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Technologies for an Intelligent Web

Francis Heylighen

Center Leo Apostel

Vrije Universiteit Brussel
1
What is intelligence?
 capacity for problem-solving in the widest sense
 problem =difference between perceived and preferred

input = perception, output =plan for action
 problem-solving=

efficiently exploring mental map

includes interpretation, search, inference, decision-making,
etc.

selecting the adequate combination of resources to go from
present state to desired state
 requires mental map or knowledge

representation of problem states and resources
2
Collective intelligence
 synergy

when the group can find more/better solutions than the sum of
solutions found by all members individually
 requires Collective Mental Map

integrated sum of all individual knowledge

read/write access for all people
 no individual or computer can store a CMM for humanity

externall, shared memory

requires a distributed representation/search

must self-organize: no centralized control possible
 the “web” can be made to function as a CMM
3
Global network - Global Brain?
4
The web as as a collective mental map
 distributed knowledge system

sum total of individual contributions

coherent because of its interlinking
 global neural network

Web pages as neurons

hyperlinks as synapses
 problem-solving support

helps the user collect the resources that solve their problem

e.g. “find me …”
– a second-hand video recorder
– the quickest way to travel from here to there
– the treatment that tackles symptoms
– information about growing blueberries
5
Hypertext network
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Ou r p lan s a re goi ng
ahe ad, Heyl igh en is
getting ticke ts, so
let's put
th at i n in ha rd
pen cil, but keep
your era ser han dy
all the sa me. My li fe
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Ou r p lan s a re goi ng ah ead , He ylig hen i s
getting ticke ts, so let's put
th at i n in ha rd pen cil, but keep yo ur
erase r h and y a ll the same . My life is
re al ly hectic, and I won 't be i n a sane
pla ce ti ll mid -April . We 'll p in
dow n th e detai ls later, b ut a t this po int
we we re consi deri ng Va l onl y to
ta lk on "The Metasystem Tra nsitio n as
th e Qu antum of Evolu ti on". Th is is
th e th eore ti cal ba se to the PC P, wh ich I
descri bed the form of in my ta lk
to WESS. It's b asica lly be tw een Fran cis
and Val ho w wou ld li ke to tal k, or
both, or wha t.
Our pla ns are go ing a hea d, H eyli ghe n is
ge ttin g ti ckets, so le t' s p ut
that in in h ard pe ncil , bu t kee p your
era ser han dy all the sa me. My li fe i s
rea lly he cti c, an d I wo n't be in a sa ne
pl ace till mi d-Apri l. We'l l pin
do wn the de ta ils la te r, but at thi s p oin t
we w ere con side ring Val o nly to
talk on "The Me ta syste m Transi ti on a s
the Quan tu m o f Evol ution ". This is
the theo retical b ase to th e PCP, whi ch I
de scribe d th e fo rm o f in my talk
to WESS. It's basi call y b etwe en Franci s
an d Va l how w oul d like to talk, or
bo th , or wh at.
6
Network of Nodes and Links
7
Web as network of resources
 Nodes are any resources that can help solve problems

web documents

computer programs or databases

software agents

products: fridges, TVs, phones, ...

people

organizations, public or commercial
 Links are relations between resources

hyperlinks

people having access to other people/devices/organizations..

relations between databases or programs
8
Links as relations
 links can have types

e.g. “is author of”, “cites”, “lives in”, “works for”, “is a type of”…
 links can have weights
 link weights measure degree of association

effort needed for the one to “access” or “connect to” the other

e.g. order in which telephone numbers are listed in cellular
phone memory
– first ones are easier to access
9
Metasystem Transitions in the Brain
 one-to-one communication

direct transmission

traditional media: phone, post, ...
 many-to-many communication

integrating and processing different signals

this is the level of the present web
 learning

creating/adapting connections from experience
 thought

exploring combinations never experienced together
 discovery

developing new concepts, rules and models
10
Learning Webs
 let the web learn from the way it is used

optimize connection between initial and desired states
 assumption: users go from a web page to relevant page
 when link between two pages is used, weight is increased

unused links are correspondingly weakened
 indirect links too are reinforced


user goes A  B, and B  C, then also A  C is
reinforced
creates shortcuts for often travelled paths
 turns the web into an associative network

the more associated the nodes, the stronger their connection

organization similar to the brain
11
The Learning Web Experiment
 performed by Johan Bollen and myself
 150 most frequent English nouns
 each word gets one web page
 each page is linked randomly to 10 other pages/words
 users are asked to choose the best association out of 10
12
The Learning Web Experiment
13
Results from the experiment
knowledge
0
trade
view
health
theory
face
book
line
world
side
government
200 steps
education
experience
example
theory
training
development
history
view
situation
work
800 steps
education
experience
development
theory
research
example
life
training
order
effect
4000 steps
education
experience
research
development
mind
life
theory
training
thought
interest
14
Associative Network from Experiment
15
Hebbian Rule for Web Learning
 Connection is strengthened proportional to joint activation
 Activation = degree of “usefulness” for user

explicit evaluation by user

implicit evaluation derived from
– duration of visit
– bookmarking, saving, printing, ordering, etc.
 Joint activation = usage by same user

product of activation degrees

activation can be negative -> link weakened
– if user dislikes resource

activation decays exponentially

 reinforcement decays with interval between usages
16
Spreading activation
 Associative networks can be explored in parallel

users can only move sequentially between nodes
 “input” nodes can be activated simultaneously

activation follows associative links to other nodes

these are in turn activated, proportionally to link strength
 thus, activation spreads over a semantic neighborhood
 primitive form of “thinking”

exploring different combinations of concepts
17
Spreading activation illustration
sea
river
bird
water
river edge
ground
seagull
bank
support
building
financial
institution
sit
money
rate
18
Spreading activation illustration
sea
river
bird
water
river edge
ground
seagull
bank
support
building
financial
institution
sit
money
rate
19
Spreading activation illustration
sea
river
bird
water
river edge
ground
seagull
bank
support
building
financial
institution
sit
money
rate
20
Spreading activation illustration
sea
river
bird
water
river edge
ground
seagull
bank
support
building
financial
institution
sit
money
rate
21
Spreading activation illustration
sea
river
bird
water
river edge
ground
seagull
bank
support
building
financial
institution
sit
money
rate
22
Personalized Recommendations
 agent collects appreciated items

e.g. liked pages, music records, concepts
 by spreading activation from these elements, the agent tries
to find associated items, e.g.

related pages, similar records

pages related to all concepts
– e.g. “paper”, “work”, “room” -> “office”
 the agent “recommends” the most activated items

these are most likely to please the user
 similar to collaborative filtering

recommend items appreciated by people with similar tastes
23
Finding attractors
 If spreading is repeated many times, activation concentrates
in “attractors” of the network

densely connected clusters of nodes
 equivalent to calculating eigenvectors of linking matrix
 Application: finding “communities”

related pages on a subject

e.g. Kleinberg, CLEVER project
 Application: determining authority

Google’s PageRank algorithm

most “attractive” pages are most authoritative
24
Spreading Authority
25
Ill-Structured Problems
 User in general cannot formulate problem/goal/preferences

only vague associations

e.g. diarrhoea, constipation, cramps, colon, gas, bloating...

implicit problem: “How to cure Irritable Bowel Syndrome?”

activate symptom resources

let activation spread

find most authoritative documents that solve problem
 The web “thinks ahead” of the user

takes into account implicit signs of interest

suggests solutions to problems the user may not even be
aware of
26
The Semantic Web
 Spreading activation diffuses or ends up in attractors

loss of information with respect to initial state
 Constrained spreading activation = inference

follow only specific link or node types

allows activation to spread in a much more focused way
 Answering structured queries

E.g. lady works for client, lives in Washington, has son that
goes to Princeton
– link types “employed by”, “adress”, “child of”, “studies at”, ...

E.g. appointment with nearest plumber within free hours
 Requires consensual ontologies

explicit taxonomies of types and their relations
27
Collective Development of Ontologies
 Ontological categories must be formal, unambiguous

very hard to develop manually
 Clustering

put “similar” items into same category

from soft associations to hard categories
 Bootstrapping

concepts defined by relations with other concepts
– represented as column vectors of association matrix

concepts more similar if associations overlap more

similarity s can be calculated
as dot product of vectors:
a b
i i
s(A, B) 
i
a b
2
i
i
i
.
2
i 28
Knowledge Discovery
 Web can autonomously create new knowledge

clustering  new categories or concepts

rule: if (concept), then (other concept)
– e.g. if banana, then yellow; if fire and gas, then explosion

system of concepts and rules  knowledge
 Ex. medical syndrome

huge database of persons, symptoms, treatments, etc.

clustering on the basis of symptoms  distinguishing
syndromes

correlating syndromes, treatments and outcomes  finding
best treatment for given syndrome
29
Conclusion
 web can be seen as network of nodes and links

nodes = resources
 new links can be learned implicitly from usage

makes the web more efficient, intuitive, dense, ...
 network can be explored through spreading activation

allows vague, intuitive, unstructured queries
 ontologies can be used to structure web

allows concrete, explicit queries
 new structures can be mined from implicit relations

allows creation of ontologies, knowledge discovery
30