Semantic Web questions we couldn't ask 10 years ago
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Transcript of Semantic Web questions we couldn't ask 10 years ago
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
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?
Semantic Web:What is it?
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”
http://www.youtube.com/watch?v=tBSdYi4EY3s
P1. Give all things a name
P2. Relations form a graph between things
P3. The names are addresses on the Web
x T
[<x> IsOfType <T>]
differentowners & locations
<analgesic>
P1+P2+P3 = Giant Global Graph
P4. explicit & formal semantics
• assign types to things• assign types to relations• organise types in a hierarchy• impose constraints on
possible interpretations
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
Semantic Web:Where are we now?
Did we get anywhere?
• Google = meaningful search• NXP = data integration• BBC = content re-use• Wallmart= SEO (RDF-a)• data.gov = data-publishing
NXP: data integration about 26.000 products
Triple store
Triple store
Departments
Customers
Notice the 3-layer architecture
BBC
Notice the 3-layer architecture
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
Size Matters: 25-45 billion facts
The questionsthat we couldn’t ask
10 years ago
• Heterogeneity• Self-organisation, long tails• Distribution • Provenance & trust• Dynamics• Errors & Noise• Scale
heterogeneityis unavoidable
•Linguistic,•Structural,•Logical,•Statistical,
....
Socio-economic
first to market
market-share
Self-organisation
Self-organisation
Self-organisation
Self-organisation
Self-organisation
Bio-medical ontologies in Bio-portal > 5 links
Self-organisation
knowledge followsa long-tail
incidental or universal?
impact on mapping?
impact on reasoning?
impact on storage?
Distribution
Caching?
Subgraphs?
Payloadpriority?
query-planning?
Provenance
Representation?
From provenance to trust?(Re)construction?
knowledge about knowledge?
Dynamics
Streams? Incremental reasoning?
Non-monotonicity?
versioning?
Errors & noiseMaximally consistent subsets?
Fuzzy Semantics?
UncertaintySemantics?
RoughSemantics?
Modules?
Repair?
Argumentation?
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
MethodologicalHobby horse
Laws about the physical universe
Laws about the information universe ?
knowledge followsa long-tail
Law: F = a-br
Law: |T|<< |A|
T = terminological knowledge
A = assertional knowledge
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