical information systems, enterprise modeling, integration of lexical resources, and information access to the Web."

• Visit their Projects page for links to projects such as DOLCE (a Descriptive Ontology for Linguistic and Cognitive Engineering) and FOS (Fishery Ontology Service).

Legal Ontologies and Artificial Intelligence Techniques Workshop (LOAIT), June 6, 2005, held in conjunction with ICAIL-05?. "In the last few years Legal Informatics (the study of methods for automating the treatment of legal information) has been significantly influenced by Artificial Intelligence (AI) approaches. For instance, Machine Learning techniques have successfully been applied to problems of legal documents classification, legal information retrieval, legal knowledge discovery and extraction. As the use of these techniques becomes more widespread it also becomes clearer how to enhance their performances. One way of doing this is to employ structured (domain) knowledge in order to reduce complexity and support correct reasoning. Legal Ontologies are playing a crucial role in providing such knowledge at various levels of specificity and formality."

• The workshop proceedings are available from the IAAIL.

National Center for Ontological Research (NCOR): " The University at Buffalo and Stanford University have established the National Center for Ontological Research (NCOR), with Buffalo and Stanford as the two principal sites, together with a number of partner institutions drawn from academia, government, and industry. NCOR has the goal of advancing ontological investigation within the United States. It will serve as a vehicle to coordinate, to enhance, to publicize, and to seek funding for ontological research activities in its two principal sites and in its partner institutions. A special focus will be on the establishment of tools and measures for quality assurance of ontologies."

Ontology.Org. "Ontology.Org is an independent industry and research forum focussed upon the application of ontologies in Internet commerce. It is central goal of Ontology.Org to use ontologies to address the problems that impact the formation and sustainability of large electronic trading groups." (Press release, 5/13/98)

"ONTOSAURUS is the name of the browser with which you can explore SENSUS. SENSUS is a 90,000-node concept thesaurus (ontology) derived from WordNet (built at Princeton University in the mid-1990's by George Miller and colleagues), and rearranged and subordinated to the Penman Upper Model at ISI by Kevin Knight. ONTOSAURUS was built at ISI by Ramesh Patil and Tom Russ." This is a great opportunity to see for yourself just what an ontology looks like.

Second International Workshop on Legal Ontologies; University of Amsterdam, Netherlands. Organised in conjunction with JURIX 2001: the 14th Annual International Conference on Legal Knowledge and Information Systems. Under the heading "Contributions" you'll find links to several papers including: J. Lehmann, Specifying Knowledge for Reasoning about Causation and Assessing Legal Responsibility; and J. Zeleznikow and A. Stranieri, An Ontology for the Construction of Legal Decision Support Systems.

Semantic Web Agents Project, Maryland Information and Network Dynamics Lab, University of Maryland, College Park. "Many of the pages contain links which will let you either let you see the Semantic Web markup directly (the machine-readable markup) or take you to pages describing how the pages are created and the tools used to power them. Please enjoy exploring this site and learning about many of the ways Semantic Web technology can be used to provide new capabilities on the Web." - excerpt from Why do we call this the first site on the Semantic Web?

The SEKT Project [Semantically-Enabled Knowledge Technologies] demos of ontology generation, metadata generation, and more!

Sites Relevant to Ontologies and Knowledge Sharing. Maintained by Adam Farquhar.

"The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. SUMO is written in the SUO-KIF language. SUMO is free and owned by the IEEE."

Standard Upper Ontology Working Group at IEEE (SUO WG). "An ontology is similar to a dictionary or glossary, but with greater detail and structure that enables computers to process its content. An ontology consists of a set of concepts, axioms, and relationships that describe a domain of interest. ... An upper ontology is limited to concepts that are meta, generic, abstract and philosophical, and therefore are general enough to address (at a high level) a broad range of domain areas."

"The World Wide Web Consortium (W3C) develops interoperable technologies (specifications, guidelines, software, and tools) to lead the Web to its full potential. W3C is a forum for information, commerce, communication, and collective understanding. On this page, you'll find W3C news, links to W3C technologies and ways to get involved."

Related AI Topics Pages

• Common Sense
• Languages & Structures
• Representation
• Web-Searching Agents

Other References Offline

Chandrasekaran, B. and Jorn R. Josephson, V. Richard Benjamins. 1999. What Are Ontologies, and Why Do We Need Them? IEEE Intelligent Systems. 14 (1): pp. 20 - 26.

AI Magazine, Volume 24, Number 3 (Fall 2003): Ontology Research (Guest Editorial) by Christopher Welty; Special Issue Articles: Sweetening WORDNET with DOLCE  by Aldo Gangemi, Nicola Guarino, Claudio Masolo, and Alessandro Oltramari; Where Are the Semantics in the Semantic Web?  by Michael Uschold; WEBODE in a Nutshell  by Julio César Arpírez, Oscar Corcho, Mariano Fernández-López, and Asunción Gómez-Pérez; Ontologies for Corporate Web Applications  by Leo Obrst, Howard Liu, and Robert Wray; The Process Specification Language (PSL) Theory and Applications  by Michael Grüninger and Christopher Menzel; The CIDOC Conceptual Reference Module: An Ontological Approach to Semantic Interoperability of Metadata  by Martin Doerr.