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

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Presentation on theme: "Ontology Quality and the Semantic Web Chris Welty IBM Watson Research Center."— Presentation transcript:

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 Bushs Memex –Associative Indexing and links 1965: Ted Nelson coins hypertext –Nonsequential writing 1967: Andries van Dams Hypertext Editing System (sponsored by IBM). 1985: Janet Walkers Symbolics Document Examiner 1987: Bill Atkinsons Hypercard on the Mac 1991: Tim Berners-Lee proposes HTTP, HTML, & URL –Genesis c : 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 didnt 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 Still in the early days –Faster adoption (so far) than early web –FOAF the most widely used SW Ontology Agent Person Organization Group DocumentImage

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 doesnt 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 Horse Chess Piece 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 x is a whole under iff is an equivalence relation that binds together all the parts of x, such that P(y,x) (P(z,x) y,z)) but not 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 property are wholes under the same relation it carries 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, it carries 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 is rigid (+R): x (x) (x) –e.g. Person, Apple is non-rigid (-R): x (x) ¬ (x) –e.g. Red, Male is anti-rigid (~R): x (x) ¬ (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 Which one is better? Computer has-part Memory Disk Drive Computer Part Memory Part Disk Part Computer Part Disk DriveMemory Computer has-part Due to: Guizzardi, et al, I~R-U +I+R+U+I+R~U -I~R-U +I+R+U +I~R~U+I~R-U +I+R~U

21 Ontology Alignment Most automatic alignment tools would say yes Lets take a closer look Food Apple Food Apple Caterpillar Are these the same?

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

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

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

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

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

30 Use OntoClean for all your ontology cleaning needs!

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