Knowledge Discovery

1
Knowledge Discovery
ACAI 05 ADVANCED COURSE ON KNOWLEDGE DISCOVERY

• Marko Grobelnik, Dunja Mladenic
• J.Stefan Institute
• Slovenia

2
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

3
Why is Knowledge Discovery appropriate for
Semantic Web?

• Idea let a computer search for knowledge whereas
  the humans give just broad directions about where
  and how to search
• Knowledge discovery (KD) could be defined as a
  research area with several subfields Machine
  Learning, Data Mining and Data bases (Mitchell,
  1997 Fayyad et al., 1996 Witten and Frank,
  1999 Hand et al., 2001)
• KD techniques
• mainly about discovering structure in the data
• can serve as one of the key mechanisms for
  structuring knowledge into an ontological
  structure being further used in Knowledge
  management process
• Data and corresponding semantic structures change
  in time
• sub-field of KD called stream mining deals with
  these kinds of problems
• Semantic Web is ultimately concerned with
  real-life data on the web which have exponential
  growth
• scalability is one of the central issues in KD

4
Machine Learning view to Ontology Generation
5
Knowledge Discovery Techniques

• Knowledge discovery technologies can be used to
  support different phases and scenarios for
  ontology generation
• Observations
• Completely automatic construction of ontologies
  is in general not possible for
• theoretical reasons (e.g., information
  bottleneck) and
• practical reasons (e.g., the soft nature of the
  knowledge being conceptualized).
• Human interventions are necessary but costly in
  terms of resources
• therefore the technology should help in
  efficient utilization of human interventions.
• Document databases are the most common data type
  conceptualized in the form of ontologies

6
What is Ontology?

• In most ML contexts we can refer to an ontology
  as being a graph/network structure consisting
  from
• a set of concepts (vertices in a graph)
• each concept Ci is described by a
  membership-function ci(x)
• a set of relations connecting concepts (directed
  edges in a graph)
• each relation Ri is described by a
  membership-function ri(Ci, Cj)
• a set of instances (data records assigned to
  concepts or relations)
• each instance Ii is described by a set of
  features Fi,j

7
We have 7 concepts (C1C7), and 3 relations
(R1R3) each of the concept and relation is
populated by a number of instances (data records)
R1
C2
C1
R3
C4
C3
R3
R2
R1
R3
R2
C5
C7
R1
C6
8
Ontology Definition

• Ontology is defined as a tuple with 5 sets of
  objects
• OntologyltClasses, Relations, Instances,
  Class-Definitions, Relation-Definitionsgt
• in short OltC, R, I, CD, RDgt
• where
• Classes set of labels Ci
• Relations set of labels Ri
• Instances set of instance feature vectors Ii
• Class-Definitions set of class membership
  functions CDi
• Relation-Definitions set of relation membership
  functions RDi
• the idea is to describe ontology learning
  tasks in above terms

9
Ontology Learning

• Ontology learning is a set of tasks based on the
  previous ontology definition
• We define ontology learning tasks in terms of
  mappings between ontology components where some
  of the components are given and some are missing
  and we want to induce the missing ones
• Some typical scenarios
• Inducing classes/Clustering of instances
• C, CDf(I)
• Ontology population
• CD, RDf(C, R, I)
• Ontology generation
• C, R, CD, RDf(I) (hardest task)

10
Representational language

• When performing learning of function f, we need
  to select language for representation of
  membership function f
• Examples
• Linear functions (Support-Vector-Machines, )
• Propositional logic (decision trees, rules, )
• First order logic (Inductive Logic programming)
• by selecting different representation languages
  we decide about
• the power of the descriptions
• complexity of computation

11
Ontology Quality

• For the same set of instances I we can have
  multiple ontologies OltC, R, I, CD, RDgtI
• We need a function q for measuring the quality of
  a given ontology OI
• function q returns numerical value
• the best ontology is the one with the highest
  quality
• Possible evaluation measures
• (1) analysis of statistical properties of
  structured data,
• (2) agreement to the properties derived from
  manually built ontologies,
• (3) optimization of efficiency of the user's
  behaviour when using an ontology,
• (4) using background knowledge, and
• (5) building hybrid measures (combination of
  various approaches).

12
Search for optimal Ontology

• Given set of instances I, we develop a series of
  ontologies
• O1, O2, O3,
• where we have set of transformation operators
  (refinement operators) going from Oi to Oi1
• Good search procedure would select such
  transformations which would lead efficiently
  towards the highest quality q(Oi)
• this formulation is in line with machine
  learning with structured output
• we could use human in the loop by using active
  learning techniques

13
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

14
Large Scale Topic Ontology population
15
Text categorization into large topic ontology

• Categorization of documents into large topic
  ontology is one of the problems in text mining
• needs to be scalable
• e.g. being able to handle DMozs 600K categories
  and 4M docs.
• needs to be accurate
• having accuracy on the level of inter-human
  agreement (60-80)
• needs to be robust
• taking into account nature of web pages
  (typically mixed quality content and often high
  quality context)

16
Approaches for handling hierarchy of categories

• There are several topic ontologies (taxonomies)
  of textual documents
• Yahoo, DMoz, Medline,
• Different people use different approaches
• series of hierarchically organized classifiers
• set of independent classifiers just for leaves
• set of independent classifiers for all nodes

17
Yahoo! topic ontology (taxonomy)

• human constructed hierarchy of Web-documents
• exists in several languages
• easy to access and regularly updated
• captures most of the Web topics
• English version includes over 2M pages
  categorized into 50,000 categories
• contains about 250Mb of HTML-files

18
Document to categorize CFP for CoNLL-2000
19
Some predicted categories
20
System architecture
Feature construction
Web
vectors of n-grams
Subproblem definition Feature selection Classifier
construction
labeled documents (from Yahoo! hierarchy)
unlabeled document
category (label)
??
Document Classifier
21
Content categories

• For each content category generate a separate
  classifier that predicts probability for a new
  document to belong to its category

22
Summary of experimental results on Yahoo!
23
DMoz / ODP is largest topic ontology on the
web 4M sites 68k editors 600k concepts
24
Categorization into DMoz

• On input we take DMoz RDF taxonomy data
• from http//rdf.dmoz.org/
• we preprocess it into efficient binary structure
• next, we build a classification model consisting
  from models for individual categories
• We take hierarchical nature into account
• Using classification model we classify new
  documents into taxonomy
• On output we get for a given document text and
  URL
• Set of most relevant categories from DMoz
• Set of most relevant keywords calculated from
  DMoz category names (segments from the path names)

25
What is used for learning?

• Currently the system uses hierarchical nearest
  neighbor
• in the past we experimented with Naïve Bayes
  for Yahoo taxonomy (http//kt.ijs.si/Dunja/yplanet
  .html)
• heavy feature selection was needed
• we plan to experiment with Support Vector
  Machine (SVM) algorithms
• we plan to use this for ACM KDD Cup 2005
  Challenge
• Scalability is a problem for learning and
  classification when dealing with 600K classes and
  4M documents
• Approaches still needs to be properly evaluated

26
Performance issues

• Preprocessing of DMoz (from RDF to classification
  model) takes approx. 1h
• For classification into the whole DMoz we need
  Win64 with at least 6Gb memory
• subsets of DMoz run on Win32 with 2Gb
• Classification into DMoz is fast
• 20 document classifications per second
• e.g. whole Wikipedia was classified into DMoz in
  several hours

27
Demos

• Demo software for classification into
  http//dmoz.org/Science/ available at
  http//agava.ijs.si/marko/DMozClassifyDemo.zip
  (40Mb)
• includes AVI file with demo movie
• demo runs at http//alchemist.ijs.si11111/
• Demo for classification into the whole DMoz (all
  600K classes) runs at http//alchemist.ijs.si2222
  2/

28
Example classification of URL of a web page
keywords
categories
classification of Hubble telescope web page
29
Example classification of URL text of a web page
30
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

31
Extracting Semantic Graph from text
32
Summarization with semantic graph (Leskovec,
Grobelnik, Milic-Frayling 2005)

• Idea extract semantic network from text
  documents and identify relevant parts of the
  semantic network to represent summary
• Semantic graph representation is used for
  summarization task (DUC Challenge)
• The main research result is the finding that
  topology of extracted semantic graph helps in
  determining importance of the content triples
  (which Subject-Predicate-Object triple is
  relevant)
• joint collaboration with Microsoft Research,
  Cambridge

33
Approach Description

• Approach
• Learn a machine learning model for selecting
  sentences
• Use information about semantic structure of the
  document (concepts and relations among concepts)
• Results are promising
• achieved 70 recall of and 25 precision on
  extracted Subject-Predicate-Object triples on DUC
  (Document understanding conference) data

34
Summarization
Human built document summary
Original Document

• Cracks Appear in U.N. Trade Embargo Against
  Iraq.
• Cracks appeared Tuesday in the U.N. trade
  embargo against Iraq as Saddam Hussein sought to
  circumvent the economic noose around his country.
  Japan, meanwhile, announced it would increase its
  aid to countries hardest hit by enforcing the
  sanctions. Hoping to defuse criticism that it is
  not doing its share to oppose Baghdad, Japan said
  up to 2 billion in aid may be sent to nations
  most affected by the U.N. embargo on Iraq.
  President Bush on Tuesday night promised a joint
  session of Congress and a nationwide radio and
  television audience that Saddam Hussein will
  fail'' to make his conquest of Kuwait permanent.
  America must stand up to aggression, and we
  will,'' said Bush, who added that the U.S.
  military may remain in the Saudi Arabian desert
  indefinitely. I cannot predict just how long it
  will take to convince Iraq to withdraw from
  Kuwait,'' Bush said. More than 150,000 U.S.
  troops have been sent to the Persian Gulf region
  to deter a possible Iraqi invasion of Saudi
  Arabia. Bush's aides said the president would
  follow his address to Congress with a televised
  message for the Iraqi people, declaring the world
  is united against their government's invasion of
  Kuwait. Saddam had offered Bush time on Iraqi TV.
  The Philippines and Namibia, the first of the
  developing nations to respond to an offer Monday
  by Saddam of free oil _ in exchange for sending
  their own tankers to get it _ said no to the
  Iraqi leader. Saddam's offer was seen as a
  none-too-subtle attempt to bypass the U.N.
  embargo, in effect since four days after Iraq's
  Aug. 2 invasion of Kuwait, by getting poor
  countries to dock their tankers in Iraq. But
  according to a State Department survey, Cuba and
  Romania have struck oil deals with Iraq and
  companies elsewhere are trying to continue trade
  with Baghdad, all in defiance of U.N. sanctions.
  Romania denies the allegation. The report, made
  available to The Associated Press, said some
  Eastern European countries also are trying to
  maintain their military sales to Iraq. A
  well-informed source in Tehran told The
  Associated Press that Iran has agreed to an Iraqi
  request to exchange food and medicine for up to
  200,000 barrels of refined oil a day and cash
  payments. There was no official comment from
  Tehran or Baghdad on the reported food-for-oil
  deal. But the source, who requested anonymity,
  said the deal was struck during Iraqi Foreign
  Minister Tariq Aziz's visit Sunday to Tehran, the
  first by a senior Iraqi official since the
  1980-88 gulf war. After the visit, the two
  countries announced they would resume diplomatic
  relations. Well-informed oil industry sources in
  the region, contacted by The AP, said that
  although Iran is a major oil exporter itself, it
  currently has to import about 150,000 barrels of
  refined oil a day for domestic use because of
  damages to refineries in the gulf war. Along
  similar lines, ABC News reported that following
  Aziz's visit, Iraq is apparently prepared to give
  Iran all the oil it wants to make up for the
  damage Iraq inflicted on Iran during their
  conflict. Secretary of State James A. Baker III,
  meanwhile, met in Moscow with Soviet Foreign
  Minister Eduard Shevardnadze, two days after the
  U.S.-Soviet summit that produced a joint demand
  that Iraq withdraw from Kuwait. During the
  summit, Bush encouraged Mikhail Gorbachev to
  withdraw 190 Soviet military specialists from
  Iraq, where they remain to fulfill contracts.
  Shevardnadze told the Soviet parliament Tuesday
  the specialists had not reneged on those
  contracts for fear it would jeopardize the 5,800
  Soviet citizens in Iraq. In his speech, Bush said
  his heart went out to the families of the
  hundreds of Americans held hostage by Iraq, but
  he declared, Our policy cannot change, and it
  will not change. America and the world will not
  be blackmailed.'' The president added Vital
  issues of principle are at stake. Saddam Hussein
  is literally trying to wipe a country off the
  face of the Earth.'' In other developments _A
  U.S. diplomat in Baghdad said Tuesday up to 800
  Americans and Britons will fly out of
  Iraqi-occupied Kuwait this week, most of them
  women and children leaving their husbands behind.
  Saddam has said he is keeping foreign men as
  human shields against attack. On Monday, a
  planeload of 164 Westerners arrived in Baltimore
  from Iraq. Evacuees spoke of food shortages in
  Kuwait, nighttime gunfire and Iraqi roundups of
  young people suspected of involvement in the
  resistance. There is no law and order,'' said
  Thuraya, 19, who would not give her last name.
  A soldier can rape a father's daughter in front
  of him and he can't do anything about it.'' _The
  State Department said Iraq had told U.S.
  officials that American males residing in Iraq
  and Kuwait who were born in Arab countries will
  be allowed to leave. Iraq generally has not let
  American males leave. It was not known how many
  men the Iraqi move could affect. _A Pentagon
  spokesman said some increase in military
  activity'' had been detected inside Iraq near its
  borders with Turkey and Syria. He said there was
  little indication hostilities are imminent.
  Defense Secretary Dick Cheney said the cost of
  the U.S. military buildup in the Middle East was
  rising above the 1 billion-a-month estimate
  generally used by government officials. He said
  the total cost _ if no shooting war breaks out _
  could total 15 billion in the next fiscal year
  beginning Oct. 1. Cheney promised disgruntled
  lawmakers a significant increase'' in help from
  Arab nations and other U.S. allies for Operation
  Desert Shield. Japan, which has been accused of
  responding too slowly to the crisis in the gulf,
  said Tuesday it may give 2 billion to Egypt,
  Jordan and Turkey, hit hardest by the U.N.
  prohibition on trade with Iraq. The pressure
  from abroad is getting so strong,'' said Hiroyasu
  Horio, an official with the Ministry of
  International Trade and Industry. Local news
  reports said the aid would be extended through
  the World Bank and International Monetary Fund,
  and 600 million would be sent as early as
  mid-September. On Friday, Treasury Secretary
  Nicholas Brady visited Tokyo on a world tour
  seeking 10.5 billion to help Egypt, Jordan and
  Turkey. Japan has already promised a 1 billion
  aid package for multinational peacekeeping forces
  in Saudi Arabia, including food, water, vehicles
  and prefabricated housing for non-military uses.
  But critics in the United States have said Japan
  should do more because its economy depends
  heavily on oil from the Middle East. Japan
  imports 99 percent of its oil. Japan's
  constitution bans the use of force in settling
  international disputes and Japanese law restricts
  the military to Japanese territory, except for
  ceremonial occasions. On Monday, Saddam offered
  developing nations free oil if they would send
  their tankers to pick it up. The first two
  countries to respond Tuesday _ the Philippines
  and Namibia _ said no. Manila said it had already
  fulfilled its oil requirements, and Namibia said
  it would not sell its sovereignty'' for Iraqi
  oil. Venezuelan President Carlos Andres Perez
  dismissed Saddam's offer of free oil as a
  propaganda ploy.'' Venezuela, an OPEC member,
  has led a drive among oil-producing nations to
  boost production to make up for the shortfall
  caused by the loss of Iraqi and Kuwaiti oil from
  the world market. Their oil makes up 20 percent
  of the world's oil reserves. Only Saudi Arabia
  has higher reserves. But according to the State
  Department, Cuba, which faces an oil deficit
  because of reduced Soviet deliveries, has
  received a shipment of Iraqi petroleum since U.N.
  sanctions were imposed five weeks ago. And
  Romania, it said, expects to receive oil
  indirectly from Iraq. Romania's ambassador to the
  United States, Virgil Constantinescu, denied that
  claim Tuesday, calling it absolutely false and
  without foundation.''.

Cracks appeared in the U.N. trade embargo against
Iraq. The State Department reports that Cuba and
Romania have struck oil deals with Iraq as others
attempt to trade with Baghdad in defiance of the
sanctions. Iran has agreed to exchange food and
medicine for Iraqi oil. Saddam has offered
developing nations free oil if they send their
tankers to pick it up. Thus far, none has
accepted. Japan, accused of responding too slowly
to the Gulf crisis, has promised 2 billion in
aid to countries hit hardest by the Iraqi trade
embargo. President Bush has promised that
Saddam's aggression will not succeed.
Manual summarization
Creation of semantic network
Semantic net of Subj-Pred-Obj triples
Automatically built document summary (not done
by us)
70 recall, 40 precision of selected triples
according to human generated summaries
Automatic summarization by selecting relevant
triples
Cracks appeared in the U.N. trade embargo against
Iraq. The State Department reports that Cuba and
Romania have struck oil deals with Iraq as others
attempt to trade with Baghdad in defiance of the
sanctions. Iran has agreed to exchange food and
medicine for Iraqi oil. Saddam has offered
developing nations free oil if they send their
tankers to pick it up. Thus far, none has
accepted. Japan, accused of responding too slowly
to the Gulf crisis, has promised 2 billion in
aid to countries hit hardest by the Iraqi trade
embargo. President Bush has promised that
Saddam's aggression will not succeed.
Nat. Lang. Generation
Mapping between graphs learned with ML methods
Semantic net of Subj-Pred-Obj triples
35
Detailed Summarization Procedure

• Linguistic analysis of the text
• - Deep parsing of sentences
• Refinement of the text parse
• - Named-entity consolidation
• Determine that George Bush Bush
• U.S. president
• - Anaphora resolution
• Link pronouns with name-entities
• Extract SubjectPredicateObject triples

Tom Sawyer went to town. He met a friend. Tom was
happy.
Tom Sawyer went to town. He Tom Sawyer met a
friend. Tom Tom Sawyer was happy.
Tom ? go ? town Tom ? meet ? friend Tom ? is ?
happy
Compose a graph from triples Describe each
triple with a set of features for learning Learn
a model to classify triples into the
summary Generate a summary graph
Use summary graph to generate textual document
summary
36
Named entities consolidation

• Consolidating different surface forms that refer
  to the same entities only for names of people,
  places, companies, etc.
• Example
• Hillary Rodham Clinton, Hillary Clinton, Hillary
  Rodham, Mrs. Clinton ? Hillary Clinton
• Heuristic based on the overlap in the surface
  form of name variances
• Accuracy on a subset of the data set 90.

37
Pronomial anaphora resolution

• Link pronouns with their references
• Mary likes Paul. She went to buy him a
  present.
• ? Mary likes Paul. She Mary went to buy him
  Paul a present.
• Method
• restrict to 5 pronouns she, he, who, I, they.
• from the pronoun, traverse the text searching for
  candidate references and assign a score
• the score is based on the distance from the
  pronoun and semantic information
• assume that pronouns refer only to named entities
  found in the document
• Problem
• One passenger in King's car said they had been
  drinking liquor.
• Average accuracy on 1,500 hand labeled pronouns
  81.2

38
Anaphora resolution evaluation
Accuracy on 5 selected 81.2 (55.2 if counting
all pronouns)
39
Extracting triples

• Enhanced parse tree is traversed to identify
  SubjectPredicateObject triples
• Example
• Conservatives embraced the nomination while
  liberals were cautious or hostile
• Resulting triples
• conservative ? embrace ? nomination
• liberal ? is ? cautious
• liberal ? is ? hostile

40
Detailed Summarization Procedure

• Linguistic analysis of the text
• - Deep parsing of sentences
• Refinement of the text parse
• - Named-entity consolidation
• Determine that George Bush Bush
• U.S. president
• - Anaphora resolution
• Link pronouns with name-entities
• Extract Subject Predicate Object triples

Tom Sawyer went to town. He met a friend. Tom was
happy.
Tom Sawyer went to town. He Tom Sawyer met a
friend. Tom Tom Sawyer was happy.
Tom ? go ? town Tom ? meet ? friend Tom ? is ?
happy
Compose a graph from triples Describe each
triple with a set of features for learning Learn
a model to classify triples into the
summary Generate a summary graph
Use summary graph to generate textual document
summary
41
Training of summarization model

• Model ranks Subject-Predicate-Object triples
  according to their importance

Document Semantic network
Summary semantic network
42
Composing a graph

• Graph consists of nodes, referred as concepts,
  which can be subjects or objects and edges which
  are predicates and capture relations among
  concepts.
• Use Word net to identify and compact synonym
  nodes as they correspond to the same concepts.

43
Feature construction

• Features used in the learning process include
  triples described by the following attributes
• Positional information
• Of the sentence from which the triple was derived
  relative to the document text
• Of the triple relative to the beginning of the
  sentence
• Linguistic attributes of the nodes in the triple
  (NLP)
• 18 syntactic attributes
• 100 semantic attributes
• 14 graph attributes PageRank, In/Out Degree,
  reachable neighbours, etc.
• Dataset this yield
• TOTAL of 466 attributes
• On average 72 non-zero attributes per triple.

44
Experiments

• Machine learning with Linear SVM to classify
  triples into relevant or not-relevant for the
  summary
• Positive examples are triples from the sentences
  which were marked as summary sentences by experts
• Negative examples are all other triples
• Data
• 147 documents from the DUC 2002 for which we had
  extracted summaries.
• Evaluation
• Report microaveraged values of precision, recall
  and F1 for the extracted triples using 10-fold
  cross validation.

45
Performance for various attribute sets
46
Performance for various attribute sets
Baseline performance (sentence position
selected terms from the sentence) F132.46 is
lower than in any of the other runs, except for
only linguistic attributes (F130.29). only
linguistic run includes only generic syntactic
and semantic labels - not expected to be good
discriminators on their own.
47
Performance for various attribute sets
Adding generic linguistic attributes reduces
precision Position of triples and sentences ?
P31.05 Adding linguistic attributes ?
P28.67 but consistently increases recall
48
Performance for various attribute sets
Information about the graph structure helps
Position of triples and sentences ? F139.05
Adding structure information ? F143.07
49
Insights
We determine the median and quartiles of the
ranks across 10 runs.

• Most highly ranked features in SVM normal

50
Example of summarization

• Cracks Appear in U.N. Trade Embargo Against
  Iraq.
• Cracks appeared Tuesday in the U.N. trade
  embargo against Iraq as Saddam Hussein sought to
  circumvent the economic noose around his country.
  Japan, meanwhile, announced it would increase its
  aid to countries hardest hit by enforcing the
  sanctions. Hoping to defuse criticism that it is
  not doing its share to oppose Baghdad, Japan said
  up to 2 billion in aid may be sent to nations
  most affected by the U.N. embargo on Iraq.
  President Bush on Tuesday night promised a joint
  session of Congress and a nationwide radio and
  television audience that Saddam Hussein will
  fail'' to make his conquest of Kuwait permanent.
  America must stand up to aggression, and we
  will,'' said Bush, who added that the U.S.
  military may remain in the Saudi Arabian desert
  indefinitely. I cannot predict just how long it
  will take to convince Iraq to withdraw from
  Kuwait,'' Bush said. More than 150,000 U.S.
  troops have been sent to the Persian Gulf region
  to deter a possible Iraqi invasion of Saudi
  Arabia. Bush's aides said the president would
  follow his address to Congress with a televised
  message for the Iraqi people, declaring the world
  is united against their government's invasion of
  Kuwait. Saddam had offered Bush time on Iraqi TV.
  The Philippines and Namibia, the first of the
  developing nations to respond to an offer Monday
  by Saddam of free oil _ in exchange for sending
  their own tankers to get it _ said no to the
  Iraqi leader. Saddam's offer was seen as a
  none-too-subtle attempt to bypass the U.N.
  embargo, in effect since four days after Iraq's
  Aug. 2 invasion of Kuwait, by getting poor
  countries to dock their tankers in Iraq. But
  according to a State Department survey, Cuba and
  Romania have struck oil deals with Iraq and
  companies elsewhere are trying to continue trade
  with Baghdad, all in defiance of U.N. sanctions.
  Romania denies the allegation. The report, made
  available to The Associated Press, said some
  Eastern European countries also are trying to
  maintain their military sales to Iraq. A
  well-informed source in Tehran told The
  Associated Press that Iran has agreed to an Iraqi
  request to exchange food and medicine for up to
  200,000 barrels of refined oil a day and cash
  payments. There was no official comment from
  Tehran or Baghdad on the reported food-for-oil
  deal. But the source, who requested anonymity,
  said the deal was struck during Iraqi Foreign
  Minister Tariq Aziz's visit Sunday to Tehran, the
  first by a senior Iraqi official since the
  1980-88 gulf war. After the visit, the two
  countries announced they would resume diplomatic
  relations. Well-informed oil industry sources in
  the region, contacted by The AP, said that
  although Iran is a major oil exporter itself, it
  currently has to import about 150,000 barrels of
  refined oil a day for domestic use because of
  damages to refineries in the gulf war. Along
  similar lines, ABC News reported that following
  Aziz's visit, Iraq is apparently prepared to give
  Iran all the oil it wants to make up for the
  damage Iraq inflicted on Iran during their
  conflict. Secretary of State James A. Baker III,
  meanwhile, met in Moscow with Soviet Foreign
  Minister Eduard Shevardnadze, two days after the
  U.S.-Soviet summit that produced a joint demand
  that Iraq withdraw from Kuwait. During the
  summit, Bush encouraged Mikhail Gorbachev to
  withdraw 190 Soviet military specialists from
  Iraq, where they remain to fulfill contracts.
  Shevardnadze told the Soviet parliament Tuesday
  the specialists had not reneged on those
  contracts for fear it would jeopardize the 5,800
  Soviet citizens in Iraq. In his speech, Bush said
  his heart went out to the families of the
  hundreds of Americans held hostage by Iraq, but
  he declared, Our policy cannot change, and it
  will not change. America and the world will not
  be blackmailed.'' The president added Vital
  issues of principle are at stake. Saddam Hussein
  is literally trying to wipe a country off the
  face of the Earth.'' In other developments _A
  U.S. diplomat in Baghdad said Tuesday up to 800
  Americans and Britons will fly out of
  Iraqi-occupied Kuwait this week, most of them
  women and children leaving their husbands behind.
  Saddam has said he is keeping foreign men as
  human shields against attack. On Monday, a
  planeload of 164 Westerners arrived in Baltimore
  from Iraq. Evacuees spoke of food shortages in
  Kuwait, nighttime gunfire and Iraqi roundups of
  young people suspected of involvement in the
  resistance. There is no law and order,'' said
  Thuraya, 19, who would not give her last name.
  A soldier can rape a father's daughter in front
  of him and he can't do anything about it.'' _The
  State Department said Iraq had told U.S.
  officials that American males residing in Iraq
  and Kuwait who were born in Arab countries will
  be allowed to leave. Iraq generally has not let
  American males leave. It was not known how many
  men the Iraqi move could affect. _A Pentagon
  spokesman said some increase in military
  activity'' had been detected inside Iraq near its
  borders with Turkey and Syria. He said there was
  little indication hostilities are imminent.
  Defense Secretary Dick Cheney said the cost of
  the U.S. military buildup in the Middle East was
  rising above the 1 billion-a-month estimate
  generally used by government officials. He said
  the total cost _ if no shooting war breaks out _
  could total 15 billion in the next fiscal year
  beginning Oct. 1. Cheney promised disgruntled
  lawmakers a significant increase'' in help from
  Arab nations and other U.S. allies for Operation
  Desert Shield. Japan, which has been accused of
  responding too slowly to the crisis in the gulf,
  said Tuesday it may give 2 billion to Egypt,
  Jordan and Turkey, hit hardest by the U.N.
  prohibition on trade with Iraq. The pressure
  from abroad is getting so strong,'' said Hiroyasu
  Horio, an official with the Ministry of
  International Trade and Industry. Local news
  reports said the aid would be extended through
  the World Bank and International Monetary Fund,
  and 600 million would be sent as early as
  mid-September. On Friday, Treasury Secretary
  Nicholas Brady visited Tokyo on a world tour
  seeking 10.5 billion to help Egypt, Jordan and
  Turkey. Japan has already promised a 1 billion
  aid package for multinational peacekeeping forces
  in Saudi Arabia, including food, water, vehicles
  and prefabricated housing for non-military uses.
  But critics in the United States have said Japan
  should do more because its economy depends
  heavily on oil from the Middle East. Japan
  imports 99 percent of its oil. Japan's
  constitution bans the use of force in settling
  international disputes and Japanese law restricts
  the military to Japanese territory, except for
  ceremonial occasions. On Monday, Saddam offered
  developing nations free oil if they would send
  their tankers to pick it up. The first two
  countries to respond Tuesday _ the Philippines
  and Namibia _ said no. Manila said it had already
  fulfilled its oil requirements, and Namibia said
  it would not sell its sovereignty'' for Iraqi
  oil. Venezuelan President Carlos Andres Perez
  dismissed Saddam's offer of free oil as a
  propaganda ploy.'' Venezuela, an OPEC member,
  has led a drive among oil-producing nations to
  boost production to make up for the shortfall
  caused by the loss of Iraqi and Kuwaiti oil from
  the world market. Their oil makes up 20 percent
  of the world's oil reserves. Only Saudi Arabia
  has higher reserves. But according to the State
  Department, Cuba, which faces an oil deficit
  because of reduced Soviet deliveries, has
  received a shipment of Iraqi petroleum since U.N.
  sanctions were imposed five weeks ago. And
  Romania, it said, expects to receive oil
  indirectly from Iraq. Romania's ambassador to the
  United States, Virgil Constantinescu, denied that
  claim Tuesday, calling it absolutely false and
  without foundation.''.

Human written summary
Cracks appeared in the U.N. trade embargo against
Iraq. The State Department reports that Cuba and
Romania have struck oil deals with Iraq as others
attempt to trade with Baghdad in defiance of the
sanctions. Iran has agreed to exchange food and
medicine for Iraqi oil. Saddam has offered
developing nations free oil if they send their
tankers to pick it up. Thus far, none has
accepted. Japan, accused of responding too slowly
to the Gulf crisis, has promised 2 billion in
aid to countries hit hardest by the Iraqi trade
embargo. President Bush has promised that
Saddam's aggression will not succeed.
7800 chars, 1300 words
51
Full document semantic graph
52
Automatically generated summary graph
53
Findings on summarization with semantic graphs

• Experiments show that attributes that
  characterize the document semantic graph improve
  selection of triples for summarization
• This results need to be verified on additional
  data sets
• Need to perform comparison with additional
  summarization methods
• Explore various strategies for extracting and
  generating summaries based on extracted triples.
• No combination of features that was examined lead
  to good separation of positive and negative
  triples in the feature space
• Opportunity for further investigations and
  improvements.

54
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

55
Active Learning /Dealing with unlabeled data
56
The idea of Active Learning

• The idea of Active Learning is if a student asks
  smart questions, it comes faster to the required
  model of knowledge as by asking random questions
• The goal is to use Active Learning algorithms for
  semiautomatic
• construction of models for labeling data and
• for ontology learning

57
Quick Intro to Active Learning
Data labels
Teacher
passive student

• We use this methods whenever hand-labeled data
  are rare or expensive to obtain
• Interactive method
• Requests only labeling of interesting objects
• Much less human work needed for the same result
  compared to arbitrary labeling examples

query
Teacher
active student
label
Active student asking smart questions
performance
Passive student asking random questions
number of questions
58
Algorithms tested

• Uncertainty sampling (efficient)
• select example closest to the decision hyperplane
  (or the one with classification probability
  closest to P0.5) (Tong Koller 2000 Stanford)
• Maximum margin ratio change
• select example with the largest predicted impact
  on the margin size if selected (Tong Koller
  2000 Stanford)
• Monte Carlo Estimation of Error Reduction
• select example that reinforces our current
  beliefs (Roy McCallum 2001, CMU)
• Random sampling as baseline
• Experimental evaluation (using F1-measure) of the
  four listed approaches shown on three categories
  from Reuters-2000 dataset
• average over 10 random samples of 5000 training
  (out of 500k) and 10k testing (out of
  300k)examples
• the last two methods a rather time consuming,
  thus we run them for including the first 50
  unlabeled examples
• experiments show that active learning is
  especially useful for unbalanced data

59
Category with balanced class distribution having
47 of positive examples Limited advantage over
random sampling
60
Category with fairly unbalanced class
distribution having 20 of positive examples Best
performance with Uncertainty and MarginRatio,
Uncertainty is simpler and much more efficient
61
Category with very unbalanced class distribution
having 2.7 of positive examples Uncertainty
seems to outperform MarginRatio
62
Illustration of Active learning

• starting with one labeled example from each class
  (red and blue)
• select one example for labeling (green circle)
• request label and add re-generate the model using
  the extended labeled data
• Illustration of linear SVM model using
• arbitrary selection of unlabeled examples
  (random)
• active learning selecting the most uncertain
  examples (closest to the decision hyperplane)

63
Uncertainty sampling of unlabeled example
64
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65
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

66
Methods Addressing Different Aspects of Ontology
Construction
67
Methods addressing different aspects of ontology
construction

• Collecting data
• focused crawling with Google and DMoz in the loop
• Dealing with different natural languages
• map the documents into a language-independent
  semantic-space
• Going directly from the data
• semi-automatic creation of an ontology directly
  from the data under predefined conditions/scenario
  s
• Annotation of text

68
Focused Crawler

• Focused crawler which finds in a relatively short
  time web pages related to the given web page
• The solution uses DMoz topic ontology to get
  content context, and Google to get web linkage
  context
• the main idea is to use browse web-graph as
  bi-directional graph using link query in
  Google
• Algorithm
• For efficient initial set of candidate pages we
  use Google and DMoz
• From initial set pages are crawled in
  breadth-first fashion
• priority in the crawler-queue is given to more
  similar pages
• after some stopping condition is met, the
  crawler returns the list of candidate web pages
• Usage serves as a technique for collecting the
  data for the next stages of data processing such
  as building and populating ontologies for the
  Semantic Web, improved knowledge access

69
Example Focused Crawl

• Focused crawl for the BT home page
  (http//www.bt.com)
• 1. www.bt.co.uk/ - BT
• 2. www.yell.com/ucs/HomePageAction.do - UK's
  local search engine
• 3. www.att.com/ - ATT The World's Networking
  Company
• 4. www.cisco.com/ - Cisco Systems, Inc
• 5. www.microsoft.com/ - Microsoft Corporation
• 6. www.bbc.co.uk/ - BBC
• 7. www.hp.com/ - HP United States
• 8. www.ntl.com/ - Broadband cable internet access
• 9. www.telekom.de/ - Deutsche Telekom
• 10. www.epsrc.ac.uk/ - EPSRC
• 11. www.cw.com/ - Cable Wireless
• 12. www.royalmail.com/ - Royal Mail
• 13. www.ericsson.com/ - Ericsson
• 14. www.bp.com/home.do?categoryId1 - BP Global
• 15. www.telewest.co.uk/ - Telewest Broadband PLC
• 16. www.verizon.com/ - Verizon
• 17. www.nokia.com/ - Nokia
• 18. www.bt.com/at_home.jsp - BT.com At Home

70
Language-independent document representation

• From aligned corpora we learn mappings between
  documents into language independent
  representation using Kernel Canonical
  Correlation Analysis method
• such representation could be used for
  multilingual classification, multilingual IR,
• On-going work on learning mappings between all
  European languages using CELEX corpus of European
  legislation in 21 lang

71
Two views of the same data find the direction
with maximal correlation

• View 1

• View 2

72
Corelation 0.17

• View 1

View 2
73
Correlation 0.44

• View 1

View 2
74
Correlation 0.97

• View 1

View 2
75
Correlated directions found with KCCA when
applied to financial news articles
76
Modelling directly from the data getting
semantic classes with LSI
77
Visualization of 6FP IST project (English)
78
Modeling relationships between companies from the
news
79
Annotation of text

• Annotation based on examples
• Annotation using clustering
• Annotation based on thesaurus

80
Annotate text based on examples

• Problem Annotation of text by assigning
  predefined labels to text fragments
• Given examples of annotated text fragments
• learn annotation rules from already annotated
  documents (.xml, ...) similar to learning IE
• learn to classify sentences into semantic roles

81
Annotate text using clustering

• Problem Annotation of text by finding labels and
  assigning them to to text fragments
• Given text to annotate
• split documents into sentences, represent each
  sentence as word-vector
• cluster sentences and label them by the most
  characteristic words from the sentences
• e.g., using local frequency of words, clustering
  with SOM and using neural network weights of
  words

82
Annotate text based on thesaurus

• Problem Annotation of text by finding labels and
  assigning them to to text fragments
• Given text to annotate, thesaurus
• a) apply NLP on text to find noun-groups and map
  them upon concepts of (medical) thesaurus
• b) split document into sentences, cluster them
  and map clusters upon concepts of a general
  thesaurus (WordNet)
• the concepts are used as semantic labels (XML
  tags) for annotating documents

83
Ontology evaluation directions

• Analysis of information-theoretic properties of
  structured data instances
• Measure of the agreement to the characteristics
  derived from manually built ontologies
• Optimization of efficiency of the user's
  behaviour when using an ontology (e.g.,
  minimizing the number of user clicks)

84
Contents

• Knowledge Discovery
• Large Scale Topic Ontology population
• Extraction of Semantic Networks from Text
• Active Learning for efficient using of human
  interventions
• Methods Addressing Different Aspects of Ontology
  Construction
• Final Remarks

85
Ontology Learning Challenge

• Academic challenge on DMoz data (Science part)
  for 3 tasks
• Taxonomy Population
• Given taxonomy with documents, the task is to
  classify new documents into taxonomic categories
• Naming Categories
• Given taxonomic categories with documents, the
  task is to (semi)automatically propose names for
  categories
• Constructing Taxonomy from Documents
• Given a set of documents, the task is to
  (semi)automatically propose taxonomic structure
• The goal is to model human skills when dealing
  with large amounts of data
• Data
• DMoz/Science (10k concepts, 100k instances)
• Tourist ontology (from KU) (70 concepts, 1000
  instances)
• The challenge will be funded through PASCAL
  Network of Excellence European project
  (http//www.pascal-network.org/)

86
Ideas / Future plans (1)

• DMoz categories as standard web meta-data
  dictionary
• the idea is to use DMoz categories/keywords as a
  standardized dictionary for meta-data labeling of
  general Web pages
• because of dynamic and adaptive nature of DMoz
  categorization (reflecting all major topics on
  the web) this could be interesting as a baseline
  for semantic web style annotation
• e.g. could be deployed as a tool for
  (semi)automatic generation of ltMETAgt tags for web
  pages

87
Ideas / Future plans (2)

• DMoz classifier as an annotation tool
• the idea is to use DMoz-classifier tool for
  meta-data (keyword) generation
• some other popular databases (e.g. Wikipedia)
  could have attached automatically generated DMoz
  categories
• could be accessible as a web service (e.g. SOAP
  interface)

88
Ideas / Future plans (3)

• DMoz Visualizer
• the idea is to create a tool for visualization
  and browsing through DMoz structure
• browsing tools could combine other public and
  commercial sources (such as Wikipedia, Google,
  Amazon, eBay, )
• could appear as e.g. web-browser toolbar

89
Ideas / Future plans (4)

• Analysis of DMoz Dynamics
• Future research plan is to model dynamics of DMoz
  taxonomy based on data from DMoz Archive
  (http//rdf.dmoz.org/rdf/archive/)
• the idea is to model decision process when and
  how the editors decide to split the category
  nodes
• currently the repository includes 120 snapshots
  of DMoz from year 2000 on

90
Ideas / Future plans (5)

• Focused crawling for DMoz
• the idea is to use focused crawler for proposing
  new web sites for particular categories (as
  editorial tool)
• at JSI we developed focused crawler for fast and
  efficient crawling for a focused content, can be
  further extended
• to use Google and DMoz in the loop
• to use user-hints (positive negative examples
  of content pages)
• based on Corpus-Builder project at CMU
• http//www-2.cs.cmu.edu/afs/cs/project/theo-4/text
  -learning/www/corpusbuilder/

91
Ideas / Future plans (6)

• Classification of non English documents
• we use string kernels for avoiding problems with
  morphology
• submitted paper at ECML/PKDD2005 (Fortuna
  Mladenic) for classification into major Slovenian
  and Croatian taxonomies
• we plan to use use Canonical Correlation
  Analysis (CCA) for efficient identification of
  similar content written in different languages

92
Text-Garden software library(in development over
the last 5 years)
93
Text-Garden data

• Set of C classes for industrial strength text
  mining problem solving
• Currently organized in 50 command line utilities
  covering
• Machine learning/Data mining on text
• Web related functionality
• Profiling, Visualization,
• Currently works on Windows, to be ported to Linux

94
Text Garden Architecture of clustering,
visualization, classification
95
Text Garden Web sitewww.textmining.net