Research Internships Advanced Research and Modeling Research
Group Slide 2 ADREM What? Research group that deals with
computational aspects of data databases data mining Information
retrieval Slide 3 ADREM Who? DB/DM/IR Floris Geerts Bart Goethals
Martin Theobald Bioinf Kris Laukens Tim Van den Bulcke + Phd
students and postdoctoral researchers http://adrem.ua.ac.be/adrem
Slide 4 Internships What? 2 research internships (15 credits each)
Msc thesis (30 credits). Goal: internships are an initiation to
research and is in collaboration with researchers in ADReM 15
credits is a lot = internship is time consuming! 1 credit = 15 hour
work Balance your course load and internship well. Internships are
not necessarily related to your Msc thesis (but it can) In a Msc
thesis your ability to independently do research plays an important
role. Slide 5 Internships Who? Everyone who follows the research
option in the database Msc program Slide 6 Research In an
internship you need to: 1.Understand a specific problem 2.Implement
an (existing) method for solving the problem 3.Test and evaluate
4.Write a report (Msc thesis: you have to solve the problem as well
by designing new methods) Slide 7 Internships in a company It is
allowed to do a internship in a company but you have to ask
permission Also, you have to find the company yourself and convince
us that there is research involved You cant receive any money from
the company during your internship Slide 8 Databases, data mining,
information retrieval These are not separate research domains The
topics for internships that each of us will present next are
usually on the intersection of these areas. Lets see some example
topics. Slide 9 Bart Goethals Slide 10 Recommender Systems
Implement state of the art recommenders Pattern mining for better
recommendations Interactive Recommendation Explaining
recommendations Test recommenders for real data Slide 11 Visual
Instant Interactive Pattern Mining Study Visualizations enabling
Interactive Pattern Mining Implement and Experiment with novel
instant mining methods Slide 12 Pattern based Clustering Implement
and evaluate different techniques for clustering based pattern
mining, and pattern based clustering Slide 13 Data Mining for
Cleaning Study and experiment with data mining methods for data
cleaning. Slide 14 Martin Theobald Slide 15 Information Extraction
(I): Wikipedia Infoboxes Slide 16 bornOn(Jeff, 09/22/42)
gradFrom(Jeff, Columbia) hasAdvisor(Jeff, Arthur)
hasAdvisor(Surajit, Jeff) knownFor(Jeff, Theory) Information
Extraction (I): Infoboxes YAGO/DBpedia et al. >120 M facts for
YAGO2 (mostly from Wikipedia infoboxes) Slide 17 Information
Extraction (II): Wikipedia Categories Slide 18 ? Slide 19
http://www.mpi-inf.mpg.de/yago-naga/ RDF Knowledge Bases Entity
Max_Planck Apr 23, 1858 Person City Country subclass Location
subclass instanceOf subclass bornOn Max Planck means subclass Oct
4, 1947 diedOn Kiel bornIn Nobel Prize Erwin_Planck FatherOf hasWon
Scientist means Max Karl Ernst Ludwig Planck Physicist instanceOf
subclass Biologist subclass Germany Politician Angela Merkel
Schleswig- Holstein State Angela Dorothea Merkel Oct 23, 1944
diedOn Organization subclass Max_Planck Society instanceOf means
instanceOf subclass means Angela Merkel means citizenOf instanceOf
locatedIn subclass accuracy 95% 3 Mio. entities, 120 Mio. facts 100
relations, 200k classes Slide 20 Linked Open Data As of Sept. 2011:
> 200 sources > 30 billion RDF triples > 400 million links
http://linkeddata.org/ Slide 21 Currently (Sept. 2011) > 5 Mio
owl:sameAs links between DBpedia/YAGO/Freebase As of Sept. 2011:
> 5 million owl:sameAs links between DBpedia/YAGO/Freebase Slide
22 IBM Watson: Deep Question Answering 99 cents got me a 4-pack of
Ytterlig coasters from this Swedish chain This town is known as
"Sin City" & its downtown is "Glitter Gulch" William
Wilkinson's "An Account of the Principalities of Wallachia and
Moldavia" inspired this author's most famous novel As of 2010, this
is the only former Yugoslav republic in the EU YAGO knowledge
back-ends question classification & decomposition
www.ibm.com/innovation/us/watson/index.htm D. Ferrucci et al.:
Building Watson: An Overview of the DeepQA Project. AI Magazine,
Fall 2010. Slide 23 A big US city with two airports, one named
after a World War II hero, and one named after a World War II
battle field? Jeopardy! Slide 24 Structured Knowledge Queries A big
US city with two airports, one named after a World War II hero, and
one named after a World War II battle field? Select Distinct ?c
Where { ?c type City. ?c locatedIn USA. ?a1 type Airport. ?a2 type
Airport. ?a1 locatedIn ?c. ?a2 locatedIn ?c. ?a1 namedAfter ?p. ?p
type WarHero. ?a2 namedAfter ?b. ?b type BattleField. } Use
manually created templates for mapping sentence patterns to
structured queries. Works for factoid and list questions. Slide 25
Mining Rules from RDF Knowledge Bases A-priori-style pre-filtering
of low-support join patterns Dynamic programming ILP algorithm
Learning with constants and type constraints Ground truth for
bornIn (partially known) Facts produced by the rule (only partially
true) Closed World Assumption: strongly penalizes the rule
Specificity: avoid producing overly general rules Use a combination
of statistical measures Confidence instead of Accuracy: do not
penalize the rule for unseen entities Our solution: Overly general
Refine by types Ground truth for livesIn (only partially known)
Knowledge base for livesIn (known positive examples) Facts produced
by the rule (only partially correct) Goal: Inductively learn (soft)
rules: livesIn(x,y) :- bornIn(x,y) G KB R Slide 26 Rule-based
Reasoning (Soft) Deduction Rules vs. (Hard) Consistency Constraints
People may live in more than one place livesIn(x,y) marriedTo(x,z)
livesIn(z,y) livesIn(x,y) hasChild(x,z) livesIn(z,y) People are not
born in different places/on different dates bornIn(x,y) bornIn(x,z)
y=z People are not married to more than one person (at the same
time, in most countries?) marriedTo(x,y,t 1 ) marriedTo(x,z,t 2 )
yz disjoint(t 1,t 2 ) [0.8] [0.5] Slide 27 Probabilistic RDF
Database \/ /\ graduatedFrom (Surajit, Princeton) [0.7]
graduatedFrom (Surajit, Princeton) [0.7] hasAdvisor (Surajit,Jeff
)[0.8] hasAdvisor (Surajit,Jeff )[0.8] worksAt (Jeff,Stanford
)[0.9] worksAt (Jeff,Stanford )[0.9] graduatedFrom (Surajit,
Stanford) [0.6] graduatedFrom (Surajit, Stanford) [0.6] Query
graduatedFrom(Surajit, y) Query graduatedFrom(Surajit, y) CD AB A
(B (C D)) A (B (C D)) graduatedFrom (Surajit, Princeton)
graduatedFrom (Surajit, Princeton) graduatedFrom (Surajit,
Stanford) graduatedFrom (Surajit, Stanford) Q1Q1 Q2Q2 Rules
hasAdvisor(x,y) worksAt(y,z) graduatedFrom(x,z) [0.4]
graduatedFrom(x,y) graduatedFrom(x,z) y=z Rules hasAdvisor(x,y)
worksAt(y,z) graduatedFrom(x,z) [0.4] graduatedFrom(x,y)
graduatedFrom(x,z) y=z Base Facts graduatedFrom(Surajit, Princeton)
[0.7] graduatedFrom(Surajit, Stanford) [0.6] graduatedFrom(David,
Princeton) [0.9] hasAdvisor(Surajit, Jeff) [0.8] hasAdvisor(David,
Jeff) [0.7] worksAt(Jeff, Stanford) [0.9] type(Princeton,
University) [1.0] type(Stanford, University) [1.0] type(Jeff,
Computer_Scientist) [1.0] type(Surajit, Computer_Scientist) [1.0]
type(David, Computer_Scientist) [1.0] Base Facts
graduatedFrom(Surajit, Princeton) [0.7] graduatedFrom(Surajit,
Stanford) [0.6] graduatedFrom(David, Princeton) [0.9]
hasAdvisor(Surajit, Jeff) [0.8] hasAdvisor(David, Jeff) [0.7]
worksAt(Jeff, Stanford) [0.9] type(Princeton, University) [1.0]
type(Stanford, University) [1.0] type(Jeff, Computer_Scientist)
[1.0] type(Surajit, Computer_Scientist) [1.0] type(David,
Computer_Scientist) [1.0] 1-(1-0.72)x(1-0.6) =0.888 0.8x0.9 =0.72
0.7x(1-0.888)=0.078(1-0.7)x0.888=0.266 Slide 28 Temporal Knowledge
Slide 29 0.08 0.12 0.16 0.4 0.6 03 0507 playsFor(Beckham, Real, T 1
) Base Facts Derived Facts 0.2 0.1 0.4 05000207 playsFor(Ronaldo,
Real, T 2 ) 04 0304 07 05 playsFor(Beckham, Real, T 1 )
playsFor(Ronaldo, Real, T 2 ) overlaps(T 1,T 2 ) t 3
teamMates(Beckham, Ronaldo, t 3 ) State Relation teamMates(Beckham,
Ronaldo, T 3 ) Probabilistic-Temporal Consistency Reasoning Slide
30 Topics for Internships & Master Theses Research Internships
Preparation & Integration of Linked Data Sources for Scientific
Experiments (SQL/Java/Python) Mining Association Rules from Linked
Data (Java/C++) Visualization Frontend for Linked Data
(ActionScript & Adobe Flash) Master Theses Implementation of a
distributed rule-based query engine for RDF data (C++ & Message
Passing Interface) Implementation of a distributed factor graph
model for correlated RDF facts (C++ & Message Passing
Interface) Faceted Search and Interactive Browsing for Linked Data
Slide 31 Floris Geerts Slide 32 Find top-3 flights from Edi to NYC
with at most one stop Items: flights Selection criteria: relational
queries Utility function: in terms of price and duration (for
ranking) RDBMS-based recommendation systems 32 Books, music, news,
Web sites, research papers,.. top-k items NY EDI items Top-k item
selection Utility function Selection criteria Slide 33 valid query
relaxation Query relaxation 33 Q(f#, name,type,ticket, time) = DT,
AT, AD, x To ( flight ( f#, EDI, x To, DT, 5/19/2012, AT, AD, Pr )
POI ( name, x To, type, ticket, time) x To = NYC ) Q 1 (f#, name,
type, ticket, time) = DT, AT, AD, u To, w Edi, w NYC,w DD ( flight
( f#, w Edi, x To, DT,w DD, AT,A D, Pr ) x To = w NYC POI( name, u
To, type, ticket, time) w DD =5/19/2012 dist(w NYC,NYC)15 dist(w
Edi,EDI) 15 x To =u To ) E = { EDI,NYC,4/1/2012 }, X = { x To }
There is no direct flight from EDI to NYC Relaxation: cities within
15 miles of EDI or NYC are acceptable Query for 5-day holiday
dist(w DD,5/10/2012 ) 3 Further relaxation: departure dates within
3 days of 5/19/2012 are acceptable Slide 34 Top-k query answering
algorithm on top of RDBMS Query relaxation approaches and query
completion Topics Slide 35 Data quality Detecting and correcting
inconsistencies Finding duplicates Finding most up-to-date
information Slide 36 Semantic errors Yahoo! Finance Nasdaq Days
Range: 93.80-95.71 52wk Range: 25.38-95.71 52 Wk: 25.38-93.72 Days
Range: 93.80-95.71 Slide 37 Instance ambiguity Slide 38 Out-of-Date
Data 4:05 pm 3:57 pm Slide 39 Unit errors 76,821,000 76.82B Slide
40 Fast inconsistency detection Duplication elimination algorithms
Automated repairing algorithms Mining of data quality rules
Topics
