14 Social Systems 14.1 Generating ontologies 14.2 Wisdom of the - - PDF document

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07.07.2009 1

Wolf-Tilo Balke Christoph Lofi Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de

Knowledge-Based Systems and Deductive Databases

14.1 Generating ontologies 14.2 Wisdom of the crowds 14.3 Folksonomies

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 2

14 Social Systems

• Last week we saw ontologies as a powerful

instrument for…

– Representing knowledge – And reason about it!

• Ontologies, rules and logics form

the middle layer of the proposed Semantic Web stack

– Formal syntax – Formal semantics

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 3

14.0 Semantic Web Reasoning

• OWL is the language (and semantics) of choice

for the ontology part

– But OWL DL has a somewhat different semantics from RDF/S – And OWL Full is compatible with RDF/S, but computationally difficult…

• Extensions to first order logic (FOL) or other

extensions, such as simple common logic (SCL) are even more difficult

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 4

14.0 Semantic Web Reasoning

• Thus, the stack does not really consists of a set of

languages building directly and completely on the lower languages (RDF/S  OWL  logic)

– Also a subsequent refinement to the „DL-program‟ bit of OWL and the split between OWL and rule languages did not help much – RDF triples encode facts, but are also used to encode syntax…

• Complex syntax is clumsy to write
• Syntax is a true fact..?!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 5

14.0 Semantic Web Reasoning

• While RDF/S (or at least the DLP bits) form a valid

foundation for OWL, Datalog-style rule languages need other assumptions

– Closed world semantics – Leads to full negation as failure (NAF) – …

• Whereas DLP is only a subset
• f Horn rules

– And if it is interpreted with Herbrand models and CWA, it is no longer suitable for OWL…

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 6

14.0 Semantic Web Reasoning

Is there an overarching logic framework?

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• Hmmmm… this leads to difficult questions…

– If you want to join the debate:

• P

. Patel-Schneider: A Revised Architecture for SemanticWeb

• Reasoning. In PPSWR„05, LNCS, Springer, 2005.
• I. Horrocks, B. Parsia, P

. Patel-Schneider, J. Hendler: Semantic Web Architecture: Stack orTwoTowers? In PPSWR„05, LNCS, Springer, 2005.

– Maybe rules on top of OWL..?!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 7

14.0 Semantic Web Reasoning

• In any case ontologies and logics are powerful
• nce you have them, but how do we get the
• ntologies..?!

– Expert create them like in our Datalog expert systems?

• Do all experts have the same world view? Can we simply

extract their knowledge?

– Create a common backbone and let all individual users build their extensions „as they go‟?

• How to keep the ontology consistent?

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 8

14.0 Semantic Web Reasoning

• Ontologies are extremely powerful and based
• n decidable logics, but…

– Let one little hobbit (read: inconsistency) in and the entire thing comes crashing down…

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 9

14.0 Semantic Web Reasoning

• So,… do we always need a full-fledged ontology
• r are there other possibilities..?!

– Depends on the area: a medical domain ontology should be sound and consistent!!! – But some ontology for document management or organizing your holiday photos..?!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 10

14.0 Semantic Web Reasoning

• Medical Subject Heading

– Controlled vocabulary for indexing journal articles and books in life sciences

• Taxonomy
• Thesaurus

– Maintained by the US National Library of Medicine (NLM)

• Used to classify the MEDLINE/PubMed collections
• Free for use and download

– Proprietary XML or text format – HTML web view

– MeSH is hand-crafted by medical experts

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 11

14.1 The MeSH Ontology

– Currently, MeSH contains around 25,000 subjects (descriptors)

• Accompanied by brief definition and a synonym list
• Descriptors are arranged in a hierarchy and may occur

multiple times in different branches

– Entries in the tree hierarchies are uniquely identified by an alpha-numerical ID system

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 12

14.1 The MeSH Ontology

Top level concepts of caries Caries types

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Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 13

14.1 The MeSH Ontology

Descriptor/heading (concept) Tree ID Definition Synonyms Related concepts Qualifiers (Common Tags)

http://www.nlm.nih.gov/cgi/mesh/2009/MB_cgi?mode=&index=3573&field=all&HM=&II=&PA=&form=&input=

• Qualifiers encode commonly used tags

– Can be added to all other headings – e.g. viral, microbiol, epidemic, etc

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 14

14.1 The MeSH Ontology

Qualifier shortcut

http://www.nlm.nih.gov/cgi/mesh/2009/MB_cgi?mode=&term=MI&field=qual

• By using MeSH, concept maps can be visualized

– Help to quickly assess a given topic

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 15

14.1 The MeSH Ontology

http://www.curehunter.com/public/dictionary.do

Visual dictionary uses co-occurrence of concepts In publications as weight indicator

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 16

14.1 The MeSH Ontology

Typed links between concepts allow for “browsing”

• Also, can be become easily very large and

complex

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 17

14.1 The MeSH Ontology

• MeSH is an example for enriched taxonomy

manually modeled by domain experts

– Expert taxonomies are widely used, however, they come with problems

• Inflexible and rigid structure representing just the authors

view and knowledge

• Hard to change once established, expensive to maintain
• Hierarchical classification often not very practical

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 18

14.1 Hierarchical Expert Ontologies

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• Example hierarchies:

– Periodic Table of Elements, devised in 1869 by Dmitri Mendeleev – Probably the best classification scheme ever – But still, it is and was heavily disputed

• Represented just the knowledge known by Mendeleev
• e.g. initial version was missing noble gases

– …by the way, is Helium really a gas? It becomes solid when cooled…

• Ordering scheme changed from weight to atomic number
• Inserted and added rows / columns, added categories, etc
• etc.

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 19

14.1 Hierarchical Expert Ontologies

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 20

14.1 Hierarchical Expert Ontologies

• Dewey Decimal Classification (DDC)

– Proprietary system for library classification, developed by Melvin Dewey in 1876

• Updated in varying intervals (currently 22nd revision)

– Used by, e.g. Library of Congress – Organizes everything in 10 main classes, which are divided into 10 divisions, which have 10 sections

• A less flexible variant of a system

similar to the tree ID in MeSH

• Strictly hierarchical

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 21

14.1 Hierarchical Expert Ontologies

• Currently, main categories are like

– e.g. 025 is library management

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 22

14.1 Hierarchical Expert Ontologies

000 – Computer science, information, and general works 100 – Philosophy and psychology 200 – Religion 300 – Social sciences 400 – Languages 500 – Science and Mathematics 600 – Technology and applied science 700 – Arts and recreation 800 – Literature 900 – History and geography and biography

• One of the main problems in inflexibility and

inability to further model relationships between entries

– Also, all entries are considered to be co-equal – Until recently, classification for the top concept 200 – Religion looked like this:

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 23

14.1 Hierarchical Expert Ontologies

200: Religion 210 Natural theology 220 Bible 230 Christian theology 240 Christian moral & devotional theology 250 Christian orders & local church 260 Christian social theology 270 Christian church history 280 Christian sects & denominations 290 Other religions

• In the late 90ties, Yahoo! started to classify the

World Wide Web

– For this task, ontology experts where hired to create the classification hierarchy – Often, this classification was quite difficult and awkward – Also, links among entities were necessary between entries

• Strict hierarchical modeling not sensible

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 24

14.1 Hierarchical Expert Ontologies

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14.1 Hierarchical Expert Ontologies

Books are not entertainment, link to Humanities! Booksellers go to professions

• Thus, the transition was made from strictly

hierarchical to linked hierarchical taxonomies

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 26

14.1 Hierarchical Expert Ontologies

• In case of highly unstructured domains, capturing

information in an hierarchical way becomes increasingly difficult

– More and more links, hierarchy less and less useful

• Modeling more and more complex

– Idea: Just omits the hierarchy part and use only links

• Folksonomies
• Automatically generated Lightweight ontologies

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 27

14.1 Hierarchical Expert Ontologies

• Of course the manual creation of ontologies is

an expensive and error-prone process

– Is there a possibility to create ontologies automatically? – It‟s a current research question, but first approaches lead to semi-automatic procedures…

• Basically all approaches mine

statistical connections between terms…

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 28

14.1 Ontology Generation

• A major group for taxonomy creation are

natural language processing approaches

– Gathering simple typical phrases from full texts like “…such as…” or “…like e.g.,…” to find synonyms or subclasses

• The surrounding noun phrases can be

put into some (hierarchical) relationship

• The belief in the correctness of derived classes and/or

hierarchies can be supported by comparison to general

• ntologies like WordNet or counting co-occurrences e.g., in

documents retrieved from Google

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 29

14.1 Ontology Generation

• Or, domain ontologies can be derived relying on

simple statistics, e.g., term co-occurrence

– Extract all salient keywords from each document – Keyword X subsumes keyword Y, if at least 80% of the documents in which Y occurs also contain X, and if X occurrs in more documents than Y – Works only if a sufficiently large number of documents for a certain domain is given

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14.1 Ontology Generation

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• Please note, all these techniques are heuristics…

– The Semantic Web does not really understand the contents of the pages (not yet..?!) – But still,… better than nothing… – Thus, the question arises: Can purely statistical approaches lead to reasonably intelligent results?

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 31

14.1 Ontology Generation

• Just a little anecdote for the start:
• Sir Francis Galton (1822-1911)

– Victorian polymath with special interest in statistics

• Established principles for correlation, deviation, and

regression

– Special interest in research methodologies of eugenics, heredity, genetics, and historiometry

• Claim: Intelligence and leadership properties are inherited,

and only few people possess them. And only those are able to lead and act intelligently.

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 32

14.2 Wisdom of Crowds

• In 1906, he visited a country fair which also featured an ox

weighting betting contest

– An ox is presented, everybody guesses how much the meat after slaughtering will weight, closest bet wins – ~800 people participated

• Farmers, housewives, cattle experts, random

visitors, children, etc

– Galtons claim:

• Experts will win, the other people will just guess nonsense, crowd

consensus will be useless

– Statistical analysis

• Ox weighted 1,198 pounds, average guess of all people was 1,197 pound,

no single guess was better than crowd consensus

– Galtons afterwards:

• “The result seems more creditable to the trustworthiness of a

democratic judgment than one might have expected.”

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 33

14.2 Wisdom of Crowds

• This observation fueled a new research observing

crowd decisions

• Experiments and observed events

– Bean guessing games

• Crowd estimate always very good

– “Who wants to be a millionaire” joker

• 91% success rate vs. 65% expert success

– Predicting outcomes of sport events

• Aggregated bets are usually more accurate than any expert

guess

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 34

14.2 Wisdom of Crowds

• In 1968, the nuclear submarine USS Scorpion

mysteriously disappeared

– Search for the sub was hopeless and was abandoned – However, Dr. John Craven from Navy‟s Special Projects continued the search with his team of mathematicians – Idea:

• Provide all known evidence to a large group
• f peoples and teams

– Submarine experts, salvage experts, oceanologists, mathematicians, ship captains, etc.

• Each team should develop a theory to what happened

and where the submarine was

• Craven combined all theories (wildly diverse) using Bayes‟s theorem
• Submarine wreckage was immediately located 200 meters off the

combined estimated location

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 35

14.2 Wisdom of Crowds

• Observation

– Under certain restrictions large crowds of people are able to perform highly effective decisions

• Far superior to nearly all singular decisions
• Some care and control is required to prevent this approach

from failing miserably

– Further reading:

• James Surowiecki: The

Wisdom of the Crowds, 2004

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 36

14.2 Wisdom of Crowds

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• Group intelligence can effectively be used on

three types of problems

• Cognition Problems

– Judging and Processing Information – Examples

• Guessing, assessing, predicting, modeling,…
• “Who will win Germany‟s Next Top model?”
• “How many beans are in the jar?”
• “How many

VW Golfs will be sold in the next term?”

• “Which movie should one watch who liked Star Wars?”
• “How can the music TOP-100 be classified into genres?”

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 37

14.2 Principles of Group Intelligence

• Coordination Problems

– How to coordinate ones own behavior with all others, knowing that they try to do the same? – Often, coordination problems encode cultural behavior – Examples

• Navigating in heavy traffic
• Using the seats in a lecture hall
• How to figure out a good price

for used items?

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 38

14.2 Principles of Group Intelligence

• Cooperation Problems

– Get self-centered, distrustful people to work together for a greater good – Forming networks of trust without necessity of a central controller – Examples

• The free market
• Paying taxes
• Dealing with pollution

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 39

14.2 Principles of Group Intelligence

• For obtaining „wise group decisions‟,

some key criteria have to be met

• Diversity of opinion

– Each person should have private information, even if it's just an eccentric interpretation of the known facts – Opposing opinions usually increase the accuracy of group decisions by either…

• Canceling out each others mistakes
• Or fostering discussion among group members

– However, it is important that everybody needs an understanding of the problem

• You don‟t need to ask kindergarten children about the potential cause
• f the SARS epidemic
• Diversity means diversity of knowledgeable opinions, not any
• pinions!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 40

14.2 Principles of Group Intelligence

– Groups being to homogeneous will not be able to tap into the power of their numbers

• Too much of just the same comes up
• Independence

– People's opinions should not be determined by the

• pinions of those around them

– The strength of group decision making comes from the diversity of opinion which will be lost, if the group members are not independent – Dominating members will affect the decisions of the other members

• Hype bubbles
• “Monkey see, monkey do”

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 41

14.2 Principles of Group Intelligence

– Especially, information cascades cancel the original diversity by homogenizing the groups opinions and reducing its effectiveness

• People observing others and assuming the observed

decision as one‟s own without further reflection

• People adopting opinions of their superiors
• Often leads to irrational and erratic

herd behavior

– “The Emperor‟s New Clothes” – Telecom stocks – New economy bubble

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14.2 Principles of Group Intelligence

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• Decentralization

– People are able to specialize and draw on local knowledge

• Crucial to tap into peoples tacit knowledge

– Specialization adds more diversity to the group

• Specialist for a certain area provide more valuable input

than non specialists for a special problem

– Using local knowledge allows for optimized solutions for special cases compared to central generic solutions

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 43

14.2 Principles of Group Intelligence

– Example:

• Open source software

– Specialists from all areas work together in decentralized fashion

• Ancient

Athens

– Local law and organization is left to regional magistrates, the central assembly only dealt with “great matters”

• Ant or bee hives

– Insects just act on their own, forming their behavior around local circumstances without central control

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 44

14.2 Principles of Group Intelligence

– Problems with decentralization without further adjustments

• Wasted efforts

– Many try to solve the same problem although it was already solved many times elsewhere

• Crucial information does not propagate among the

groups

– Think of 09/11: most facts for predicting the incident were known, but scattered among all the intelligence agencies

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 45

14.2 Principles of Group Intelligence

• Aggregation

– All individual efforts are lost, if there is no mechanism for turning them into a collective decision

• Bean or Ox Guessing

– Compute the average

• Sport betting

– Aggregate bets in form of betting margins or ratios

• Find lost submarines

– Perform Bayesian aggregation

• Program an operating system

– Integrate code of contributors into the distributions /core /etc.

• Intelligence Services

– Communicate and share information

• …

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 46

14.2 Principles of Group Intelligence

• How can crowd intelligence be harnessed for our

problems (i.e. dealing with knowledge in computer science)?

• Most popular example: Google PageRank!
• Base idea:

– Each link from one page to another is a vote, i.e. the author thinks that the linked page is somewhat important – The more “votes” a page gets, the more important is it – Pages originating from important pages count more than those from unimportant ones – The votes thus propagate along all pages, encoding the common, aggregated belief of importance of all websites given by all website authors!

• Incorporating the crowd knowledge given by page rank into

traditional IR methods made Google the most successful search engine ever!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 47

14.3 Folksonomies

• So, how to use crowds for actually modeling

knowledge?

– Observation around 2004: People enthusiastically enjoyed tagging content on the web – Idea arose that these tags can be used to represent common, shared knowledge similar to ontologies

• The folksonomy was invented!

– Usually credited to Thomas Vander Wal

• Also collaborative tagging, social

classification, social indexing, and social tagging

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 48

14.3 Folksonomies

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• What is tagging?

– A tag is just some word which is assigned to some resource and represents some informal meta-data

• Tags are usually freely chosen by the tagger

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 49

14.3 Folksonomies

ROFL! HöHö TU BS TU BS IZ IfIS TU BS IZ infernal prison

• The tags for a single resource can be represented

by tag could

– The bigger a tag appears, the more often it was used for this resource

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14.3 Folksonomies

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14.3 Folksonomies

TU BS IZ IfIS IfIS TU BS databases RDB1 IfIS lol

• Now, a folksonomy could be build by, e.g. observing

the co-occurrence of tags on resources

TU BS IZ IfIS ROFL! höhö ROFL! lol databases RDB1 prison

• What are folksonomies?

– A folksonomy is a much weaker structure than description logic ontologies

• No taxonomies and usually not even a vocabulary

– A Folksonomy just link some tags to some other tags

• The tags themselves as well as the links do not have to be

necessarily meaningful

– A Folksonomy represent the self-emergent semantics of the collaborative tagging effort

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 52

14.3 Folksonomies

• Formal representation of folksonomies

– A folksonomy T can be represented by a tripartite hypergraph H(T) = <V <V, E>

• Vertices V = A ⋃ C ⋃ I are partitioned into the disjoint sets

– set A of actors/users, – set C of tags/concepts – set I of instances/objects.

• Each tag represents an edge between an actor, tag, and

instance

– E= {{a,c,i} | (a,c,i) ∈ T+

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 53

14.3 Folksonomies

Ontologies are us: A unified model of social networks and semantics, Peter Mika, ISWC 2005 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig

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14.3 Folksonomies

Instances I Concepts C Actors A lol TU BS IfIS H(T) T) = <V, , E>

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• Based on the hypergraph of T, three weighted

bipartite graphs can be generated

– Weight represents how often the two diagrammed vertices of the bipartite graph had been connected by edges in the hypergraph – The graph AC AC of actors and concepts – The graph CI CI of concepts and instances – The graph AI AI of actors and instances – E.g., see definition of AC:

• AC = <A × C, Eac>, Eac = *(a, c) | ∃ i∈I (a,c,i)∈E,

w : E → ℕ, ∀e= (a, c) ∈ Eac, w(e) := |{i : (a,c,i)∈ E+|

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 55

14.3 Folksonomies

• Resulting weighted graph CI

CI

– Also called affiliation graph

• Optionally, a threshold can be applied to remove weak

edges

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 56

14.3 Folksonomies

Instances I Concepts C lol TU BS IfIS 1 1 2 2

• The affiliation graphs can be folded into two

lightweight ontologies

– i.e. for the affiliation graph CI, we can get

• The lightweight ontology of related concepts
• The lightweight ontology of related instances

– Those ontologies represent how strongly its contained entities are related

• Similar to counting co-occurrence

– Mathematically, this can be achieved by multiplying the matrix of the affiliation graph within its inverse, normalizing it with Jaccuard-Coefficient, etc, ….

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 57

14.3 Folksonomies

• Lightweight ontology for concepts in delicious

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 58

14.3 Folksonomies

• An excerpt from the delicious lightweight
• ntology graph

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 59

14.3 Folksonomies

• Some shortcoming

– No controlled vocabulary

• e.g. sciencefiction vs. Science Fiction vs. science_fiction
• LOL, ROFL, knorpelfunky, etc.

– Handling of synonyms and homonyms

• IfIS vs. Institute für Informations Systeme
• Bachelor (degree) vs. Bachelor (unmarried male)

– Questionable semantics of links

• What does a link in a folksonomy mean? Does it mean

something?

• No formal reasoning possible!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 60

14.3 Folksonomies

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07.07.2009 11

• delicous.com

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14.3 Folksonomies

tags

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig

• flickr.com

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14.3 Folksonomies

Tags Picture

Comments

Group

In-Picture-Tags Comments

• Project 10X

– “Industry Roadmap to Web 3.0 and Multibillion Dollar Market Opportunities”

• Vast industrial report on semantic web business future
• i.e. marketing blubber, but still realistic

– Web 2.0: Connecting people – Web 3.0: Connecting knowledge

• Add a “knowledge layer”
• n top of the internet
• Finally realize the Semantic

Web vision

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 63

14.4 The Web 3.0?

http://www.project10x.com/ http://www.isoco.com/pdf/Semantic_Wave_2008-Executive_summary.pdf

• Claim: the support for creating the Web 3.0 is

finally there

– Semantic technologies embraced by many big players

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 64

14.4 The Web 3.0?

• Trends of Web 3.0

– Semantic User Experience

• “Intelligent user interfaces drive gains in user productivity &

satisfaction”

• Personalized, context aware, immersive human-

computer interaction

– Semantic Social Computing

• “Collective knowledge systems become the next killer app”
• Enrich Web 2.0 technologies (blogging, tagging, social

networking, wikis, etc.) with semantic layers

– Tag ontologies, semantic wikis, semantic blogs, etc

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 65

14.4 The Web 3.0?

– Semantic Applications

• “New capabilities, concepts of operation, & improved lifecycle

economics”

• Enhance enterprise-level off the shelf software (e.g. ERP,

CRM, SCM, PLM, HR, etc) with knowledge layers

– Ontology-driven discovery of documents – Policy-driven processes modeled using ontologies – Business logic modeling – Automated agents and advisors

– Semantic Infrastructure

• “Hardware for semantic software”
• New immersive display technologies for better data interaction,

specialized processors, mega-broadband internet, “everything connected”

– Ubiquitous computing

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 66

14.4 The Web 3.0?

SLIDE 12

07.07.2009 12

• The future internet in 2020? Web 4.0?

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 67

14.4 The Web 3.0?

• T

echnologies for Web 3.0?

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 68

14.4 Web 3.0?

• I hope you enjoyed the lecture and learned at

least some interesting stuff…

– Next semester‟s master courses: Multimedia Databases, XML Databases, GIS

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 69

14 Thank You!