From Taxonomies to Ontologies
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A presentation for the Earley & Associates monthly call in November 2009. Ralph Hodgson of Top Quadrant also presented that day.
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From Taxonomies to Ontologies
- 1. From Taxonomies to Ontologies Christine Connors Among other things: librarian, information scientist, semantic web advocate and Founder, TriviumRLG LLC November 4, 2009 Developing Ontologies, Part Of The Earley & Associates Call Series
- 2. The Continuum We are building more complex and powerful data architectures; all types are available for use on the semantic web
- 3. Ontology Thesaurus Taxonomy Power Synonym Ring List Folksonomy Complexity The Continuum We are building more complex and powerful data architectures; all types are available for use on the semantic web
- 4. The Continuum Thesaurus Ambiguity Control Folksonomy Synonym Ring Synonym Control Hierarchical Relationships Personalized Labels Synonym Associative Relationships Control Scope Note (Equivalency) (BT, NT, RT, USE, SeeAlso) Less Complexity More Taxonomy Ontology List Ambiguity Control Ambiguity Control Ambiguity Synonym Control Synonym Control Control Hierarchical Relationships Hierarchical Relationships (BT, NT) Associative Relationships Classes Properties Localization Annotation Reasoning “NOT” Inspired by NISO Z39.19-2005
- 5. Terminology ✤ Ontology ~ Given a knowledge domain and scope, the encoding of its concepts, their properties, and the relationships among them. ✤ Serialization ~ How the ontology is encoded for machine use and transmission. Use what works for your project: RDF/XML, JSON, N-Triples, whatever! ✤ Triple ~ The basic building block of an ontology; Subject-Predicate-Object. ✤ Graph ~ A visualization of the linked triples. ✤ URI ~ Uniform Resource Indicator, a web-based identifier more generic than the URL. ✤ Namespace ~ A collection of URIs from an authoritative source that share a common identifier. ✤ Qname ~ A shortcut; an abbreviation of the shared namespace identifier, followed by a colon and a concept name. e.g. dc:creator represents the “creator” element in the Dublin Core schema. “dc” is defined in the ontology as “http://purl.org/dc/terms/”
- 6. Capabilities ✤ Properties ✤ Transitive ✤ Symmetrical ✤ Functional ✤ Inverse Functional ✤ Inferencing
- 7. NT England Britain BT NT NT BT BT Wales Great Britain NT NT BT Scotland BT United NT Northern Kingdom BT Ireland
- 8. NT England Britain BT God and my right NT NT BT BT Wales motto Great Britain NT NT BT Scotland BT flag United NT Northern God Save the Queen anthem Kingdom BT Ireland official English language capital currency legislature London pound sterling Parliament
- 9. Transitivity ✤ In a simple hierarchical system (e.g. taxonomy) you have Broader Than/Narrower Than ✤ United Kingdom ✤ Great Britain ✤ Scotland ✤ In an ontology, we can define a Transitive Property (e.g. owl:TransitiveProperty) to cause: ✤ Scotland is a subclass of Great Britain ✤ Great Britain is a subclass of United Kingdom ✤ Therefore, Scotland is a subclass of United Kingdom
- 10. Symmetry ✤ Sometimes we want to explicitly state that a relationship is bi- directional. ✤ e.g. “spouse” or “sibling” Jack Jill spouse ✤ See Also and Use/Used For conventions are not as complete or as efficient as a SymmetricProperty.
- 11. Functional and Inverse Functional Properties ✤ It can be useful to indicate if a concept can have only ONE value for a specific attribute. ✤ e.g. a ‘person’ can be EITHER ‘male’ or ‘female’ and not both ✤ It can also be useful to indicate that a value can only be applied to ONE concept. ✤ e.g. a ‘unique employee id’ can only be assigned to ONE ‘staff member’
- 12. Inferencing ✤ It is not necessary in a well-modeled ontology to explicitly encode every possible triple, many can be inferred. ✤ s: father p: gender o: male ✤ s: father p: typeOf o: parentalRole ✤ s: John p: parentalRole o: father ✤ Therefore ✤ s: John p: gender o: male
- 13. Things to Remember ✤ Governance ~ even more important due to ontologies being more complex ✤ BUT you also have better tools to test: SPARQL, inferencing engines & reasoners ✤ Open-world vs. closed-world assumption ✤ Close it if you must! ✤ Curate the content, not the container ✤ This is more than a descriptive, bibliographic form; you can model the knowledge, not just the pointers to it
- 14. There is no “right way.” There are best practices. Image by playful.geometer
- 15. Developing an Ontology Wednesday November 4th, 1:00 PM ET Taxonomy Community of Practice Call Series, presented by Earley & Associates http://www.earley.com Thank you CJMConnors@triviumrlg.com Nick: CJMConnors at Twitter, Slideshare, LinkedIn, Identi.ca et al TriviumRLG.com
Hinweis der Redaktion
- Rather than define these here, I’m going to show you some examples. These are some examples you are likely to encounter early on - but are not ALL of the available tools. The most important thing to remember is to take baby-steps. Don’t try to read all of the standards and expect to know how to use them right away! You’ll likely drive yourself mad - it’s a lot to learn, and some things are very different from database and other programming methodologies. Learn each of these things as you encounter a use case for them! And get a good book or two.
- This is still the tip of the iceberg!
- Why would you want to do this? So that Scotland can inherit properties of its super-classes.
- If ‘Jack’ “spouse” ‘Jill’ then ‘Jill’ “spouse” ‘Jack’
- You may wonder about the problem of syllogisms, but that is why careful modeling and testing is needed.
- Most of what you already know about defining schema and building taxonomies applies to ontology creation as well: know your use case, define your requirements, understand your knowledge domain and the scope of detail you want. Look for existing ontologies to use or buy. Put small pieces together to form your overall model. Make use of subject matter experts, data modeling experts, and keep your core team small.