SlideShare a Scribd company logo
1 of 99
Download to read offline
ELIS – Multimedia Lab
Semantics of Notation3 Logic:
A solution for implicit quantification
Dörthe Arndt, Ruben Verborgh, Jos De Roo, Hong Sun,
Erik Mannens, and Rik Van De Walle
Multimedia Lab, Ghent University - iMinds, Belgium
Agfa Healthcare - Ghent, Belgium
RuleML 2015, Berlin, August 04, 2015
1 / 28
ELIS – Multimedia Lab
Outline
Notation3 Logic
Implicit Quantification
Formal Semantics
2 / 28
ELIS – Multimedia Lab
Notation3 Logic
Notation3 Logic
What is Notation3 Logic?
Syntax
Semantics
Implicit Quantification
Formal Semantics
3 / 28
ELIS – Multimedia Lab
What is Notation3 Logic?
A rule logic for the Semantic Web
4 / 28
ELIS – Multimedia Lab
What is Notation3 Logic?
A rule logic for the Semantic Web
Invented by Tim Berners-Lee and Dan Connolly (∼2005)
4 / 28
ELIS – Multimedia Lab
What is Notation3 Logic?
A rule logic for the Semantic Web
Invented by Tim Berners-Lee and Dan Connolly (∼2005)
Superset of RDF/Turtle
4 / 28
ELIS – Multimedia Lab
Syntax
5 / 28
ELIS – Multimedia Lab
Syntax
Simple Turtle triples:
5 / 28
ELIS – Multimedia Lab
Syntax
Simple Turtle triples:
:Socrates a :Man.
5 / 28
ELIS – Multimedia Lab
Syntax
Simple Turtle triples:
:Socrates a :Man.
Rules:
5 / 28
ELIS – Multimedia Lab
Syntax
Simple Turtle triples:
:Socrates a :Man.
Rules:
{:Socrates a :Man.}
=> {:Socrates a :Mortal.}.
5 / 28
ELIS – Multimedia Lab
Syntax
Statements about formulas:
6 / 28
ELIS – Multimedia Lab
Syntax
Statements about formulas:
:Plato :says {:Socrates a Mortal.}.
6 / 28
ELIS – Multimedia Lab
Syntax
Statements about formulas:
:Plato :says {:Socrates a Mortal.}.
Use of (quantified) variables:
6 / 28
ELIS – Multimedia Lab
Syntax
Statements about formulas:
:Plato :says {:Socrates a Mortal.}.
Use of (quantified) variables:
:Plato :knows _:x. _:x a :Man.
6 / 28
ELIS – Multimedia Lab
Syntax
Statements about formulas:
:Plato :says {:Socrates a Mortal.}.
Use of (quantified) variables:
:Plato :knows _:x. _:x a :Man.
{?x a :Man.}=>{?x a :Mortal.}.
6 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
Documents like the W3C Team Submission describe the desired
semantics
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
Documents like the W3C Team Submission describe the desired
semantics
Implementations such as reasoners can also be helpful.
In particular:
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
Documents like the W3C Team Submission describe the desired
semantics
Implementations such as reasoners can also be helpful.
In particular:
Cwm
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
Documents like the W3C Team Submission describe the desired
semantics
Implementations such as reasoners can also be helpful.
In particular:
Cwm
EYE
7 / 28
ELIS – Multimedia Lab
What about Semantics?
The formal semantics of N3 is not defined(yet)
Some implementations even differ!
But:
Documents like the W3C Team Submission describe the desired
semantics
Implementations such as reasoners can also be helpful.
In particular:
Cwm
EYE
  We use all these as sources to define N3’s formal semantics
7 / 28
ELIS – Multimedia Lab
Implicit Quantification
Notation3 Logic
Implicit Quantification
Existentials
Universals
Formal Semantics
8 / 28
ELIS – Multimedia Lab
What is implicit quantification?
In N3 quantified variables can be expressed without explicitly stating
the quantifier
9 / 28
ELIS – Multimedia Lab
What is implicit quantification?
In N3 quantified variables can be expressed without explicitly stating
the quantifier
Blank nodes _:x are existentially quantified variables
9 / 28
ELIS – Multimedia Lab
What is implicit quantification?
In N3 quantified variables can be expressed without explicitly stating
the quantifier
Blank nodes _:x are existentially quantified variables
Variables beginning with a question mark ?x are universally
quantified
9 / 28
ELIS – Multimedia Lab
What is implicit quantification?
In N3 quantified variables can be expressed without explicitly stating
the quantifier
Blank nodes _:x are existentially quantified variables
Variables beginning with a question mark ?x are universally
quantified
But: What is the scope?
9 / 28
ELIS – Multimedia Lab
Simple Examples
_:x :knows :Socrates.
10 / 28
ELIS – Multimedia Lab
Simple Examples
_:x :knows :Socrates. → ∃x : knows(x, Socrates)
10 / 28
ELIS – Multimedia Lab
Simple Examples
_:x :knows :Socrates. → ∃x : knows(x, Socrates)
?x :knows :Socrates.
10 / 28
ELIS – Multimedia Lab
Simple Examples
_:x :knows :Socrates. → ∃x : knows(x, Socrates)
?x :knows :Socrates. → ∀x : knows(x, Socrates)
10 / 28
ELIS – Multimedia Lab
Both types of variables
?x :loves _:y.
11 / 28
ELIS – Multimedia Lab
Both types of variables
?x :loves _:y.
∀x∃y : loves(x, y)
"Everybody loves
someone."
11 / 28
ELIS – Multimedia Lab
Both types of variables
?x :loves _:y.
∀x∃y : loves(x, y)
"Everybody loves
someone."
or
∃y∀x : loves(x, y)
"There is someone who
is loved by everyone."
11 / 28
ELIS – Multimedia Lab
Both types of variables
?x :loves _:y.
∀x∃y : loves(x, y)
"Everybody loves
someone."
or
∃y∀x : loves(x, y)
"There is someone who
is loved by everyone."
 11 / 28
ELIS – Multimedia Lab
Both types of variables
“If both universal and existential quantification are specified for
the same formula, then the scope of the universal quantification
is outside the scope of the existentials”.
Source: W3C Team submission; cwm and EYE give the same result.
12 / 28
ELIS – Multimedia Lab
Existentials
_:x :says {_:x a :Mortal}.
13 / 28
ELIS – Multimedia Lab
Existentials
_:x :says {_:x a :Mortal}.
∃x : says(x, Mortal(x))
There is someone who
says about himself that he
is mortal.
13 / 28
ELIS – Multimedia Lab
Existentials
_:x :says {_:x a :Mortal}.
∃x : says(x, Mortal(x))
There is someone who
says about himself that he
is mortal.
or ∃x1 : says(x, (∃x2 : Mortal(x2)))
There is someone who
says that someone is
mortal.
13 / 28
ELIS – Multimedia Lab
Existentials
_:x :says {_:x a :Mortal}.
∃x : says(x, Mortal(x))
There is someone who
says about himself that he
is mortal.
or ∃x1 : says(x, (∃x2 : Mortal(x2)))
There is someone who
says that someone is
mortal.
 13 / 28
ELIS – Multimedia Lab
Existentials
“When formulae are nested, _: blank nodes syntax [is] used to only
identify blank node in the formula it occurs directly in. It is
an arbitrary temporary name for a symbol which is existentially
quantified within the current formula (not the whole file). They can
only be used within a single formula, and not within nested
formulae.”
Source: W3C Team submission; cwm and EYE give the same result.
14 / 28
ELIS – Multimedia Lab
Universals
{{?x :p :a.} = {?x :q :b.}.}
=
{{?x :r :c.} = {?x :s :d.}.}.
15 / 28
ELIS – Multimedia Lab
Universals
{{?x :p :a.} = {?x :q :b.}.}
=
{{?x :r :c.} = {?x :s :d.}.}.
(∀x1 : p(x1, a) → q(x1, b))
→
(∀x2 : r(x2, c) → s(x2, d))
15 / 28
ELIS – Multimedia Lab
Universals
{{?x :p :a.} = {?x :q :b.}.}
=
{{?x :r :c.} = {?x :s :d.}.}.
(∀x1 : p(x1, a) → q(x1, b))
→
(∀x2 : r(x2, c) → s(x2, d))
or
∀x : ((p(x, a) → q(x, b))
→
(r(x, c) → s(x, d)))
15 / 28
ELIS – Multimedia Lab
Universals
{{?x :p :a.} = {?x :q :b.}.}
=
{{?x :r :c.} = {?x :s :d.}.}.
(∀x1 : p(x1, a) → q(x1, b))
→
(∀x2 : r(x2, c) → s(x2, d))
or
∀x : ((p(x, a) → q(x, b))
→
(r(x, c) → s(x, d)))
Here the reasoning results differ!
EYECwm
15 / 28
ELIS – Multimedia Lab
Who is right?
16 / 28
ELIS – Multimedia Lab
Universals
The team submission states:
“Apart from the set of statements, a formula also has a set of URIs
of symbols which are universally quantified, and a set of URIs of
symbols which are existentially quantified. Variables are then in
general symbols which have been quantified. There is a also a
shorthand syntax ?x which is the same as :x except that it implies
that x is universally quantified not in the formula but in its
parent formula.”
17 / 28
ELIS – Multimedia Lab
Universals
Which is the parent?
18 / 28
ELIS – Multimedia Lab
Universals
Which is the parent?
:Plato :says { :Socrates a Mortal.
Formula
}.
Parent formula
18 / 28
ELIS – Multimedia Lab
Universals
Which is the parent?
:Plato :says { :Socrates a Mortal.
Formula
}.
Parent formula
 The parent formula p of a formula f is the formula containing {f }
as a component.
18 / 28
ELIS – Multimedia Lab
Universals
But: Universal quantification also counts for descendants
19 / 28
ELIS – Multimedia Lab
Universals
But: Universal quantification also counts for descendants
{?x :p :a.}={ :s :q { ?x :r :b.
Formula
}.
Parent formula
}.
Grandparent formula
19 / 28
ELIS – Multimedia Lab
Universals
But: Universal quantification also counts for descendants
{?x :p :a.}={ :s :q { ?x :r :b.
Formula
}.
Parent formula
}.
Grandparent formula
Is interpreted as:
∀x : p(x, a) → q(s, r(x, b))
19 / 28
ELIS – Multimedia Lab
Universals
But: Universal quantification also counts for descendants
{?x :p :a.}={ :s :q { ?x :r :b.
Formula
}.
Parent formula
}.
Grandparent formula
Is interpreted as:
∀x : p(x, a) → q(s, r(x, b))
And not as
∀x1 : p(x1, a) → (∀x2 : q(s, r(x2, b)))
19 / 28
ELIS – Multimedia Lab
Universals
But: Universal quantification also counts for descendants
{?x :p :a.}={ :s :q { ?x :r :b.
Formula
}.
Parent formula
}.
Grandparent formula
Is interpreted as:
∀x : p(x, a) → q(s, r(x, b))
And not as
∀x1 : p(x1, a) → (∀x2 : q(s, r(x2, b)))
((((((((((((((((((hhhhhhhhhhhhhhhhhh
19 / 28
ELIS – Multimedia Lab
Formal Semantics
Notation3 Logic
Implicit Quantification
Formal Semantics
Handling variables
N3 context
20 / 28
ELIS – Multimedia Lab
Handling variables
The scope of an existential variable is always only the
formula it occurs in, not its descendant
21 / 28
ELIS – Multimedia Lab
Handling variables
The scope of an existential variable is always only the
formula it occurs in, not its descendant
The scope of a universal variable depends on its context,
scoping is also valid on the descendants of a quantified
formula
21 / 28
ELIS – Multimedia Lab
Handling variables
We define two ways to apply a substitution σ:
22 / 28
ELIS – Multimedia Lab
Handling variables
We define two ways to apply a substitution σ:
1. Component wise application σc: replace only direct
components of a formula
22 / 28
ELIS – Multimedia Lab
Handling variables
We define two ways to apply a substitution σ:
1. Component wise application σc: replace only direct
components of a formula
2. Total application σt: replace all direct components and
nested components
22 / 28
ELIS – Multimedia Lab
Handling variables
For
f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates}
23 / 28
ELIS – Multimedia Lab
Handling variables
For
f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates}
we obtain:
f σc
= :Socrates :says {?x a :Mortal.}.
23 / 28
ELIS – Multimedia Lab
Handling variables
For
f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates}
we obtain:
f σc
= :Socrates :says {?x a :Mortal.}.
and
f σt
= :Socrates :says {:Socrates a :Mortal.}.
23 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o.
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}.
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
f = ?y :p1 {?x :p2 :o2}.
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2
f = ?y :p1 {?y :p2 {?x :p3 :o3}}.
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2
f = ?y :p1 {?y :p2 {?x :p3 :o3}}. → nf (?x) = 3
24 / 28
ELIS – Multimedia Lab
Handling variables
To cope with the behavior of universal quantification we define the
nesting level nf(?x) of a variable ?x in a formula f as the lowest
level, counted from above, where ?x can be found:
f = ?x :p :o. → nf (?x) = 1
f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1
f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2
f = ?y :p1 {?y :p2 {?x :p3 :o3}}. → nf (?x) = 3
We call a universal variable accessible in a formula f iff
0  nf(?x) ≤ 2
.
24 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3 → ?x is not accessible in f0
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3 → ?x is not accessible in f0
nf11 (?x) = 2 and nf12 (?x) = 2
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3 → ?x is not accessible in f0
nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3 → ?x is not accessible in f0
nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12
Those are exactly the formulas where the two ?x are quantified:
25 / 28
ELIS – Multimedia Lab
Handling variables
Consider
{ {?x :p :a.} = {?x :q :b.}.
f11
} = {{?x :r :c.} = {?x :s :d.}.
f12
}.
f0
nf0 (?x) = 3 → ?x is not accessible in f0
nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12
Those are exactly the formulas where the two ?x are quantified:
{ {?x :p :a.} = {?x :q :b.}. }= { {?x :q :c.} = {?x :r :d.}. }
(∀x1 : (p(x1, a) → q(x1, b))) → (∀x2 : (r(x2, c) → s(x2, d)))
25 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
26 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
Ground implications have the usual first order meaning
26 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
Ground implications have the usual first order meaning
Except if they occur in implications, graphs are handled as
simple URIs
26 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
Ground implications have the usual first order meaning
Except if they occur in implications, graphs are handled as
simple URIs
We add grounding steps for any formula f in the following
order:
26 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
Ground implications have the usual first order meaning
Except if they occur in implications, graphs are handled as
simple URIs
We add grounding steps for any formula f in the following
order:
1. If f contains accessible universal variables, it is valid iff f σt
is
valid for every ground substitution σ for those variables.
26 / 28
ELIS – Multimedia Lab
N3 context
Our model definition is oriented on the RDF semantics with the
following additions:
Ground implications have the usual first order meaning
Except if they occur in implications, graphs are handled as
simple URIs
We add grounding steps for any formula f in the following
order:
1. If f contains accessible universal variables, it is valid iff f σt
is
valid for every ground substitution σ for those variables.
2. If f contains existentials on top level it is valid iff there exists a
ground substitution σ for those variables such that f σc
is valid.
26 / 28
ELIS – Multimedia Lab
Notation3 Logic
Implicit Quantification
Formal Semantics
27 / 28
ELIS – Multimedia Lab
Conclusion
It is possible to define the semantics as in the team submission
28 / 28
ELIS – Multimedia Lab
Conclusion
It is possible to define the semantics as in the team submission
It is difficult and rather unusual to define the scope of
universals as it is proposed in the team submission
28 / 28
ELIS – Multimedia Lab
Conclusion
It is possible to define the semantics as in the team submission
It is difficult and rather unusual to define the scope of
universals as it is proposed in the team submission
Implicit universal quantification could be handled easier, for
example on formula level
28 / 28
ELIS – Multimedia Lab
Conclusion
It is possible to define the semantics as in the team submission
It is difficult and rather unusual to define the scope of
universals as it is proposed in the team submission
Implicit universal quantification could be handled easier, for
example on formula level
Let’s simplify this!
28 / 28
ELIS – Multimedia Lab
Example: Explicit quantification
@forAll #x. @forSome #y. #x #loves #y .
@forSome #y. @forAll #x. #x #loves #y .
29 / 28
ELIS – Multimedia Lab
Example: Explicit quantification
@forAll #x. @forSome #y. #x #loves #y .
@forSome #y. @forAll #x. #x #loves #y .
Have the same meaning:
∀x∃y : loves(x, y).
29 / 28
ELIS – Multimedia Lab
Example: Variable of not?
@forSome :y. :x :p :y. @forAll :x. :x :q :o.
30 / 28
ELIS – Multimedia Lab
Example: Variable of not?
@forSome :y. :x :p :y. @forAll :x. :x :q :o.
Is the first :x quantified?
30 / 28

More Related Content

What's hot

A Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisA Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisJiahao Chen
 
Constructive Modal and Linear Logics
Constructive Modal and Linear LogicsConstructive Modal and Linear Logics
Constructive Modal and Linear LogicsValeria de Paiva
 
Categorical Semantics for Explicit Substitutions
Categorical Semantics for Explicit SubstitutionsCategorical Semantics for Explicit Substitutions
Categorical Semantics for Explicit SubstitutionsValeria de Paiva
 
A Distributed Architecture System for Recognizing Textual Entailment
A Distributed Architecture System for Recognizing Textual EntailmentA Distributed Architecture System for Recognizing Textual Entailment
A Distributed Architecture System for Recognizing Textual EntailmentFaculty of Computer Science
 
Dialectica Categories and Petri Nets
Dialectica Categories and Petri NetsDialectica Categories and Petri Nets
Dialectica Categories and Petri NetsValeria de Paiva
 
Relevant Dialectica Categories
Relevant Dialectica CategoriesRelevant Dialectica Categories
Relevant Dialectica CategoriesValeria de Paiva
 
Dialectica Categories for the Lambek Calculus
Dialectica Categories for the Lambek CalculusDialectica Categories for the Lambek Calculus
Dialectica Categories for the Lambek CalculusValeria de Paiva
 
Natural Language Inference for Humans
Natural Language Inference for HumansNatural Language Inference for Humans
Natural Language Inference for HumansValeria de Paiva
 
Going Without: a modality and its role
Going Without: a modality and its roleGoing Without: a modality and its role
Going Without: a modality and its roleValeria de Paiva
 

What's hot (9)

A Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisA Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysis
 
Constructive Modal and Linear Logics
Constructive Modal and Linear LogicsConstructive Modal and Linear Logics
Constructive Modal and Linear Logics
 
Categorical Semantics for Explicit Substitutions
Categorical Semantics for Explicit SubstitutionsCategorical Semantics for Explicit Substitutions
Categorical Semantics for Explicit Substitutions
 
A Distributed Architecture System for Recognizing Textual Entailment
A Distributed Architecture System for Recognizing Textual EntailmentA Distributed Architecture System for Recognizing Textual Entailment
A Distributed Architecture System for Recognizing Textual Entailment
 
Dialectica Categories and Petri Nets
Dialectica Categories and Petri NetsDialectica Categories and Petri Nets
Dialectica Categories and Petri Nets
 
Relevant Dialectica Categories
Relevant Dialectica CategoriesRelevant Dialectica Categories
Relevant Dialectica Categories
 
Dialectica Categories for the Lambek Calculus
Dialectica Categories for the Lambek CalculusDialectica Categories for the Lambek Calculus
Dialectica Categories for the Lambek Calculus
 
Natural Language Inference for Humans
Natural Language Inference for HumansNatural Language Inference for Humans
Natural Language Inference for Humans
 
Going Without: a modality and its role
Going Without: a modality and its roleGoing Without: a modality and its role
Going Without: a modality and its role
 

Viewers also liked

RuleML2015: How to combine event stream reasoning with transactions for the...
RuleML2015:   How to combine event stream reasoning with transactions for the...RuleML2015:   How to combine event stream reasoning with transactions for the...
RuleML2015: How to combine event stream reasoning with transactions for the...RuleML
 
History of Induction and Recursion B
History of Induction and Recursion B History of Induction and Recursion B
History of Induction and Recursion B Damien MacFarland
 
Induction and Decision Tree Learning (Part 1)
Induction and Decision Tree Learning (Part 1)Induction and Decision Tree Learning (Part 1)
Induction and Decision Tree Learning (Part 1)butest
 
RuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule EventsRuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule EventsRuleML
 
11X1 T14 08 mathematical induction 1 (2011)
11X1 T14 08 mathematical induction 1 (2011)11X1 T14 08 mathematical induction 1 (2011)
11X1 T14 08 mathematical induction 1 (2011)Nigel Simmons
 
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...RuleML
 
Challenge@rule ml2015 rule based recommender systems for the Web of Data
Challenge@rule ml2015 rule based recommender systems for the Web of DataChallenge@rule ml2015 rule based recommender systems for the Web of Data
Challenge@rule ml2015 rule based recommender systems for the Web of DataRuleML
 
Iteration, induction, and recursion
Iteration, induction, and recursionIteration, induction, and recursion
Iteration, induction, and recursionMohammed Hussein
 
Math induction principle (slides)
Math induction principle (slides)Math induction principle (slides)
Math induction principle (slides)IIUM
 
Mathematical induction
Mathematical inductionMathematical induction
Mathematical inductionsonia -
 
Mathematical induction
Mathematical inductionMathematical induction
Mathematical inductionSman Abbasi
 
5.4 mathematical induction
5.4 mathematical induction5.4 mathematical induction
5.4 mathematical inductionmath260
 
Principle of mathematical induction
Principle of mathematical inductionPrinciple of mathematical induction
Principle of mathematical inductionKriti Varshney
 
mathematical induction
mathematical inductionmathematical induction
mathematical inductionankush_kumar
 

Viewers also liked (14)

RuleML2015: How to combine event stream reasoning with transactions for the...
RuleML2015:   How to combine event stream reasoning with transactions for the...RuleML2015:   How to combine event stream reasoning with transactions for the...
RuleML2015: How to combine event stream reasoning with transactions for the...
 
History of Induction and Recursion B
History of Induction and Recursion B History of Induction and Recursion B
History of Induction and Recursion B
 
Induction and Decision Tree Learning (Part 1)
Induction and Decision Tree Learning (Part 1)Induction and Decision Tree Learning (Part 1)
Induction and Decision Tree Learning (Part 1)
 
RuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule EventsRuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule Events
 
11X1 T14 08 mathematical induction 1 (2011)
11X1 T14 08 mathematical induction 1 (2011)11X1 T14 08 mathematical induction 1 (2011)
11X1 T14 08 mathematical induction 1 (2011)
 
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...
 
Challenge@rule ml2015 rule based recommender systems for the Web of Data
Challenge@rule ml2015 rule based recommender systems for the Web of DataChallenge@rule ml2015 rule based recommender systems for the Web of Data
Challenge@rule ml2015 rule based recommender systems for the Web of Data
 
Iteration, induction, and recursion
Iteration, induction, and recursionIteration, induction, and recursion
Iteration, induction, and recursion
 
Math induction principle (slides)
Math induction principle (slides)Math induction principle (slides)
Math induction principle (slides)
 
Mathematical induction
Mathematical inductionMathematical induction
Mathematical induction
 
Mathematical induction
Mathematical inductionMathematical induction
Mathematical induction
 
5.4 mathematical induction
5.4 mathematical induction5.4 mathematical induction
5.4 mathematical induction
 
Principle of mathematical induction
Principle of mathematical inductionPrinciple of mathematical induction
Principle of mathematical induction
 
mathematical induction
mathematical inductionmathematical induction
mathematical induction
 

Similar to RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantification

RuleML2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML2015:  Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML2015:  Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML
 
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...Universität Leipzig
 
Lambda? You Keep Using that Letter
Lambda? You Keep Using that LetterLambda? You Keep Using that Letter
Lambda? You Keep Using that LetterKevlin Henney
 
Can Deep Learning Techniques Improve Entity Linking?
Can Deep Learning Techniques Improve Entity Linking?Can Deep Learning Techniques Improve Entity Linking?
Can Deep Learning Techniques Improve Entity Linking?Julien PLU
 
Sparklis exploration et interrogation de points d'accès sparql par interactio...
Sparklis exploration et interrogation de points d'accès sparql par interactio...Sparklis exploration et interrogation de points d'accès sparql par interactio...
Sparklis exploration et interrogation de points d'accès sparql par interactio...SemWebPro
 
Concepts in Application Context ( How we may think conceptually )
Concepts in Application Context ( How we may think conceptually )Concepts in Application Context ( How we may think conceptually )
Concepts in Application Context ( How we may think conceptually )Steffen Staab
 
Deep Dependency Graph Conversion in English
Deep Dependency Graph Conversion in EnglishDeep Dependency Graph Conversion in English
Deep Dependency Graph Conversion in EnglishJinho Choi
 
What's next in Julia
What's next in JuliaWhat's next in Julia
What's next in JuliaJiahao Chen
 
NLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology ConstraintsNLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology ConstraintsDimitris Kontokostas
 
A package system for maintaining large model distributions in vle software
A package system for maintaining large model distributions in vle softwareA package system for maintaining large model distributions in vle software
A package system for maintaining large model distributions in vle softwareDaniele Gianni
 
Simplicial closure and higher-order link prediction LA/OPT
Simplicial closure and higher-order link prediction LA/OPTSimplicial closure and higher-order link prediction LA/OPT
Simplicial closure and higher-order link prediction LA/OPTAustin Benson
 
Object? You Keep Using that Word
Object? You Keep Using that WordObject? You Keep Using that Word
Object? You Keep Using that WordKevlin Henney
 
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...Marko Rodriguez
 
np-compete: a Science Hack Day 2010 elevator pitch
np-compete: a Science Hack Day 2010 elevator pitchnp-compete: a Science Hack Day 2010 elevator pitch
np-compete: a Science Hack Day 2010 elevator pitchDan Hagon
 
Extracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis
Extracting Relevant Questions to an RDF Dataset Using Formal Concept AnalysisExtracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis
Extracting Relevant Questions to an RDF Dataset Using Formal Concept AnalysisMathieu d'Aquin
 
How the Web can change social science research (including yours)
How the Web can change social science research (including yours)How the Web can change social science research (including yours)
How the Web can change social science research (including yours)Frank van Harmelen
 

Similar to RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantification (20)

RuleML2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML2015:  Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML2015:  Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
 
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...
MEX Vocabulary - A Lightweight Interchange Format for Machine Learning Experi...
 
Recommandation sociale : filtrage collaboratif et par le contenu
Recommandation sociale : filtrage collaboratif et par le contenuRecommandation sociale : filtrage collaboratif et par le contenu
Recommandation sociale : filtrage collaboratif et par le contenu
 
Lambda? You Keep Using that Letter
Lambda? You Keep Using that LetterLambda? You Keep Using that Letter
Lambda? You Keep Using that Letter
 
Can Deep Learning Techniques Improve Entity Linking?
Can Deep Learning Techniques Improve Entity Linking?Can Deep Learning Techniques Improve Entity Linking?
Can Deep Learning Techniques Improve Entity Linking?
 
Sparklis exploration et interrogation de points d'accès sparql par interactio...
Sparklis exploration et interrogation de points d'accès sparql par interactio...Sparklis exploration et interrogation de points d'accès sparql par interactio...
Sparklis exploration et interrogation de points d'accès sparql par interactio...
 
Concepts in Application Context ( How we may think conceptually )
Concepts in Application Context ( How we may think conceptually )Concepts in Application Context ( How we may think conceptually )
Concepts in Application Context ( How we may think conceptually )
 
Deep Dependency Graph Conversion in English
Deep Dependency Graph Conversion in EnglishDeep Dependency Graph Conversion in English
Deep Dependency Graph Conversion in English
 
What's next in Julia
What's next in JuliaWhat's next in Julia
What's next in Julia
 
NLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology ConstraintsNLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology Constraints
 
Reproducible Science and Deep Software Variability
Reproducible Science and Deep Software VariabilityReproducible Science and Deep Software Variability
Reproducible Science and Deep Software Variability
 
A package system for maintaining large model distributions in vle software
A package system for maintaining large model distributions in vle softwareA package system for maintaining large model distributions in vle software
A package system for maintaining large model distributions in vle software
 
Simplicial closure and higher-order link prediction LA/OPT
Simplicial closure and higher-order link prediction LA/OPTSimplicial closure and higher-order link prediction LA/OPT
Simplicial closure and higher-order link prediction LA/OPT
 
ESWC 2014 Tutorial part 3
ESWC 2014 Tutorial part 3ESWC 2014 Tutorial part 3
ESWC 2014 Tutorial part 3
 
FinalReport
FinalReportFinalReport
FinalReport
 
Object? You Keep Using that Word
Object? You Keep Using that WordObject? You Keep Using that Word
Object? You Keep Using that Word
 
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
 
np-compete: a Science Hack Day 2010 elevator pitch
np-compete: a Science Hack Day 2010 elevator pitchnp-compete: a Science Hack Day 2010 elevator pitch
np-compete: a Science Hack Day 2010 elevator pitch
 
Extracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis
Extracting Relevant Questions to an RDF Dataset Using Formal Concept AnalysisExtracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis
Extracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis
 
How the Web can change social science research (including yours)
How the Web can change social science research (including yours)How the Web can change social science research (including yours)
How the Web can change social science research (including yours)
 

More from RuleML

Aggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and SolutionsAggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and SolutionsRuleML
 
A software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasksA software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasksRuleML
 
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...RuleML
 
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...RuleML
 
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive ConceptsRule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive ConceptsRuleML
 
RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?RuleML
 
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
 
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and RulesRuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and RulesRuleML
 
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...RuleML
 
A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...RuleML
 
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-RuleML
 
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML
 
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML
 
RuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML
 
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML
 
RuleML2015: Compact representation of conditional probability for rule-based...
RuleML2015:  Compact representation of conditional probability for rule-based...RuleML2015:  Compact representation of conditional probability for rule-based...
RuleML2015: Compact representation of conditional probability for rule-based...RuleML
 
RuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML
 
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...RuleML
 
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...RuleML
 
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...RuleML
 

More from RuleML (20)

Aggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and SolutionsAggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and Solutions
 
A software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasksA software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasks
 
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
 
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
 
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive ConceptsRule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
 
RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?
 
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
 
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and RulesRuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
 
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
 
A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...
 
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
 
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
 
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
 
RuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the Browser
 
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
 
RuleML2015: Compact representation of conditional probability for rule-based...
RuleML2015:  Compact representation of conditional probability for rule-based...RuleML2015:  Compact representation of conditional probability for rule-based...
RuleML2015: Compact representation of conditional probability for rule-based...
 
RuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information Systems
 
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...
 
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...
RuleML2015: User Extensible System to Identify Problems in OWL Ontologies and...
 
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...
RuleML2015: Representing Flexible Role-Based Access Control Policies Using Ob...
 

Recently uploaded

Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptArshadWarsi13
 
User Guide: Orion™ Weather Station (Columbia Weather Systems)
User Guide: Orion™ Weather Station (Columbia Weather Systems)User Guide: Orion™ Weather Station (Columbia Weather Systems)
User Guide: Orion™ Weather Station (Columbia Weather Systems)Columbia Weather Systems
 
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxSTOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxMurugaveni B
 
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)riyaescorts54
 
Topic 9- General Principles of International Law.pptx
Topic 9- General Principles of International Law.pptxTopic 9- General Principles of International Law.pptx
Topic 9- General Principles of International Law.pptxJorenAcuavera1
 
Speech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxSpeech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxpriyankatabhane
 
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)User Guide: Pulsar™ Weather Station (Columbia Weather Systems)
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)Columbia Weather Systems
 
Davis plaque method.pptx recombinant DNA technology
Davis plaque method.pptx recombinant DNA technologyDavis plaque method.pptx recombinant DNA technology
Davis plaque method.pptx recombinant DNA technologycaarthichand2003
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Patrick Diehl
 
Neurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trNeurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trssuser06f238
 
User Guide: Capricorn FLX™ Weather Station
User Guide: Capricorn FLX™ Weather StationUser Guide: Capricorn FLX™ Weather Station
User Guide: Capricorn FLX™ Weather StationColumbia Weather Systems
 
TOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsTOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsssuserddc89b
 
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdf
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdfPests of Blackgram, greengram, cowpea_Dr.UPR.pdf
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdfPirithiRaju
 
Environmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial BiosensorEnvironmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial Biosensorsonawaneprad
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander  in real timeGrafana in space: Monitoring Japan's SLIM moon lander  in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real timeSatoshi NAKAHIRA
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPirithiRaju
 
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuine
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 GenuineCall Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuine
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuinethapagita
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024AyushiRastogi48
 
Citronella presentation SlideShare mani upadhyay
Citronella presentation SlideShare mani upadhyayCitronella presentation SlideShare mani upadhyay
Citronella presentation SlideShare mani upadhyayupadhyaymani499
 

Recently uploaded (20)

Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.ppt
 
User Guide: Orion™ Weather Station (Columbia Weather Systems)
User Guide: Orion™ Weather Station (Columbia Weather Systems)User Guide: Orion™ Weather Station (Columbia Weather Systems)
User Guide: Orion™ Weather Station (Columbia Weather Systems)
 
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxSTOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
 
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)
(9818099198) Call Girls In Noida Sector 14 (NOIDA ESCORTS)
 
Topic 9- General Principles of International Law.pptx
Topic 9- General Principles of International Law.pptxTopic 9- General Principles of International Law.pptx
Topic 9- General Principles of International Law.pptx
 
Speech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxSpeech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptx
 
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)User Guide: Pulsar™ Weather Station (Columbia Weather Systems)
User Guide: Pulsar™ Weather Station (Columbia Weather Systems)
 
Davis plaque method.pptx recombinant DNA technology
Davis plaque method.pptx recombinant DNA technologyDavis plaque method.pptx recombinant DNA technology
Davis plaque method.pptx recombinant DNA technology
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?
 
Neurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trNeurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 tr
 
User Guide: Capricorn FLX™ Weather Station
User Guide: Capricorn FLX™ Weather StationUser Guide: Capricorn FLX™ Weather Station
User Guide: Capricorn FLX™ Weather Station
 
TOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsTOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physics
 
Volatile Oils Pharmacognosy And Phytochemistry -I
Volatile Oils Pharmacognosy And Phytochemistry -IVolatile Oils Pharmacognosy And Phytochemistry -I
Volatile Oils Pharmacognosy And Phytochemistry -I
 
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdf
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdfPests of Blackgram, greengram, cowpea_Dr.UPR.pdf
Pests of Blackgram, greengram, cowpea_Dr.UPR.pdf
 
Environmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial BiosensorEnvironmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial Biosensor
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander  in real timeGrafana in space: Monitoring Japan's SLIM moon lander  in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real time
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
 
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuine
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 GenuineCall Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuine
Call Girls in Majnu Ka Tilla Delhi 🔝9711014705🔝 Genuine
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024
 
Citronella presentation SlideShare mani upadhyay
Citronella presentation SlideShare mani upadhyayCitronella presentation SlideShare mani upadhyay
Citronella presentation SlideShare mani upadhyay
 

RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantification

  • 1. ELIS – Multimedia Lab Semantics of Notation3 Logic: A solution for implicit quantification Dörthe Arndt, Ruben Verborgh, Jos De Roo, Hong Sun, Erik Mannens, and Rik Van De Walle Multimedia Lab, Ghent University - iMinds, Belgium Agfa Healthcare - Ghent, Belgium RuleML 2015, Berlin, August 04, 2015 1 / 28
  • 2. ELIS – Multimedia Lab Outline Notation3 Logic Implicit Quantification Formal Semantics 2 / 28
  • 3. ELIS – Multimedia Lab Notation3 Logic Notation3 Logic What is Notation3 Logic? Syntax Semantics Implicit Quantification Formal Semantics 3 / 28
  • 4. ELIS – Multimedia Lab What is Notation3 Logic? A rule logic for the Semantic Web 4 / 28
  • 5. ELIS – Multimedia Lab What is Notation3 Logic? A rule logic for the Semantic Web Invented by Tim Berners-Lee and Dan Connolly (∼2005) 4 / 28
  • 6. ELIS – Multimedia Lab What is Notation3 Logic? A rule logic for the Semantic Web Invented by Tim Berners-Lee and Dan Connolly (∼2005) Superset of RDF/Turtle 4 / 28
  • 7. ELIS – Multimedia Lab Syntax 5 / 28
  • 8. ELIS – Multimedia Lab Syntax Simple Turtle triples: 5 / 28
  • 9. ELIS – Multimedia Lab Syntax Simple Turtle triples: :Socrates a :Man. 5 / 28
  • 10. ELIS – Multimedia Lab Syntax Simple Turtle triples: :Socrates a :Man. Rules: 5 / 28
  • 11. ELIS – Multimedia Lab Syntax Simple Turtle triples: :Socrates a :Man. Rules: {:Socrates a :Man.} => {:Socrates a :Mortal.}. 5 / 28
  • 12. ELIS – Multimedia Lab Syntax Statements about formulas: 6 / 28
  • 13. ELIS – Multimedia Lab Syntax Statements about formulas: :Plato :says {:Socrates a Mortal.}. 6 / 28
  • 14. ELIS – Multimedia Lab Syntax Statements about formulas: :Plato :says {:Socrates a Mortal.}. Use of (quantified) variables: 6 / 28
  • 15. ELIS – Multimedia Lab Syntax Statements about formulas: :Plato :says {:Socrates a Mortal.}. Use of (quantified) variables: :Plato :knows _:x. _:x a :Man. 6 / 28
  • 16. ELIS – Multimedia Lab Syntax Statements about formulas: :Plato :says {:Socrates a Mortal.}. Use of (quantified) variables: :Plato :knows _:x. _:x a :Man. {?x a :Man.}=>{?x a :Mortal.}. 6 / 28
  • 17. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined 7 / 28
  • 18. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) 7 / 28
  • 19. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! 7 / 28
  • 20. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: 7 / 28
  • 21. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: Documents like the W3C Team Submission describe the desired semantics 7 / 28
  • 22. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: Documents like the W3C Team Submission describe the desired semantics Implementations such as reasoners can also be helpful. In particular: 7 / 28
  • 23. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: Documents like the W3C Team Submission describe the desired semantics Implementations such as reasoners can also be helpful. In particular: Cwm 7 / 28
  • 24. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: Documents like the W3C Team Submission describe the desired semantics Implementations such as reasoners can also be helpful. In particular: Cwm EYE 7 / 28
  • 25. ELIS – Multimedia Lab What about Semantics? The formal semantics of N3 is not defined(yet) Some implementations even differ! But: Documents like the W3C Team Submission describe the desired semantics Implementations such as reasoners can also be helpful. In particular: Cwm EYE   We use all these as sources to define N3’s formal semantics 7 / 28
  • 26. ELIS – Multimedia Lab Implicit Quantification Notation3 Logic Implicit Quantification Existentials Universals Formal Semantics 8 / 28
  • 27. ELIS – Multimedia Lab What is implicit quantification? In N3 quantified variables can be expressed without explicitly stating the quantifier 9 / 28
  • 28. ELIS – Multimedia Lab What is implicit quantification? In N3 quantified variables can be expressed without explicitly stating the quantifier Blank nodes _:x are existentially quantified variables 9 / 28
  • 29. ELIS – Multimedia Lab What is implicit quantification? In N3 quantified variables can be expressed without explicitly stating the quantifier Blank nodes _:x are existentially quantified variables Variables beginning with a question mark ?x are universally quantified 9 / 28
  • 30. ELIS – Multimedia Lab What is implicit quantification? In N3 quantified variables can be expressed without explicitly stating the quantifier Blank nodes _:x are existentially quantified variables Variables beginning with a question mark ?x are universally quantified But: What is the scope? 9 / 28
  • 31. ELIS – Multimedia Lab Simple Examples _:x :knows :Socrates. 10 / 28
  • 32. ELIS – Multimedia Lab Simple Examples _:x :knows :Socrates. → ∃x : knows(x, Socrates) 10 / 28
  • 33. ELIS – Multimedia Lab Simple Examples _:x :knows :Socrates. → ∃x : knows(x, Socrates) ?x :knows :Socrates. 10 / 28
  • 34. ELIS – Multimedia Lab Simple Examples _:x :knows :Socrates. → ∃x : knows(x, Socrates) ?x :knows :Socrates. → ∀x : knows(x, Socrates) 10 / 28
  • 35. ELIS – Multimedia Lab Both types of variables ?x :loves _:y. 11 / 28
  • 36. ELIS – Multimedia Lab Both types of variables ?x :loves _:y. ∀x∃y : loves(x, y) "Everybody loves someone." 11 / 28
  • 37. ELIS – Multimedia Lab Both types of variables ?x :loves _:y. ∀x∃y : loves(x, y) "Everybody loves someone." or ∃y∀x : loves(x, y) "There is someone who is loved by everyone." 11 / 28
  • 38. ELIS – Multimedia Lab Both types of variables ?x :loves _:y. ∀x∃y : loves(x, y) "Everybody loves someone." or ∃y∀x : loves(x, y) "There is someone who is loved by everyone." 11 / 28
  • 39. ELIS – Multimedia Lab Both types of variables “If both universal and existential quantification are specified for the same formula, then the scope of the universal quantification is outside the scope of the existentials”. Source: W3C Team submission; cwm and EYE give the same result. 12 / 28
  • 40. ELIS – Multimedia Lab Existentials _:x :says {_:x a :Mortal}. 13 / 28
  • 41. ELIS – Multimedia Lab Existentials _:x :says {_:x a :Mortal}. ∃x : says(x, Mortal(x)) There is someone who says about himself that he is mortal. 13 / 28
  • 42. ELIS – Multimedia Lab Existentials _:x :says {_:x a :Mortal}. ∃x : says(x, Mortal(x)) There is someone who says about himself that he is mortal. or ∃x1 : says(x, (∃x2 : Mortal(x2))) There is someone who says that someone is mortal. 13 / 28
  • 43. ELIS – Multimedia Lab Existentials _:x :says {_:x a :Mortal}. ∃x : says(x, Mortal(x)) There is someone who says about himself that he is mortal. or ∃x1 : says(x, (∃x2 : Mortal(x2))) There is someone who says that someone is mortal. 13 / 28
  • 44. ELIS – Multimedia Lab Existentials “When formulae are nested, _: blank nodes syntax [is] used to only identify blank node in the formula it occurs directly in. It is an arbitrary temporary name for a symbol which is existentially quantified within the current formula (not the whole file). They can only be used within a single formula, and not within nested formulae.” Source: W3C Team submission; cwm and EYE give the same result. 14 / 28
  • 45. ELIS – Multimedia Lab Universals {{?x :p :a.} = {?x :q :b.}.} = {{?x :r :c.} = {?x :s :d.}.}. 15 / 28
  • 46. ELIS – Multimedia Lab Universals {{?x :p :a.} = {?x :q :b.}.} = {{?x :r :c.} = {?x :s :d.}.}. (∀x1 : p(x1, a) → q(x1, b)) → (∀x2 : r(x2, c) → s(x2, d)) 15 / 28
  • 47. ELIS – Multimedia Lab Universals {{?x :p :a.} = {?x :q :b.}.} = {{?x :r :c.} = {?x :s :d.}.}. (∀x1 : p(x1, a) → q(x1, b)) → (∀x2 : r(x2, c) → s(x2, d)) or ∀x : ((p(x, a) → q(x, b)) → (r(x, c) → s(x, d))) 15 / 28
  • 48. ELIS – Multimedia Lab Universals {{?x :p :a.} = {?x :q :b.}.} = {{?x :r :c.} = {?x :s :d.}.}. (∀x1 : p(x1, a) → q(x1, b)) → (∀x2 : r(x2, c) → s(x2, d)) or ∀x : ((p(x, a) → q(x, b)) → (r(x, c) → s(x, d))) Here the reasoning results differ! EYECwm 15 / 28
  • 49. ELIS – Multimedia Lab Who is right? 16 / 28
  • 50. ELIS – Multimedia Lab Universals The team submission states: “Apart from the set of statements, a formula also has a set of URIs of symbols which are universally quantified, and a set of URIs of symbols which are existentially quantified. Variables are then in general symbols which have been quantified. There is a also a shorthand syntax ?x which is the same as :x except that it implies that x is universally quantified not in the formula but in its parent formula.” 17 / 28
  • 51. ELIS – Multimedia Lab Universals Which is the parent? 18 / 28
  • 52. ELIS – Multimedia Lab Universals Which is the parent? :Plato :says { :Socrates a Mortal. Formula }. Parent formula 18 / 28
  • 53. ELIS – Multimedia Lab Universals Which is the parent? :Plato :says { :Socrates a Mortal. Formula }. Parent formula  The parent formula p of a formula f is the formula containing {f } as a component. 18 / 28
  • 54. ELIS – Multimedia Lab Universals But: Universal quantification also counts for descendants 19 / 28
  • 55. ELIS – Multimedia Lab Universals But: Universal quantification also counts for descendants {?x :p :a.}={ :s :q { ?x :r :b. Formula }. Parent formula }. Grandparent formula 19 / 28
  • 56. ELIS – Multimedia Lab Universals But: Universal quantification also counts for descendants {?x :p :a.}={ :s :q { ?x :r :b. Formula }. Parent formula }. Grandparent formula Is interpreted as: ∀x : p(x, a) → q(s, r(x, b)) 19 / 28
  • 57. ELIS – Multimedia Lab Universals But: Universal quantification also counts for descendants {?x :p :a.}={ :s :q { ?x :r :b. Formula }. Parent formula }. Grandparent formula Is interpreted as: ∀x : p(x, a) → q(s, r(x, b)) And not as ∀x1 : p(x1, a) → (∀x2 : q(s, r(x2, b))) 19 / 28
  • 58. ELIS – Multimedia Lab Universals But: Universal quantification also counts for descendants {?x :p :a.}={ :s :q { ?x :r :b. Formula }. Parent formula }. Grandparent formula Is interpreted as: ∀x : p(x, a) → q(s, r(x, b)) And not as ∀x1 : p(x1, a) → (∀x2 : q(s, r(x2, b))) ((((((((((((((((((hhhhhhhhhhhhhhhhhh 19 / 28
  • 59. ELIS – Multimedia Lab Formal Semantics Notation3 Logic Implicit Quantification Formal Semantics Handling variables N3 context 20 / 28
  • 60. ELIS – Multimedia Lab Handling variables The scope of an existential variable is always only the formula it occurs in, not its descendant 21 / 28
  • 61. ELIS – Multimedia Lab Handling variables The scope of an existential variable is always only the formula it occurs in, not its descendant The scope of a universal variable depends on its context, scoping is also valid on the descendants of a quantified formula 21 / 28
  • 62. ELIS – Multimedia Lab Handling variables We define two ways to apply a substitution σ: 22 / 28
  • 63. ELIS – Multimedia Lab Handling variables We define two ways to apply a substitution σ: 1. Component wise application σc: replace only direct components of a formula 22 / 28
  • 64. ELIS – Multimedia Lab Handling variables We define two ways to apply a substitution σ: 1. Component wise application σc: replace only direct components of a formula 2. Total application σt: replace all direct components and nested components 22 / 28
  • 65. ELIS – Multimedia Lab Handling variables For f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates} 23 / 28
  • 66. ELIS – Multimedia Lab Handling variables For f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates} we obtain: f σc = :Socrates :says {?x a :Mortal.}. 23 / 28
  • 67. ELIS – Multimedia Lab Handling variables For f = ?x :says {?x a :Mortal.}. and σ = {?x/:Socrates} we obtain: f σc = :Socrates :says {?x a :Mortal.}. and f σt = :Socrates :says {:Socrates a :Mortal.}. 23 / 28
  • 68. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: 24 / 28
  • 69. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. 24 / 28
  • 70. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 24 / 28
  • 71. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. 24 / 28
  • 72. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 24 / 28
  • 73. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 f = ?y :p1 {?x :p2 :o2}. 24 / 28
  • 74. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2 24 / 28
  • 75. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2 f = ?y :p1 {?y :p2 {?x :p3 :o3}}. 24 / 28
  • 76. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2 f = ?y :p1 {?y :p2 {?x :p3 :o3}}. → nf (?x) = 3 24 / 28
  • 77. ELIS – Multimedia Lab Handling variables To cope with the behavior of universal quantification we define the nesting level nf(?x) of a variable ?x in a formula f as the lowest level, counted from above, where ?x can be found: f = ?x :p :o. → nf (?x) = 1 f = ?x :p1 {?x :p2 :o2}. → nf (?x) = 1 f = ?y :p1 {?x :p2 :o2}. → nf (?x) = 2 f = ?y :p1 {?y :p2 {?x :p3 :o3}}. → nf (?x) = 3 We call a universal variable accessible in a formula f iff 0 nf(?x) ≤ 2 . 24 / 28
  • 78. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 25 / 28
  • 79. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 25 / 28
  • 80. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 → ?x is not accessible in f0 25 / 28
  • 81. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 → ?x is not accessible in f0 nf11 (?x) = 2 and nf12 (?x) = 2 25 / 28
  • 82. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 → ?x is not accessible in f0 nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12 25 / 28
  • 83. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 → ?x is not accessible in f0 nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12 Those are exactly the formulas where the two ?x are quantified: 25 / 28
  • 84. ELIS – Multimedia Lab Handling variables Consider { {?x :p :a.} = {?x :q :b.}. f11 } = {{?x :r :c.} = {?x :s :d.}. f12 }. f0 nf0 (?x) = 3 → ?x is not accessible in f0 nf11 (?x) = 2 and nf12 (?x) = 2 → ?x is accessible in f11 and f12 Those are exactly the formulas where the two ?x are quantified: { {?x :p :a.} = {?x :q :b.}. }= { {?x :q :c.} = {?x :r :d.}. } (∀x1 : (p(x1, a) → q(x1, b))) → (∀x2 : (r(x2, c) → s(x2, d))) 25 / 28
  • 85. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: 26 / 28
  • 86. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: Ground implications have the usual first order meaning 26 / 28
  • 87. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: Ground implications have the usual first order meaning Except if they occur in implications, graphs are handled as simple URIs 26 / 28
  • 88. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: Ground implications have the usual first order meaning Except if they occur in implications, graphs are handled as simple URIs We add grounding steps for any formula f in the following order: 26 / 28
  • 89. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: Ground implications have the usual first order meaning Except if they occur in implications, graphs are handled as simple URIs We add grounding steps for any formula f in the following order: 1. If f contains accessible universal variables, it is valid iff f σt is valid for every ground substitution σ for those variables. 26 / 28
  • 90. ELIS – Multimedia Lab N3 context Our model definition is oriented on the RDF semantics with the following additions: Ground implications have the usual first order meaning Except if they occur in implications, graphs are handled as simple URIs We add grounding steps for any formula f in the following order: 1. If f contains accessible universal variables, it is valid iff f σt is valid for every ground substitution σ for those variables. 2. If f contains existentials on top level it is valid iff there exists a ground substitution σ for those variables such that f σc is valid. 26 / 28
  • 91. ELIS – Multimedia Lab Notation3 Logic Implicit Quantification Formal Semantics 27 / 28
  • 92. ELIS – Multimedia Lab Conclusion It is possible to define the semantics as in the team submission 28 / 28
  • 93. ELIS – Multimedia Lab Conclusion It is possible to define the semantics as in the team submission It is difficult and rather unusual to define the scope of universals as it is proposed in the team submission 28 / 28
  • 94. ELIS – Multimedia Lab Conclusion It is possible to define the semantics as in the team submission It is difficult and rather unusual to define the scope of universals as it is proposed in the team submission Implicit universal quantification could be handled easier, for example on formula level 28 / 28
  • 95. ELIS – Multimedia Lab Conclusion It is possible to define the semantics as in the team submission It is difficult and rather unusual to define the scope of universals as it is proposed in the team submission Implicit universal quantification could be handled easier, for example on formula level Let’s simplify this! 28 / 28
  • 96. ELIS – Multimedia Lab Example: Explicit quantification @forAll #x. @forSome #y. #x #loves #y . @forSome #y. @forAll #x. #x #loves #y . 29 / 28
  • 97. ELIS – Multimedia Lab Example: Explicit quantification @forAll #x. @forSome #y. #x #loves #y . @forSome #y. @forAll #x. #x #loves #y . Have the same meaning: ∀x∃y : loves(x, y). 29 / 28
  • 98. ELIS – Multimedia Lab Example: Variable of not? @forSome :y. :x :p :y. @forAll :x. :x :q :o. 30 / 28
  • 99. ELIS – Multimedia Lab Example: Variable of not? @forSome :y. :x :p :y. @forAll :x. :x :q :o. Is the first :x quantified? 30 / 28