Discussion on Uncertainty
Ontology for Annotation and
Reasoning (a position paper)
J. Dědek, A. Eckhardt, L. Galamboš,
P. Vojtáš
Charles University Prague
Positions for discussion
•Discuss the what, who, when, where, why and how
of uncertain reasoning
•First annotate (WIE) then reason
•Uncertainty Ontology and/or ontology for uncertainty
annotation (and reasoning)
•Scope – whole web?
•Uncertainty coming from a third party annotation
•Stepwise semantization of the web
URW3X Uncertainty Ontology
Domain independent WIE
Domain independent WIE
Uncertainty
Linguistic anotation - UFALware
(He) would
go
to the forest
.
Uncertainty
Domain dependent anotation
Uncertainty
Context of Our Experiments
Semantic Web & Semantic Data Extraction
ILP
background
knowledge
Web
Extraction
process
Linguistic
trees
Texts
Extraction
rules
Human
annotator
+
Learning
examples
+ Semantics
• Get semantics form Web of today
• Czech pages, Czech texts
• Czech linguistic tools
ILP
learning
Extracted
Semantic
data
• Domain of traffic accidents
• Semantics given by human
• Generalized & extracted by ILP
tree_root(node0_0). node(node0_0).
id(node0_0, t_jihomoravsky49640_txt_001_p1s4).
%%%%%%%% node0_1 %%%%%%%%%%%%%%%%%%
node(node0_1).
functor(node0_1, pred).
gram_sempos(node0_1, v).
t_lemma(node0_1, zemrit).
%%%%%%%% node0_2 %%%%%%%%%%%%%%%%%%
node(node0_2).
functor(node0_2, act).
gram_sempos(node0_2, n_pron_def_pers).
t_lemma(node0_2, x_perspron).
%%%%%%%% node0_3 %%%%%%%%%%%%%%%%%%%
node(node0_3). id(node0_3,
functor(node0_3, loc).
gram_sempos(node0_3, n_denot).
t_lemma(node0_3, trabant).
...
edge(node0_0, node0_1). edge(node0_1, node0_2).
edge(node0_1, node0_3). edge(node0_3, node0_4).
edge(node0_4, node0_5). edge(node0_3, node0_6).
edge(node0_3, node0_7). edge(node0_3, node0_8).
...
Logic representation
Source web page
Linguistic trees
Domain dependent anotation
m/tag
Incident
actionManner
t_lemma
String*
negation
Boolean
actionType
hasParticipant
String
Instance*
Participant
hasParticipant*
t_lemma
Participant
t_lemma
t_lemma
+ numeral translation
participantType
String
participantQuantity
Integer
Uncertainty
Querying with a help of an agent
Semantic
Data
Semantic
Store
Semantic
Data
Semantic
Search
Engine
Query
Agent 1
Recommendation
User preferences /
User feedback
User 1
Proposal of an agent
3 * Price  1 * Consumptio n
@Price, Consumptio n  
4
evaluation
User1 - price
3
2
1
0
0
50
100
150
200
1500
2000
price
evaluation
User2 - distance
3
2
1
0
0
500
1000
distance
Uncertainty
Nominal atributes
• By importance of attribute values
Number of objects
100
Red
Black
Blue
1
Rating of the object
Green
Orange
„expressive“ objects
Domain of the attribute
„inexpressive“ objects
• Average of importance of values is
importance of the whole attribute
Uncertainty
Proposal of ontology for uncertainnty annotation
First extract (WIE) then annotate
and finally reason (query)
Idea of web semantization
WEB
Web
Store
Semantic
Content
HTML
page
Web
Crawler
Extractor 3
(semantic)
Extractor 2
(linguistic)
Extractor 1
(classifier)
New
Semantic
Content 3
New
Semantic
Content 2
+
+
Semantic
ContentSemantic
Content
Semantic
Content
is
growing
+
New
Semantic
Content 1
Semantic
Content
Semantic
Store
Positions for discussion
•Discuss the what, who, when, where, why and how
of uncertain reasoning
•First annotate (WIE) then reason
•Uncertainty Ontology and/or ontology for uncertainty
annotation (and reasoning)
•Scope – whole web?
•Uncertainty coming from a third party annotation
•Stepwise semantization of the web