1

Technologies for an Intelligent Web

Francis Heylighen Center Leo Apostel Vrije Universiteit Brussel

2

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

3

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

4

Global network - Global Brain?

5

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

6

Hypertext network

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our plans are go ing ahead, Heylighen is getting tickets, so le t' s putthat in in hard pencil , but keep your eraser handy all the same. My li fe isrea lly hectic, and I won't be in a sane place till mid-Apri l. We'l l pindown the deta ils la ter, but at this poin t we were considering Val only totalk on "The Metasystem Transi tion as the Quantum of Evolution". This isthe theoretical base to the PCP, which I described the form of in my talkto WESS. It's basically between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only tota lk on "The Metasystem Transition as the Quantum of Evolution". Th is isthe theoretical base to the PCP, which I described the form of in my ta lkto WESS. It's basica lly between Francis and Val how would like to talk, orboth, or what.

Our p lans are going ahead, Heyl ighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy all the same. My li fe

Our p lans are going ahead, Heylighen is getting tickets, so let's putthat in in hard pencil, but keep your eraser handy a ll the same. My life isreal ly hectic, and I won 't be in a sane place ti ll mid-April . We'll p indown the detai ls later, but a t this po int we were considering Val only

7

Network of Nodes and Links

8

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

9

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

10

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

11

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

12

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

13

The Learning Web Experiment

14

Results from the experiment

knowledge0 200 steps 800 steps 4000 stepstrade education education educationview experience experience experiencehealth example development researchtheory theory theory developmentface training research mindbook development example lifeline history life theoryworld view training trainingside situation order thoughtgovernment work effect interest

15

Associative Network from Experiment

16

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

17

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

18

Spreading activation illustration

seagull

waterbird river

river edge

bank

money

rate

sea

financialinstitution

sit

support

building

ground

19

Spreading activation illustration

seagull

waterbird river

river edge

bank

money

rate

sea

financialinstitution

sit

support

building

ground

20

Spreading activation illustration

seagull

waterbird river

river edge

bank

money

rate

sea

financialinstitution

sit

support

building

ground

21

Spreading activation illustration

seagull

waterbird river

river edge

bank

money

rate

sea

financialinstitution

sit

support

building

ground

22

Spreading activation illustration

seagull

waterbird river

river edge

bank

money

rate

sea

financialinstitution

sit

support

building

ground

23

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

24

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

25

Spreading Authority

26

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

27

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

28

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: s(A,B) =aibi

i∑ai

2

i∑ bi

2

i∑

.

29

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

30

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