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Technologies for an Intelligent Web
Francis Heylighen Center Leo Apostel Vrije Universiteit Brussel
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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
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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
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Global network - Global Brain?
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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
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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
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Network of Nodes and Links
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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
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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
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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
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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
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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
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The Learning Web Experiment
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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
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Associative Network from Experiment
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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
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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
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Spreading activation illustration
seagull
waterbird river
river edge
bank
money
rate
sea
financialinstitution
sit
support
building
ground
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Spreading activation illustration
seagull
waterbird river
river edge
bank
money
rate
sea
financialinstitution
sit
support
building
ground
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Spreading activation illustration
seagull
waterbird river
river edge
bank
money
rate
sea
financialinstitution
sit
support
building
ground
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Spreading activation illustration
seagull
waterbird river
river edge
bank
money
rate
sea
financialinstitution
sit
support
building
ground
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Spreading activation illustration
seagull
waterbird river
river edge
bank
money
rate
sea
financialinstitution
sit
support
building
ground
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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
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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
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Spreading Authority
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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
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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
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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∑
.
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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
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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
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