Knowledge-Based Systems and Deductive Databases · •OWL is the language (and semantics) of choice...

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

Knowledge-Based Systems and Deductive Databases

13.1 Generating ontologies 13.2 Wisdom of the crowds 13.3 Folksonomies

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13 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

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13.0 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…

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• 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..?!

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• 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 of Horn rules – And if it is interpreted with

Herbrand models and CWA, it is no longer suitable for OWL…

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• Hmmmm… this leads to difficult questions… – If you want to join the debate: • P. Patel-Schneider: A Revised Architecture for Semantic Web

Reasoning. In PPSWR‘05, LNCS, Springer, 2005. • I. Horrocks, B. Parsia, P. Patel-Schneider, J. Hendler: Semantic

Web Architecture: Stack or Two Towers? In PPSWR‘05, LNCS, Springer, 2005.

– Maybe rules on top of OWL..?!

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

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• Ontologies are extremely powerful and based on decidable logics, but… – Let one little hobbit (read:

inconsistency) in and the entire thing comes crashing down…

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• So,… do we always need a full-fledged ontology or 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..?!

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• 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

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13.1 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

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13.1 The MeSH Ontology 2&,4C<@0#CS?&+'<"(-G4#"4&@0I-

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• Qualifiers encode commonly used tags – Can be added to all other headings – e.g. viral, microbiol, epidemic, etc

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• By using MeSH, concept maps can be visualized – Help to quickly assess a given topic

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• Also, can easily become very large and complex

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• 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

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13.1 Hierarchical Expert Ontologies

• 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.

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• 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

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• Currently, main categories are like

– e.g. 025 is library management

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• 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:

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• 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

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• Thus, the transition was made from strictly hierarchical to linked hierarchical taxonomies

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• 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

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• 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…

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13.1 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 ontologies like WordNet or counting co-occurrences e.g., in documents retrieved from Google

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• 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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• 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?

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• 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.

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13.2 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.”

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• 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

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• 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 of 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

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• 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 • TED Talk at

https://www.ted.com/speakers/james_surowiecki

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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?”

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13.2 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?

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• 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

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• 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

of the SARS epidemic • Diversity means diversity of knowledgeable opinions, not any

opinions!

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– Groups being too 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

opinions 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”

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– 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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• 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

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– 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

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– 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

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• 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 • …

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• 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!

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- FM-

13.3 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

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- FN-

13.3 Folksonomies

• 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

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- FO-

13.3 Folksonomies

UgiHh-

_j_j-

;B-*.-

;B--*.-

Ak-

A:A.-

;B--*.-

Ak-

<":&C"+%-@C<,#"-

• The tags for a single resource can be represented by tag clouds – The bigger a tag appears, the more often it was used

for this resource

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JQ-

13.3 Folksonomies

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JP-

13.3 Folksonomies ;B--*.-

Ak-

A:A.-

A:A.-

;B-*.-

'+0+7+,&,-

U2*P-

A:A.-

%#%-

• Now, a folksonomy could be build by, e.g. observing the co-occurrence of tags on resources

;B-*.- Ak-

A:A.-

UgiHh-

UgiHh-

%#%-

'+0+7+,&,-

U2*P-

• 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

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JD-

13.3 Folksonomies

• Formal representation of folksonomies – A folksonomy ! can be represented by a tripartite

hypergraph "#!$%&%'()%*+,• Vertices (%&%-%.%/%.%0%%are partitioned into the disjoint sets

– set - of actors/users, – set / of tags/concepts – set 0 of instances/objects.

• Each tag represents an edge between an actor, tag, and instance – *&%112)3)45%6%#2)3)4$%7%!5,

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JE-

13.3 Folksonomies

g"0#%#(<&,-+C&-3,X-`-3"<T&'-1#'&%-#:-,#4<+%-"&0$#C=,-+"'-,&1+"54,e->&0&C-Z<=+e--A.9R-DQQJ-

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JF-

13.3 Folksonomies

A",0+"4&,-0,

R#"4&@0,--/, `40#C,-`,

%#%-

;B--*.-

A:A.-

"#!$%&%'()%*+-

• Based on the hypergraph of !, 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 -/ of actors and concepts – The graph%/0%of concepts and instances – The graph -0 of actors and instances – E.g., see definition of AC: • -/%&%'-%8%/)%*23+)%*23%&%1#2)%3$%6%9%470%#2)3)4$7*)%:;%<%*%=%>)%?@&%#2)%3$%7%*23)%;#@$%<&%614%<%#2)3)4$7%*56%%%,

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JJ-

13.3 Folksonomies

• Resulting weighted graph /0,– Also called affiliation graph • Optionally, a threshold can be applied to remove weak

edges

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JK-

13.3 Folksonomies

A",0+"4&,-0,R#"4&@0,--/,

%#%-

;B--*.-

A:A.-

P-

P-

DD

• The affiliation graphs can be folded into two lightweight ontologies – i.e. for the affiliation graph /0, 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, ….

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JM-

13.3 Folksonomies

• Lightweight ontology for concepts in delicious

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JN-

13.3 Folksonomies

• An excerpt from the delicious lightweight ontology graph

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- JO-

13.3 Folksonomies

• Some shortcoming – No controlled vocabulary • e.g. A34@B3@C43D4EB%vs. F34@B3@%G43D4EB vs. A34@B3@HC43D4EB,• IJI)%KJGI)%LBEMN@OPQBLR)%etc.,

– Handling of synonyms and homonyms • 0P0F vs. 0BAD4DQD@%PSM%0BPEMT2D4EBA%FRAD@T@,• U23V@OEM (degree) vs. U23V@OEM (unmarried male)

– Questionable semantics of links • What does a link in a folksonomy mean? Does it mean

something? • No formal reasoning possible!

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KQ-

13.3 Folksonomies

• delicous.com

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KP-

13.3 Folksonomies

0+(,-

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(-

• flickr.com

KD-

13.3 Folksonomies

;+(,-

><403C&-

R#11&"0,-

lC#3@-

A")><403C&);+(,-

R#11&"0,-

• 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”

on top of the internet • Finally realize the Semantic

Web vision

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KE-

13.4 The Web 3.0?

?W@XSS$$$Y@C#m&40PQ^Y4#1S-?W@XSS$$$Y<,#4#Y4#1S@':S.&1+"54[9+6&[DQQN)n^&4356&[,311+C/Y@':-

• Claim: the support for creating the Web 3.0 is finally there – Semantic technologies embraced by many big players

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KF-

13.4 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

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KJ-

13.4 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

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KK-

13.4 The Web 3.0?

• The future internet in 2020? Web 4.0?

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KM-

13.4 The Web 3.0?

• Technologies for Web 3.0?

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KN-

13.4 Web 3.0?

• Question Answering – Basic Techniques – Watson

!"#$%&'(&)*+,&'-./,0&1,-+"'-2&'3456&-2+0+7+,&,-8-9#%:);<%#-*+%=&-8->?<%<@@-9<%%&-8-A:A.-8-;B-*C+3",4?$&<(- KO-

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