Ontology Quality and the Semantic Web

1. Ontology Quality and the Semantic Web Chris Welty IBM Watson Research Center

2. Outline • Welcome, opening joke • History of web and hypertext • Semantic Web overview • Ontology Engineering and Quality • Summary and Closing joke

3. History of Hypertext • 1945: Vannevar Bush’s Memex • Associative Indexing and links • 1965: Ted Nelson coins hypertext • “Nonsequential writing” • 1967: Andries van Dam’s Hypertext Editing System (sponsored by IBM). • 1985: Janet Walker’s Symbolics Document Examiner • 1987: Bill Atkinson’s Hypercard on the Mac • 1991: Tim Berners-Lee proposes HTTP, HTML, & URL • Genesis c. 1989 • 1993: Mark Andreesen releases Mosaic for Mac, Unix, Windows…

4. Hypertext Research • Dating back at least to the late 60s • Many foci • Technology (mouse, software, protocols) • User interaction • Aesthetic • Post-modern • Engineering • Largely ignored by web developers • Especially in the early days of the web (93-96)

5. Grassroots to the Web • Early web dominated by “what it looks like” in Mosaic • Focus on spreading the word, not doing it right • Many early web pages didn’t have links in text at all • “Catalog” pages with lists of links • “Text” pages with few or no links • Embedded images more interesting than links • Just do it rather than do it right • But… • When the web became serious, the research started to matter

6. Semantic Web • Defined, to date, by RDF and OWL • Genesis c. 2000 • Still in the “early days” • Faster adoption (so far) than early web • FOAF the most widely used SW Ontology Agent Document Image Person Group http://xmlns.com/foaf/0.1/ Organization

7. Ontology Research • Dating back… • Multiple foci • Technology (logics, reasoners…) • Meta-physics (what there is) • Knowledge Acquisition • NLP • Engineering • Largely ignored by SW developers • Web 2.0, groundswell • Specifically criticized by some SW pundits

8. A little semantics… • The SW catchphrase • “A little semantics goes a long way” • Sometimes strengthened • A lot of semantics is too much • 80/20 rule • Double-edged sword • FOAF doesn’t look like even 1% • The simplicity of FOAF hides any serious value proposition for SW • SW not for people, for data • Important to get it right?

9. Some evidence • Does quality matter? • Good quality ontologies cost more • Required for some applications • Improvements in quality can improve performance [Welty, et al, 2004] • 18% f-improvement in search • Cleanup cost ~1mw/3000 classes • BUT … low quality ontology still improved base

10. Dimensions of Quality • Coverage, correctness, richness, commitment [Kashyap, 2003] • Organization, modularity [Rector, 2002] • Relation to reality [Smith & Welty, 2001] • Making meaning clear [Guarino, 1998] • Meta-level consistency [Guarino & Welty, 2000] • Captures the invariant structure of the domain [Welty & Guarino, 2001]

11. Making Meaning Clear • Part-of relates parts to their wholes • E.g. part-of(engine,car) • Part-of is irreflexive • Part-of is anti-symmetric • Nothing can have only one part

12. Reduction of unintended models • Generally, involves more axioms • Typically requires negation • Disjointness • Positive axioms • Also makes meaning clear, e.g. • Clear significance for ontology alignment Mammal Chess Piece Horse Horse

13. Meta-Level Consistency with OntoClean • Identity • Unity • Rigidity • Dependence • Actuality • Permanence • Note on terminology: property is a unary relation (aka class), meta-property is a property of a class

14. Identity • The foundation of ontology, conceptual analysis, etc • The criteria under which equivalence is determined • Or under which difference is determined • Already accepted practice in RDBs, OOP • When you conceive of a class, ask “What makes each instance unique?” • Note for SW: uniqueness not assumed • Meta-property • Is there an identity criterion for this class (+I) • Not always productive to specify the precise condition • Esp. if this results in artificial attributes • -I  +I

15. Unity Criteria • An object xis a whole under w iff w is an equivalence relation that binds together all the parts of x, such that P(y,x)  (P(z,x)  w(y,z)) but not w(y,z)  x(P(y,x)  P(z,x)) • P is the part-of relation •  can be seen as a generalized indirect connection

16. Unity Meta-Properties • If all instances of a propertyare wholes under the samerelation itcarries unity (+U) • When at least one instance of a property is not a whole, or when two instances are wholes under different relations, it does not carry unity (-U) • When no instance of a property is a whole, itcarries anti-unity (~U) • -U  +U • +U  ~U

17. Rigidity • An essential property of an entity is a property that must necessarily (always) hold • A rigid property is a property that is essential to all possible instances (+R) • A non-rigid property is a property that is not rigid (-R) • An anti-rigid property is a property that is not essential to all possible instances (~R) • +R  ~R

18. Formal Rigidity • f is rigid (+R): x f(x) f(x) • e.g. Person, Apple • f is non-rigid (-R): xf(x)  ¬f(x) • e.g. Red, Male • f is anti-rigid (~R): x f(x)  ¬f(x) • e.g. Student, Agent (what about time?)

19. Rigidity Constraint +R  ~R • Why?  x P(x) Q(x) Q~R P+R O10

20. Computer Part Disk Drive Memory Which one is better? Computer Computer has-part has-part +I+R+U +I+R~U -I~R-U Disk Drive Memory -I~R-U Computer Part +I+R+U +I+R~U +I~R-U +I~R~U Disk Part Memory Part Due to: Guizzardi, et al, 2004.

21. Food Food Apple Caterpillar Apple Ontology Alignment Are these the same? • Most automatic alignment tools would say yes • Let’s take a closer look

22. Ontology Alignment +I~U+D~R +I+U-D+R • Different meta-properties for Food • Different intended meaning • Should not be aligned • Meta-level analysis helps make meaning more clear Food Food Apple Caterpillar Apple

23. A formal ontology of properties Category +R Non-sortal -I Attribution -R-D Role ~R+D Formal Role Property Material role Anti-rigid ~R Phased sortal -D +L Non-rigid -R Sortal+I Mixin -D Rigid +R Type +O Quasi-type -O

24. The Backbone Taxonomy Assumption: no entity without identity Quine, 1969 • Since identity is supplied by types, every entity must instantiate a type • The taxonomy of types spans the whole domain • Together with categories, types form the backbone taxonomy, which represents the invariant structure of a domain (rigid properties spanning the whole domain)

25. Entity Amount of matter Location Group Agent Physical object Living being Legal agent Food Red Social entity Fruit Animal Group of people Apple Lepidopteran Vertebrate Geographical Region Country Caterpillar Butterfly Red apple Person Organization

26. Entity Amount of matter Location Group Physical object Living being Social entity Fruit Animal Group of people Apple Lepidopteran Vertebrate Geographical Region Country Person Organization

27. Upper-Level Backbone • The upper level backbone accounts for 5% of an ontology and spans the domain • In empirical work, this is the most important layer [Fan et al, 2003] • Some value in providing upper level ontologies to establish the basic distinctions

28. Backbone of quality • Conjecture: the primary purpose of an ontology is to specify the backbone taxonomy, which is the invariant structure of the domain • Bad ontologies: • “folksonomies”, • Subject hierarchies • Thesauri

29. Summary • Good ontologies should: • Clarify meaning • Add constraints to eliminate unintended models • Have clear identity criteria • Have consistent meta-level properties • Specify the invariant structure of a domain

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