"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.

1. Objective Fiction
The semantic construction of (web) reality
Aldo Gangemi
aldo.gangemi@cnr.it, @lipn.univ-paris13.fr
RCLN, LIPN Université Paris 13, UMR CNRS, Sorbonne Cité
Semantic Technology Lab, Institute for Cognitive Sciences, CNR, Rome, Italy
Work described jointly with STLab people in the last years:
Valentina Presutti, Francesco Draicchio, Andrea Nuzzolese, Diego Reforgiato
Alessandro Adamou, Eva Blomqvist, Enrico Daga, Alfio Gliozzo, Alberto Musetti

2. My arguments
Semantic technologies only sparsely
address real semantic phenomena

3. My arguments
Semantic technologies only sparsely
address real semantic phenomena
Semantic Framing is not explicit

4. My arguments
Semantic technologies only sparsely
address real semantic phenomena
Semantic Framing is not explicit
We need a semantic data science

5. Objectivity or fiction?
• Objective fiction!
• Data scientists contribute to build the reality
we live in
• The Web gives them more empowerment
• Semantics should give them even more
•
Is that happening?

6. Objective fiction
Bare fact
Berlusconi has been sentenced for tax fraud
Opinion
I like the decision of Berlusconi’s trial court
Framing
The arrest of Berlusconi’s would be an offense to millions of
voters
Like Al Capone, he’s been sentenced for the least severe
crime
Story-based repositioning
Berlusconi’s sentencing is like the story of Enzo Tortora’s (a
talk show host on Italian television, who was falsely
accused of drug trafficking)
Disinformation
The law that allows banning Berlusconi from public offices is
unconstitutional
A snakes and ladders game about Berlusconi’s conviction

7. Semantic technologies have progressed
significantly, but they still miss most relevant
semantic phenomena in social life

8. How to synthetically tell what a text is about, in
the open domain, i.e. without specific training?
What is its core meaning, emotional content,
position in the evolution of its topic, etc.?

9. How to analytically mashup data in relevant
ways, in the open domain, i.e. without
someone putting the necessary intelligence
within?

10. A sample correlation fallacy
• In 2010, a data scientist presented an
analysis of Facebook status updates
• Focus was on terms break up, broken up
• Results show a curve that peaks in early
summer and before Christmas
Reported by: Has the Internet changed Science?, by E. Pisani, Prospect, November 2010

11. Interpretation of the speaker
“relationships melt down because of the
stress of spending time together”
Reported by: Has the Internet changed Science?, by E. Pisani, Prospect, November 2010

12. Question by an attendee
“maybe not about relationships, but the end of
terms, e.g. broke up for Christmas last Tuesday”
Reported by: Has the Internet changed Science?, by E. Pisani, Prospect, November 2010

13. Partly open problems
•
Data integration interpretation without
designers (risk of correlation fallacy)
•
Opportunistic reasoning: travel planning,
financial opportunities, team building, etc.
•
•
•
Smart text summarization
Opinion mining on the right spots
Domain dynamics: science evolution, scholar
changes, market dynamics, ...

14. Human, social
knowledge management
is not exempt from
framing: it is modulated
by frames, metaphors,
and stories that make
something relevant
through neural
activation patterns

15. People can be aware of framing ...
Pareidolia

16. ... but often they are not
Think about how to frame an issue using your value
For example, if the issue is poverty and your value is protection
From a Foot Print Strategies Inc. spin doctor’s presentation

17. ... but often they are not
Think about how to frame an issue using your value
For example, if the issue is poverty and your value is protection
“Every family deserves a safe and healthy place to live”
Being_at_risk
frame (FrameNet)
Restructured from a Foot Print Strategies Inc. spin doctor’s presentation

18. Political reframing
•
Conservatives say
•
•
We need tax relief
•
Same sex marriage will
undermine family life
•
Trial lawyer. Frivolous
lawsuits
We need a strong
president to protect us
• Progressives say
•
•
Taxes are investments
•
Marriage is the realization of
love in a lifetime
commitment
•
Public protection attorney
We have a weak president
who didn’t protect us
Restructured from a Foot Print Strategies Inc. spin doctor’s presentation

19. I’m in good company
•
Myself (you never know!) (ESWC2009 keynote): knowledge patterns as
objects of empirical investigation
•
Frank Van Harmelen (ISWC2011 keynote): route to empirical research:
data science, data patterns
•
Martin Hepp (EKAW2012 keynote): web semantics not necessarily
coincident with DL and traditional OE
•
David Karger (ESWC2013 keynote): what can the SW do for average
users? Not much until now
•
Enrico Motta (ESWC2013 keynote): what semantics in the current SW?
Different forces, still faith in good old logic
•
John Sowa (SemTech2013 lecture): patterns exist at different levels of
data, ontologies, and reality

20. ... on the shoulders of
• Köhler, Bartlett, Piaget, Fillmore, Minsky, and
many cognitive and neuro- scientists ...

21. Hypotheses from cognitive
semantics
Cf. 2012 Dagstuhl Seminar on Cognitive
Science for the Semantic Web

22. Reality is “framed” by
our prior knowledge,
expectations,
opportunities, goals,
which are organized as
conceptual frames and
stories, and linked to
our emotions

23. Public
services
Becoming
visible
Desirability
Quantity
IS Vertical
position
Price per
unit
Arithmetic
commutative
Risky
situation
Institution
IS Family
Frames are diverse
Precariousness
more or less abstract, complex,
metaphoric, or specific
Criminal
investigation
Partwhole
many of them stay unconscious
most of the time
Affectivity IS
Temperature
Being
‘mbari in
Catania
Family
in Italian
Law
Facebook
Timeline
format
Address
microformat
most are evident in human artifacts
they play a role in narratives (idealized
stories) used to drive our decisions and
motivate our plans
Cf. George Lakoff ’s The Political Mind

24. Frames are part of our biology
Frames create our individual counterpart to reality
They are activated in neural binding, emotional
paths, somatic markers and mirror neurons
Neural binding allows to “connect the
pieces” that come from perception,
recall, abstraction, and imagination
Neural binding works according to
emotional paths (dopaminergic,
noradrenergic), linked to narratives and
frames (hypothesis)
Mirror neurons activate the same circuitry
for actual perception, recall of perception,
abstraction of perception, and imagination
of new perceptions

25. • Semantics of social reality is implemented
in media applications but it is hard-coded
• It remains in the mind of designers
• Semantic Web is overlooking to make it
explicit

26. Semantic expressivity?
•
Is our semantics enough to support extraction,
representation, and harnessing of social semantics?
•
•
triples are simple structures
classes represent arbitrary concepts
where is cognitive adequacy?
when does a class represent arbitrary data, and when is
it a counterpart of a human knowledge pattern?
is that difference important in general?

27. Administrative
frames
Geographic
frames
Communication
frames
DBpedia
When triplifying Wikipedia
infoboxes, its designers
lost the framing of boxes
and internal sub-boxes

28. The case of Infoboxes
•
Infobox framing is missing in the DBpedia
ontology too
•
If we mine the ontology to check what
properties can be applied to what classes, the
result is partial and often non-correspondent to
the original frame
•
Scraping heuristics may be more cognitivelysound ...

29. Interaction semantics
• Interfaces and interaction patterns convey
frame semantics
•
Schema induction and triplification of databases
can be improved by exploiting interfaces exposing
data, cf. data.cnr.it ontology design
•
HTML pages and stylesheets contain a lot of
framing knowledge, cf. Craig Knoblock’s work
•
Infographics can change the way we interpret the
same data

30. Cognitive principles of KR on the Web
Empirical Conservativeness
Relevance in Modeling
Structured Provenance
To be added to LOD principles

31. Cognitive principles of KR on the Web
Empirical Conservativeness
Relevance in Modeling
Structured Provenance
To be added to LOD principles

32. Empirical conservativeness
What is present with a function in (evident,
extracted, emerging) empirical data should be
preserved in its semantic representation

33. Empirical conservativeness
• It is a measure against “oversimplification”
• The case of Infobox framing loss is a sample
violation of this principle

34. Special case
• Keeping interaction boundaries is a special
case of empirical conservativeness
• Like neural binding (at the neural level) and
linguistic framing (at the cognitive level),
relevant boundaries of logical
representations need to be represented
Cf. original Marvin Minsky’s frames:
“representations that mirror cognitive mechanisms”

35. Is there anything like that in OWL or RDF?
Maybe ontology modules, classes, named
graphs, hasKey axioms
Not specific to the boundary problem, nor to framing or
neural binding
*Very recent: new spec for named graphs accepts typing

36. • With infoboxes, we are still discussing a
case of basic semantics, since it is fully
presented in data
• More cases from social reality include
slippery relations:
counting as, intentionality, action schemes, normative
constraints, emerging patterns, frames, metaphors, irony,
stories, socio-technical task semantics

37. • Those relations are partly implicit, and the
modeling practices we use on SW/LOD are
not designed for that, contra Minsky

38. More semantics or more distinctions?
•
I am not advocating for “more semantics” in
terms of complexity, rather for more distinctions
•
•
Human knowledge is relational in nature
•
Classes are powerful primitives in logical languages,
specially in description logics and triple-based
languages
We need n-ary and multigrade relations, but arbitrary
relations would be too much in current KR scenarios,
then we can use them with smart reification patterns

39. More semantics or more distinctions?
• Fixation on classes goes with a trade-off
•
Classes need to be distinguished in terms of
design
•
Class-oriented representation needs a “push-up”
to partly recover the lost structure

40. Class types?
Types of classes have been distinguished in the past
•
•
AI: sorts and types
•
OWL2 punning: arbitrary typing of classes
Formal Ontology: OntoClean metaclasses, based on formal
criteria
Solutions span between the two extremes: heavy principles
(OntoClean) - no principle at all (punning)

41. Pragmatic meta-classes
• How about distinguishing classes that
implement interesting social relations?
• This classification should produce
“reference frames” when querying,
reasoning, or reusing data and ontologies,
as well as when aligning, extracting, and
discovering data and ontologies

42. Relevance in modeling
When modeling a class, its design motivation
(relevance) must be explicit: it should be typed
with the reason for that relevance

43. Relevance in modelling
• “What’s special in that class?”
•
E.g., it’s central in the data, it’s a frame, it’s an n-ary
reification mechanism, it’s the result of a discovery
algorithm, etc.
• A new vocabulary for metaclasses?

44. • Top-down principles do not work unless
people adopt them or there are
procedures to discover them in data
•
•
Disseminating a good practice
A research program of discovering and reusing
class types for the common good

45. Structured provenance
When merging RDF triples, they should come
with their provenance data

46. The RDF mix case (sig.ma)

47. Some results from STLab

48. http://wit.istc.cnr.it/stlab-tools/
We (STLab and RCLN) are researching on
Discovering
Reengineering
knowledge patterns as keys for accessing
meaning on the Web
Collecting
Using

49. A broader vision: knowledge
patterns and their façades
Informal
graphs
•
DL & Rule
patterns
Data patterns
Cognitive
patterns
The Knowledge Pattern (KP) Model is a discovery and analogy structure
<C,L,I,D,W,T,V,X,F,S,R,U> such that a KP emerges out of invariances across multiple façades:
•
•
•
•
•
•
•
•
•
•
•
•
C: concept graphs
L: logical forms (some axiomatization of multigrade predicates)
Ontology
design
patterns
I: local inference rules (either classical or approximate)
D: (relational) data
W: linguistic data
T: social tagging data
Lexical and
NLP data
patterns
V: interaction/visualization/formatting structures
Socio-patterns
X: provenance data
F: framing information
S: sentic information
R: relations to other KP
Web and
interaction
patterns
U: use cases
Task patterns
All façades can provide features for
stochastic processes
All façades should be encoded in RDF
for interoperability and joint reasoning
Aldo Gangemi,Valentina Presutti: Towards a pattern science for the Semantic Web. Semantic Web 1(1-2): 61-68 (2010)

50. Description:
Compatibility
scenario
Assessment layer
A pattern framework:
Descriptions & Situations
satisfies
Situation:
Entrenchment Case
hasSetting
hasSetting
Description:
Norm
Normative layer
hasSetting
satisfies
Situation:
Case in point
Social layer
Description:
Meta-Norm
SETTING
Description:
Social norm
satisfies
hasSetting
satisfies
Situation:
Jurisprudential
Conflict Case
Sample application to
modeling legal cases with
norms, conflicts, and
entrenchment
Aldo Gangemi, Peter Mika: Understanding the Semantic Web through Descriptions and Situations. ODBASE 2003: 689-706

51. Layered pattern morphisms
An ontology design pattern describes a formal expression that
can be exemplified, morphed, instantiated, and expressed in order to
solve a domain modelling problem
•
•
•
•
•
owl:Class:_:x rdfs:subClassOf owl:Restriction:_:y
Inflammation rdfs:subClassOf (localizedIn some BodyPart)
Colitis rdfs:subClassOf (localizedIn some Colon)
John’s_colitis isLocalizedIn John’s_colon
“John’s colon is inflammated”, “John has got colitis”, “Colitis is the inflammation of
colon”
expressedAs
Logical
Pattern
(MBox)
Logic
exemplifiedAs
Generic
Content
Pattern
(TBox)
morphedAs
Specific
Content
Pattern
(TBox)
Meaning
instantiatedAs
Data
Pattern
(ABox)
Reference
expressedAs
Linguistic
Pattern
Expression
Abstraction
Aldo Gangemi,Valentina Presutti: Ontology Design Patterns. Handbook on Ontologies 2nd ed. (2009)

52. N-ary patterns in KR
• Temporal indexing pattern
– (R(a,b))+t sentence indexing
• quads, external time stamps
– R(a,b)+t relation indexing
• reified n-ary relations (3D frames)
– R(a+t,b+t) individual indexing
• fluents, 4D, tropes, “context slices” (4D frames)
– tR name nesting
• ad hoc naming of binary relations
• More indexes for additional arguments
Aldo Gangemi,Valentina Presutti: A Multi-dimensional Comparison of Ontology Design
Patterns for Representing n-ary Relations. SOFSEM 2013: 86-105
Andreas Scheuermann, Enrico Motta, Paul Mulholland, Aldo Gangemi and Valentina
Presutti. An Empirical Perspective on Representing Time. K-CAP 2013

53. Centrality discovery in datasets
mo:Track
mo:Playlist
mo:track
mo:Record
mo:available_as
mo:MusicAr.st
mo:ED2K
mo:available_as
foaf:maker
mo:available_as
tags:taggedWithTag
mo:image
dc:date
dc:+tle
dc:descrip+on
tags:Tag
rdfs:Literal
mo:Torrent
Valen&na
Presu,,
Lora
Aroyo,
Alessandro
Adamou,
Balthasar
Schopman,
Aldo
Gangemi,
Guus
Schreiber:
Extrac&ng
Core
Knowledge
from
Linked
Data.
COLD2011,
CEUR-­‐WS.org
Vol-­‐782.

54. Encyclopedic Knowledge Patterns:
example
•
•
An Encyclopedic Knowledge Pattern (EKP) is discovered from the paths
emerging from Wikipedia page link invariances
They are represented as OWL2 ontologies
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna'onal
Seman'c
Web
Conference
(1)
2011:
520-­‐536

55. Encyclopedic KP: input data
• Wikipedia page links generate 107.9M triples
• Infobox-based triples are 13.6M, including data value triples (9.4M)
• “Unmapped” object value triples are only 7% of page links

56. Encyclopedic KP: input data
• Wikipedia page links generate 107.9M triples
• Infobox-based triples are 13.6M, including data value triples (9.4M)
• “Unmapped” object value triples are only 7% of page links
dbpo:MusicalArtist
dbpo:MusicalArtist
dbpo:Organisation
dbpo:Place

57. Encyclopedic KP: input data
• Wikipedia page links generate 107.9M triples
• Infobox-based triples are 13.6M, including data value triples (9.4M)
• “Unmapped” object value triples are only 7% of page links
• Paths are used to discover Encyclopedic Knowledge Patterns
– Such patterns should make it emerge the most typical types of things that the Wikipedia
crowd uses to describe a resource of a given type
dbpo:MusicalArtist
dbpo:MusicalArtist
rtist
usicalA
nksToM
li
links
dbpo:Place
ToPl
a
linksToOrganisation
dbpo:Organisation
ce

58. Encyclopedic KP: input data
• Wikipedia page links generate 107.9M triples
• Infobox-based triples are 13.6M, including data value triples (9.4M)
• “Unmapped” object value triples are only 7% of page links
• Paths are used to discover Encyclopedic Knowledge Patterns
– Such patterns should make it emerge the most typical types of things that the Wikipedia
crowd uses to describe a resource of a given type
Path à Pi,j= [Si, p, Oj]
dbpo:MusicalArtist
dbpo:MusicalArtist
rtist
usicalA
nksToM
li
links
dbpo:Place
ToPl
a
linksToOrganisation
dbpo:Organisation
ce

59. k-means clustering
on Path Popularity
Sample distribution of pathPopularity for DBpedia
paths. The y-axis indicates how many paths (on
average) are above a certain value t for
pathPopularity
Encyclopedic
Knowledge Patterns
from Wikipedia
Wikilinks
(@ISWC2011)
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna&onal
Seman&c
Web
Conference
(1)
2011:
520-­‐536

60. k-means clustering
on Path Popularity
Sample distribution of pathPopularity for DBpedia
paths. The y-axis indicates how many paths (on
average) are above a certain value t for
pathPopularity
Encyclopedic
Knowledge Patterns
from Wikipedia
Wikilinks
(@ISWC2011)
1 big cluster (4-cluster) with
ranks below 18.18%
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna&onal
Seman&c
Web
Conference
(1)
2011:
520-­‐536

61. k-means clustering
on Path Popularity
Sample distribution of pathPopularity for DBpedia
paths. The y-axis indicates how many paths (on
average) are above a certain value t for
pathPopularity
3 small clusters with
ranks above 22.67%
1 big cluster (4-cluster) with
ranks below 18.18%
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna&onal
Seman&c
Web
Conference
(1)
2011:
520-­‐536
Encyclopedic
Knowledge Patterns
from Wikipedia
Wikilinks
(@ISWC2011)

62. k-means clustering
on Path Popularity
Sample distribution of pathPopularity for DBpedia
paths. The y-axis indicates how many paths (on
average) are above a certain value t for
pathPopularity
3 small clusters with
ranks above 22.67%
1 big cluster (4-cluster) with
ranks below 18.18%
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna&onal
Seman&c
Web
Conference
(1)
2011:
520-­‐536
Encyclopedic
Knowledge Patterns
from Wikipedia
Wikilinks
(@ISWC2011)

63. k-means clustering
on Path Popularity
Sample distribution of pathPopularity for DBpedia
paths. The y-axis indicates how many paths (on
average) are above a certain value t for
pathPopularity
3 small clusters with
ranks above 22.67%
Encyclopedic
Knowledge Patterns
from Wikipedia
Wikilinks
(@ISWC2011)
1 big cluster (4-cluster) with
ranks below 18.18%
1 alternative cluster (6cluster) with ranks below
11.89%
Andrea
Giovanni
Nuzzolese,
Aldo
Gangemi,
Valen&na
Presu,,
Paolo
Ciancarini:
Encyclopedic
Knowledge
PaUerns
from
Wikipedia
Links.
Interna&onal
Seman&c
Web
Conference
(1)
2011:
520-­‐536

64. Serendipity in exploratory browsing
http://www.aemoo.org
Andrea
Giovanni
Nuzzolese,
Valen&na
Presu,,
Aldo
Gangemi,
Alberto
Muse,,
Paolo
Ciancarini:
Aemoo:
exploring
knowledge
on
the
web.
WebSci
2013:
272-­‐275
Aemoo:
exploratory
search
based
on
EKP
-­‐
Seman'c
Web
Challenge
@ISWC
2011
–
Short
listed,
4th
place

65. Machine reading with FRED
http://wit.istc.cnr.it/stlab-tools/fred/
Valen&na
Presu,,
Francesco
Draicchio,
Aldo
Gangemi:
Knowledge
Extrac&on
Based
on
Discourse
Representa&on
Theory
and
Linguis&c
Frames.
EKAW
2012:
114-­‐129

66. Event and Frames from text
http://wit.istc.cnr.it/stlab-tools/fred/
The
New
York
Times
reported
that
John
McCarthy
died.
He
invented
the
programming
language
LISP.
Custom
namespace
Meta-­‐level
Frames/events
Seman&c
roles
Vocabulary
alignment
Co-­‐reference
Resolu&on
and
linking
Taxonomy
Valen&na
Presu,,
Francesco
Draicchio,
Aldo
Gangemi:
Knowledge
Extrac&on
Based
on
Discourse
Representa&on
Theory
and
Linguis&c
Frames.
EKAW
2012:
114-­‐129
Types

67. Sentic frames from text
http://wit.istc.cnr.it/stlab-tools/sentilo

68. Sentic frames from text
http://wit.istc.cnr.it/stlab-tools/sentilo
Paul
Newman
thinks
that
Barack
Obama
is
a
great
president!

69. But beware “patternicity”

70. Psychopathology of big data
• Pattern recognition vs. patternicity:
•
•
•
•
•
Simple correlation fallacy
Synchronicity
Pareidolia
Conspiracy theories
Schizophrenic apophany

71. Web helps spreading patternicity
• More information
• Faster spread of information
• More difficult provenance checking

72. Finally, back to the objective
fiction problem
Logic, ontologies, and data design practices
construct a reality
Similarly to what social institutions do since
millennia by playing with the many layers at which
communication means can be morphed

73. The reality currently built by triple-based
languages is basic
It looks more like a quiz-show than fullfledged social reality

74. • On the contrary, the reality constructed by
current institutions and media is a glorious
“objective fiction”
• A powerful system feeding a giant, analogical
Matrix that is partly opaque to most people

75. • Our use of semantics should try to
approximate the level of sophistication that
objective fiction has gathered to now
• We have a responsibility of creating an
added value: making objective fiction
explicit

76. •
When simple objective data are provided, its
semantic representation is too simple to
provide added value to users
•
We need to raise our semantic grasp to
institutional relations, hidden knowledge
patterns, action, situation, sentic and metaphoric
frames, leading stories, socio-technical tasks
•
Semantic technologies should make us aware of
real semantics, not just bare facts

77. That's very difficult, but the alternative is leaving
real semantics to spin doctors

78. •
Worse, simple triple-based semantics gives them
more power
•
Transparency and distributed decision making
being easily accessible on an open Semantic Web:
the Holy Grail?

79. Thanks for your attention!