Ontology

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kalli 15:41, 23 January 2007 (MET)

Definition

General definition

from Miriam-Webster online:

Ontology

1. : a branch of metaphysics concerned with the nature and relations of being
2. : a particular theory about the nature of being or the kinds of things that have existence

In philosophy

The art of ranking things in genera and species is of no small importance and very much assists our judgment as well as our memory. You know how much it matters in botany, not to mention animals and other substances, or again moral and notional entities as some call them. Order largely depends on it, and many good authors write in such a way that their whole account could be divided and subdivided according to a procedure related to genera and species. This helps one not merely to retain things, but also to find them.

And those who have laid out all sorts of notions under certain headings or categories have done something very useful.

Gottfried Wilhelm Leibniz, New Essays on Human Understanding (taken from John F. Sowa's homepage

In AI

From the KS, AI Lab at Stanford University referring to ontology in [AI]: “An ontology is an explicit specification of some topic. For our purposes, it is a formal and declarative representation which includes the vocabulary (or names) for referring to the terms in that subject area and the logical statements that describe what the terms are, how they are related to each other, and how they can or cannot be related to each other. Ontologies therefore provide a vocabulary for representing and communicating knowledge about some topic and a set of relationships that hold among the terms in that vocabulary.”

Tom Gruber's short answer: “An ontology is an explicit specification of a conceptualization.”

Gruber's long answer:

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 berepresented 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.

Gruber (1993, p. 3)

Why create ontologies ?

The Knowledge Systems, AI Laboratory (KSL) at Stanford University defines some simple needs ontologies can serve.

• To enable a machine to use the knowledge in some application.
• To enable multiple machines to share their knowledge.
• To help yourself understand some area of knowledge better.
• To help other people understand some area of knowledge.
• To help people reach a consensus in their understanding of some area of knowledge.

Limitations

[Information architecture] is the attempt to give structure to an ontology that will best describe entities and their relations, but ontologies have inherent biases derived from their respective domains, cultures, purposes and the environment in which their entities exist. An ontology used to describe books in a library thematically will be influenced by the fact that they must exist in a physical space and can only be in one place at a time. An ontology of the same books but not leading to a physical location, for example [e-book]s available online would be different as the same book can exist under several categories.

For more on limitations of ontologies see Clay Shirky on Ontolology is overrated

Design principles

Criteria

1. Clarity: terms should be explicitly defined and objective.
2. Coherence: inferences made from the ontology should be in accordance with definitions and logically consistent.
3. Extendibility: the ontology should be useful in multiple contexts and tasks and should be able to incorporated new terms.
4. Minimal encoding bias: choices of representation should be based on the knowledge represented by the entity, not on the needs of implementation.
5. Minimal ontological commitment: the use of the vocabulary in the ontology should be consistent enough so that knowledge sharing activities can be supported across multiple contexts but not so rigid as to exclude specialized use.

Dave Shirky [1] presents a list of characteristics where an ontology can be effectively applied.

If the domain to be organized can be defined as:

• Small corpus
• Formal categories
• Stable entities
• Restricted entities
• Clear edges

AND its the following participants are included in the process

• Expert cataloguers
• Authoritative source of judgment
• Coordinated users
• Expert users

e.g.: classifying bibliographical data.

Likewise where ontological classification does not work is when the domain contains:

• A large corpus
• No formal categories
• Unstable entities
• Unrestricted entities
• No clear edges

and/or the participants are:

• Uncoordinated users
• Amateur users
• Naive catalogers
• No Authority

e.g.: the web

How to

KSL at Stanford University that offers an online ontology editor for registered and anonymous users presents some guidelines for creating ontologies.

Before you use the editor to create an ontology, you first need to design your ontology. Following are a few suggestions to help you with this process:

1. Write a few sentences describing your ontology. You should include the general subject area that you intend to cover with your ontology. You should also include any simplifying assumptions you are making. 2. Make a list of what you would like to state in your ontology. 3. Make a list of the concepts that you think should be included in your ontology. 4. Look for ontologies in the library of ontologies that may contain terms which you can use to develop your ontology.

5. Review and make modifications to your lists as needed throughout these steps.

Types of structural representations

Ontologies can be structured effectively by domain experts and expert users, but when there are many domains and many individual users ontologies by the act of grouping entities and groups contributes to signal loss: the distinction between similar labels, e.g. eating out and dining out.

Hierarchical architectures present an authoritative structuring of categories that may not correspond to users' categorization of the same information.

Social bookmarking on the other hand allows for user cataloguing of information where over time an ontology may develop from the label (tagging) that users give to their shared bookmarks (entities).

A hypertext as a semantic network of entities

Related topics Information architecture, Artificial intelligence

Ontology editors

• [2] at Stanford KSL Network Services - online editor with a list of ontologies that can be modified, or new ones added.

Related links

• Department of Philosophy at University of New York at Buffalo - historical and contemporary uses, resources, lists of ontologies
• John F. Sowa's site on ontology and knowledge representation, including history, glossary and tutorials
• A survey of ontology editing tools at O'Reilly's xml.com

References

Online

• Shirky, C. Clay Shirky's Writings About the Internet Economics & Culture, Media & Community, [[3]]
• Stanford KSL Network Services [4]

Bibliographic

• Gruber, T.R. (1993). Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Technical Report KSL 93-04, Knowledge Systems Laboratory, Stanford University