semantic web questions we couldn't ask 10 years ago
DESCRIPTION
Talk given at the SSSW 2013 Semantic Web Summerschool. Part 1: What is "Semantic Web" (in 4 principles and 1 movie) Part 2: What question can we ask now that we couldn't ask 10 years ago Part 3: Treat Computer Science as a *science*, not just as engineering! (this part a short version of http://slidesha.re/SaUhS4 )TRANSCRIPT
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Frank van Harmelen
All the questions we couldn’t ask
10 years ago
Creative Commons License: allowed to share & remix,but must attribute & non-commercial
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The bad news: you’re going to get 3 talks
1.Where are we now?– The Semantic Web in 4 principles & a movie– Did we get anywhere?
2.Now what?– Questions we couldn’t ask 10 years ago
3.Methodological hobby horse– Science or engineering?
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Semantic Web:What is it?
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a web page in English
aboutFrank
And this page is about
LarKC
and another web page
aboutFrank
And this page is about
Stefano
This page is about the Vrije
Uniersitei
“The Semantic Web” a.k.a. “The Web of Data”
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P1. Give all things a name
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P2. Relations form a graph between things
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P3. The names are addresses on the Web
x T
[<x> IsOfType <T>]
differentowners & locations
<analgesic>
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P1+P2+P3 = Giant Global Graph
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P4. explicit & formal semantics
• assign types to things• assign types to relations• organise types in a hierarchy• impose constraints on
possible interpretations
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Examples of “semantics”
Semantics = predictable inference
Frank Lyndamarried-to
• Frank is male• married-to relates
males to females
• married-to relates 1 male to 1 female
• Lynda = Hazel
lowerbound upperbound
Hazelmarried-to
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Semantic Web:Where are we now?
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Did we get anywhere?
• Google = meaningful search• NXP = data integration• BBC = content re-use• Wallmart= SEO (RDF-a)• data.gov = data-publishing
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NXP: data integration about 26.000 products
Triple store
Triple store
Departments
Customers
Notice the 3-layer architecture
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BBC
Notice the 3-layer architecture
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Did we get anywhere?
• Google = meaningful search
• NXP = data integration
• BBC = content re-use
• BestBuy = SEO (RDF-a)
• data.gov = data-publishing
Oracle DB, IBM DB2
Reuters,New York Times, Guardian
Sears, Kmart, OverStock, Volkswagen, Renault
GoodRelations ontology,schema.org
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Size Matters: 25-45 billion facts
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The questionsthat we couldn’t ask
10 years ago
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• Heterogeneity• Self-organisation, long tails• Distribution • Provenance & trust• Dynamics• Errors & Noise• Scale
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heterogeneityis unavoidable
•Linguistic,•Structural,•Logical,•Statistical,
....
Socio-economic
first to market
market-share
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Self-organisation
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Self-organisation
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Self-organisation
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Self-organisation
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Self-organisation
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Bio-medical ontologies in Bio-portal > 5 links
Self-organisation
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knowledge followsa long-tail
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incidental or universal?
impact on mapping?
impact on reasoning?
impact on storage?
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Distribution
Caching?
Subgraphs?
Payloadpriority?
query-planning?
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Provenance
Representation?
From provenance to trust?(Re)construction?
knowledge about knowledge?
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Dynamics
Streams? Incremental reasoning?
Non-monotonicity?
versioning?
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Errors & noiseMaximally consistent subsets?
Fuzzy Semantics?
UncertaintySemantics?
RoughSemantics?
Modules?
Repair?
Argumentation?
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Maximally consistent subsets?
Modules?
Repair?
Argumentation?
Fuzzy Semantics?
UncertaintySemantics?
RoughSemantics?
Streams?
Incremental reasoning?
Non-monotonicity?
versioning?
Representation?
From provenance to trust?
(Re)construction?
knowledge about knowledge?
Caching?
Subgraphs?
Payloadpriority?
incidental or universal?
impact on mapping?
impact on reasoning?
impact on storage?
Socio-economic
first to market
market-share
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MethodologicalHobby horse
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Laws about the physical universe
Laws about the information universe ?
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knowledge followsa long-tail
Law: F = a-br
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Law: |T|<< |A|
T = terminological knowledge
A = assertional knowledge
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Dataset Closure of T
Closure of T + A
Ratio
LUBM 8sec 1h15min 562Linked Life Data 332sec 1h05min 11FactForge 89sec 2h45min 111
We don’t have any good laws on complexity
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