Semantic web rdf and inferencing

"Golden Delicious"
"Elstar"

rdfs:label

el
"Conference"

:lab
bot:GoldenDelicious

rdfs
"Gala" "Fuji"
rdfs:l
el

abel bot:Elstar
:lab

bot:Gala
el
rdfs

b
:la "any edible part of a plant with a sweet flavor"
bot:Braeburn fs
bot:Apple rd
Conference bot:Fuji
l
labe
rdfs:

t
rdf

men
"Braeburn" "Fig"

s:la

:com
be
"Fruit"

l

rdfs:label

rdfs
"Pear" "Apple" l
: labe
rdfs
cul:Fruit

el bot:Fig
"Pommegranate"
l ab
s:
df :label
rdfs
bot:pomaceousFruit bot:accessoryFruit
bot:Pommegranate
"Structure of
label

el
ab
hinese Pear" "Fruit"

s:l
rdfs:

rdf
el
:lab
el lab
rdfs rdfs:
" #" "Pomaceous Fruit" "Accessory Fruit" bot:Fruit
bo bot:PlantStructure
bot:Strawberry t:c
el on
ab ta
l ins bot:N

bo
:
fs

t

t:p
rd
el

en

ar
:lab

Semantic Web mm

tO
gkeum
s

f
:co
rdf

:Seed

s
rd
rdf
fs: bot:Plant
la "Strawberry"
be
l

DON WILLEMS

RDF and Inferencing
"!"" bo
INTELLIGENT SYSTEMS ONTOLOGY LUNCH MEETING JUNE 14TH 2011
"the ovary of a seed-bearing plant"
WAGENINGEN UR/FOOD & BIOBASEDtOf
:par
RESEARCH
bot
bot:Tomato

based on:
Chapter 5 - RDF and Inferencing
Semantic Web for the Working Ontologist
Dean Allemang and Jim Hendler
ISBN 978-0-12-373556-0

Making Data Smarter

In the semantic web we have an integrated and
distributed representation of data

Modeling is needed to make sense of this network
of data

But how can a model help us make sense of this
network of data?

Example
bot:Fruit cul:Fruit

Suppose you find RDF
data concerning bot:accessoryFruit

bot:Kosui

How would you know bot:pomaceousFruit

that Kosui is a type of
fruit? bot:Pear

bot:EuropeanPear bot:ChinesePear

bot:Conference bot:DoyenneDuComice bot:BlakesPride bot:Forelle bot:Kosui bot:Shinko bot:Whangkeum

Example: Query
Solution 1: Leverage the power of the query:
SELECT ?super WHERE {{
bot:Kosui rdfs:subClassOf ?super.
} UNION {
bot:Kosui rdfs:subClassOf [rdfs:subClassOf ?super].
} UNION {
bot:Kosui rdfs:subClassOf [rdfs:subClassOf [rdfs:subClassOf ?super]].
} UNION {
bot:Kosui rdfs:subClassOf [rdfs:subClassOf [rdfs:subClassOf
[rdfs:subClassOf ?super]]].
} UNION {
[rdfs:subClassOf [rdfs:subClassOf ?super]]]].
} ...
}

Example: Query
bot:Kosui rdfs:subClassOf ?super. ---> bot:ChinesePear
} UNION {
} UNION {
} UNION {
} UNION {
} ...
}

Example: Query
} UNION {
bot:Kosui rdfs:subClassOf [rdfs:subClassOf ?super].---> bot:Pear
} UNION {
} UNION {
} UNION {
} ...
}

Example: Query
} UNION {
} UNION {
} UNION { ---> bot:pomaceousFruit
} UNION {
} ...
}

Example: Query
} UNION {
} UNION {
} UNION {
} UNION {
---> bot:accessoryFruit
} ...
}

Example: Query
} UNION {
} UNION {
} UNION {
} UNION {
} ...
}
---> bot:Fruit
---> cul:Fruit

Example: Inferencing

Solution 2: Inferencing
IF
IF ?A rdfs:subClassOf ?B.
?A rdfs:subClassOf ?super. AND
AND ?B rdfs:subClassOf ?C.
?x rdf:type ?A. THEN
THEN ?A rdfs:subClassOf ?C.
?x rdf:type ?super.
subclass-propagation
type-propagation rule
rule

bot:Fruit cul:Fruit

IF
?A rdfs:subClassOf ?B.
bot:accessoryFruit
AND
?B rdfs:subClassOf ?C.
THEN
?A rdfs:subClassOf ?C.
bot:pomaceousFruit

bot:Pear



bot:Fruit cul:Fruit

IF
AND bot:accessoryFruit

THEN
bot:pomaceousFruit

bot:Pear



bot:Fruit cul:Fruit

How would you know bot:accessoryFruit

that Kosui is a type of
fruit?
bot:pomaceousFruit

Kosui is a subclass of
Fruit bot:Pear



Inferencing
bot:Fruit cul:Fruit

bot:accessoryFruit

Problem: Possible
explosion of the bot:pomaceousFruit

number of triples

bot:Pear



Inferencing:
asserted vs. inferred Triples
bot:Fruit cul:Fruit

bot:accessoryFruit

Asserted Triples
bot:pomaceousFruit
Inferred Triples

bot:Pear



Inferencing:
asserted vs. inferred Triples

Think of inferencing and querying
Asserted Triples as separate processes

Inferred Triples 1. Use inference rules to find all
inferred triples using the
asserted triples as a conditional

2. Run SPARQL query over
asserted and inferred triples

Inferencing:
when does inferencing happen?
Out of range of the RDFS and OWL definitions

Depends on the implementation of the inference
engine

• As soon as a conditional pattern is identified,
inference happens and inferred triples are
stored in the same RDF store --> explosion
risk

• Never store inferred triples. Inference only
happens in response to a query --> duplicate
inference work

Inferencing:
change management

One problem (if inferred triples are stored) is
change management.

• What happens when a data source changes
• A triple is added --> inferencing
• A triple is removed/changed -->
• Which inferred triples need to be
removed?

Example:
change management

cul:Ingredient

State of triple store
before inference bot:Fruit cul:Vegetable

bot:Tomato

Example:
change management

cul:Ingredient
Apply subclass-
propagation rule

bot:Fruit cul:Vegetable

bot:Tomato

Example:
change management

cul:Ingredient
Apply subclass-
propagation rule

Resulting (inferred)
triple already exists bot:Fruit cul:Vegetable

and is not added
again

bot:Tomato

Example:
change management

cul:Ingredient

State of triple store
after inference bot:Fruit cul:Vegetable

bot:Tomato

Example:
change management

cul:Ingredient
Remove erroneous
triple
(not all Fruits (in a botanical
X
sense) are ingredients (in a
culinary sense).

bot:Tomato

Example:
change management

cul:Ingredient
Remove triples that
were inferred as a
result of the
existence of the
erroneous triple.
bot:Fruit
X cul:Vegetable

bot:Tomato

Example:
change management

State after triple cul:Ingredient
removal

Problem: The
inferred triple should
not have been bot:Fruit cul:Vegetable

removed because it
could have been
inferred using
another set of
bot:Tomato
conditionals

Example:
change management

Desired state after
triple removal cul:Ingredient

Two solutions:
1. Very complex
inference and bot:Fruit cul:Vegetable

change
management

2. Remove all inferred
triples and re-infer bot:Tomato
everything

Inferencing:
forward vs. backward chaining

Until now we used forward chaining as inference
method.

1. Find all existing triples that can be used as
condition (the IF part) in the inference rule.

2. Add a new triple specified in the result (THEN)
part of the inference rule.

This inference method is mostly used after (a) new
fact(s) are added to the triple store.

Inferencing:

Backward chaining works the other way around.

1. Find all triples that can be seen as a result
(THEN part) of an inference rule.

cul:Ingredient

IF
AND
THEN

bot:Tomato

Inferencing:


2. If the triple(s) in the condition do not yet exist
try to fulfill the condition by other means (ask
the user)
Is cul:Vegetable Is Fruit
a subclass a subclass
cul:Ingredient cul:Ingredient
of cul:Ingredient? of cul:Ingredient?

? ?
bot:Fruit cul:Vegetable bot:Fruit cul:Vegetable

bot:Tomato bot:Tomato

Inferencing:


3. If the condition is fulfilled add the inferred triple
(defined in the condition) to the triple store.

cul:Ingredient

NO YES

bot:Tomato

Conclusion
• We want to be able to write a single query that
can fetch related data from all integrated data
sources.

• RDF provides a consistent way to represent data
so that information from multiple sources can
be brought together.

• Information integration is achieved by invoking
inference before or during the query process.

Semantic web rdf and inferencing

Recommandé

Recommandé

Contenu connexe

En vedette

En vedette (20)

Dernier

Dernier (20)

Semantic web rdf and inferencing

Notes de l'éditeur