Ontology Development in the Sciences Some Fundamental Considerations Ontolytics LLC Topics: ...

Ontology Development in the SciencesSome Fundamental Considerations

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Possible uses of ontologies Ontologies vs. terminologies Ontologies and their languages Importance of a conceptual foundation and vision Design, scenarios, and use cases Positive steps Dangers and sources of error Suggested reading

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Keep these separated in your thinking and your representations:

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Language

Thought

Reality

Word

Concept

Object

Represent their relations explicitly, and not implicitly


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Some possible uses of ontologies or terminologies:

• Document classification• Enhanced search

o Concept-enhanced searcho Search expansion to related objects or concepts

• Text mining• Data mining and inferencing• Knowledge discovery• Interpretation or use of other ontologies/terminologies

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Ontologies vs. Terminologies:

A terminology, dictionary, lexicon, or thesaurus is a term system• Its constituents are terms• It represents relations among terms (typically such as synonymy, partial

synonymy, or meaning overlap or inclusion) and properties of terms An ontology is an object system (or a category system)

• Its constituents are objects (of one sort or another) that are not linguistic entities such as terms.

• It represents relations among objects (such as “is a part of”, “has as a function”, “is connected to”)

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An ontology may have one or more languages related to it in such a way that

• The terms (or more complex name-like expressions) in the language(s) denote the entities (things) in the ontology

• The language(s) may be used to form assertions of varying degrees of complexity about the things in the ontology.

• In this way, an ontology may be thought of as the “domain” or the “universe of discourse” of a scientific theory: the set of things, formally characterized, that the theory is about.

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The importance of a clear conceptual foundation and goal-oriented vision

• Scientific goals -- not implementation goals.• Conceptual and scientific characterization -- not technical description and

implementation-level characterization.• How to tell that you have the wrong kind of high-level characterization of

your vision and task.• Some examples:

1. Ontology Matching, by Euzenat and Shvaiko2. “Engineering a Development Platform for Ontology-Enhanced

Knowledge Applications”, G. Merrill, in A Handbook of Principles, Concepts and Applications in Information Systems”, ed. by R. Sharman, R. Kishore, and R. Ramesh, Springer, 2006.

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Design Scenarios and Use Cases (analogy of ontology design with UI design):

• A use case is the specification of a particular task or action that must be accomplished, what its starting state is, and what its ending state must be. It is therefore particularly useful to implementers in getting things right. But it is a concept borrowed from software testing.

• A design scenario (or user scenario) tells a story about the use of an application from the perspective of a single typical user. It is more broadly goal oriented than a use case, provides richer context, justification, and rationale. It will typically involve several episodes, each of which could be extracted as a use case, and it is useful in the careful characterization and design of the system. It paints a detailed picture of business or scientific goals and needs. Use cases come from scenarios.

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Some positive steps:• Provide a clearly written description of goals and criteria of adequacy

illustrated with detailed and realistic scenarios of use.• Provide a precise and implementation-independent characterization of

the basic concepts of the ontology, its intended domain, and the fundamental properties of and relations among elements of the ontology.

• State principles in a way that can be understood and objectively applied by all ontology users.

• Take account of the need to be able to modify and expand the ontology as errors and new knowledge are discovered.

• Be mindful of any need to link your ontology with other ontologies in one way or another.


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Some dangers and sources of error:

• Failure to clearly characterize purposes and goals in a way that is precise, based on realistic scenarios, and independent of implementation.

• Conflation of terminology or language with ontology.• Confusion of meaning relations among terms with empirical or nomic

relations among ontology entities or categories.• Conflation of an ontology with its implementation using a particular

representation language or a particular set of data structures (e.g., thinking that the ontology is characterized by its OWL or RDF or OBO implementation).

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Suggested reading:

There is now a vast amount of literature on scientific ontologies and their creation and maintenance. I recommend the following as fundamental sources and starting points:

• Ontology Matching, J. Euzenat and P. Shvaiko, Springer, Berlin, 2007. Highly rigorous and comprehensive in scope. In dealing with problems of matching one ontology to another it introduces all the fundamental issues involved in ontology design.

• The Laboratory of Applied Ontology. Institute of Cognitive Science and Technology, Italian National Research Council. There is a wealth of information on this site and links to more.

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http://www.loa-cnr.it/index.html

http://www.loa-cnr.it/index.html

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