Data CleaningJacob Lurye

CS265

KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing. Xu Chu, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, Yin Ye SIGMOD Conference 2015

Let’s talk dirty data

Source: BigDansing: A System for Big Data Cleansing. Zuhair Khayyat, Ihab F. Ilyas, Alekh Jindal, Samuel Madden, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Nan Tang, Si Yin SIGMOD Conference 2015

Source: BigDansing: A System for Big Data Cleansing. Zuhair Khayyat, Ihab F. Ilyas, Alekh Jindal, Samuel Madden, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Nan Tang, Si Yin SIGMOD Conference 2015

Source: BigDansing: A System for Big Data Cleansing. Zuhair Khayyat, Ihab F. Ilyas, Alekh Jindal, Samuel Madden, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Nan Tang, Si Yin SIGMOD Conference 2015

Pr(city = ‘LA’ | zipcode = 90210)

A B C D E F G

Rossi Italy Rome Verona Italian Proto 1.78

Klate S. Africa Pretoria Pirates Afrikaans P. Eliz. 1.69

Pirlo Italy Madrid Juve Italian Flero 1.77

Integrity constraints?

Machine learning?

A B C D E F G

Rossi Italy Rome Verona Italian Proto 1.78

Klate S. Africa Pretoria Pirates Afrikaans P. Eliz. 1.69

Pirlo Italy Madrid Juve Italian Flero 1.77

We need something more...

Enter KATARA...

What is KATARA?

1. Table pattern definition and discovery (using KBs)2. Table pattern validation via crowdsourcing3. Data annotation4. Repair recommendation

What is a knowledge base, and how can it help us clean data?

Resource Description Framework

Resource Description Framework

Resource (and URIs)

Resource Description Framework

Literals

10,500,000

Resource Description Framework

Properties

10,500,000

directorOf

budgetOf

Resource Description Framework

Classes and Instances

Spielberg is an instance of class Director

E.T is an instance of class Sci-Fi Movie which is a subclass of Movie

So how does this all relate back to data cleaning?

Table pattern semantics

Looks a lot like RDF!

Formalizing pattern matching

So what do we do with this formalization?

KBs and table patterns: a few possibilities

Full KB Coverage

KBs and table patterns: a few possibilities

Partial KB Coverage

“Does S. Africa hasCapital Pretoria?”

KBs and table patterns: a few possibilities

Partial KB Coverage

KBs and table patterns: a few possibilities

Not covered by the KB

“What are the possible relationships between Rossi and 1.78?”

How do we actually get knowledge from KBs?

SPARQL: a language for querying KBs

Get types and supertypes of resources with value t [ Ai ]

SPARQL: a language for querying KBs

Q1: get relationships where both attributes are resources

Q2: get relationships with one resource and one literal

So we’ve run our queries — what next?

Evaluating KB types ( Ti ) for table attributes ( Ai )

Evaluating possible column relationships

prob. of any entity appearing in the subject of property P

prob. of any entity being of type T

prob. of an entity being of type T and appearing as subject of P

From PMI we get a measure of semantic coherence

Finally! A metric for scoring table patterns:

Generating the top-k patterns

We have the top-k patterns — now what?

We have the top-k patterns — now what?

Asking the crowd for help — some challenges

vs.

Decomposing table patterns into questions

Column Type Validation

Relationship Validation

So we have our questions — in what order should we ask them?

Maximizing uncertainty reduction

prob. of pattern pi

Pattern validation

First, identify the variable that maximizes expected entropy reduction.

Remove tuples that violate validation, and repeat above until left with one pattern.

Query the crowd for validation.

Wait — what about the “data cleaning” part?

Recognizing erroneous tuples

Just execute a SPARQL query on the tuple.

Fully covered? Otherwise, we need the crowd.

Recognizing erroneous tuples

Table pattern implies this, crowd says yes.

Table pattern implies this, crowd says no. Opportunity to

enrich the KB

1 change 5 changes

Experiments

Setup

(i) RankJoin (KATARA)(ii) Support(iii) MaxLike(iv) PGM

Data:

Algorithms:

Wikitables: 28 tables, avg. 32 tuples

Webtables: 30 tables, avg. 60 tuples

RelationalTables: 3 tables: Person: 317K tuples Soccer: 1625 tuples University: 1357 tuples

Ground truth table patterns

Effectiveness – pattern matching

RankJoin requires fewest top-k patterns to acheivehigh F-measure

Effectiveness – pattern matching

RankJoin requires fewest top-k patterns to acheivehigh F-measure

Effectiveness – pattern matching

RankJoin requires fewest top-k patterns to achievehigh F-measure

Paper’s reason for fast convergence:

DBpedia: 865 typesYago: 317K types

RankJoin outperformsMaxLike and PGM, and is nearly as fast as Support.

All are faster on DBPedia.

Efficiency – pattern matching

Effectiveness – crowdsourcing

10 students validate patternsgiven 5 tuples per question

Effectiveness – crowdsourcing

10 students validate patternsgiven 5 tuples per question

Most improvementfrom first questionalone

Effectiveness – crowdsourcing

Question order matters.

MUVF (most uncertain variable first)

AVF (all variables independent)

Effectiveness – crowdsourcing

Crowd offers substantialannotation error reduction across the board

Effectiveness – repair suggestion

EQ: equivalence-class approach

SCARE: ML approach

Effectiveness – repair suggestion

EQ: equivalence-class approach

SCARE: ML approach

Randomly generated errors?

Only RelationalTables?

Effectiveness – repair suggestions

Repairs are ranked well

Thoughts on the experiments?

Next steps?

Some possible next steps

Cold start – no KBs, pure crowdsourced knowledge bootstrapping

Nth degree relationships – person is from city that is located in state that is located in country

Leveraging multiple KBs at once —DBpedia and Yago, not just either / or