from big text to big knowledge - indian statistical …acmsc/wbda2015/slides/ppt/ppt-isi...from big...

Derry WijayaTom Mitchell

Partha TalukdarMachine And Language Learning (MALL) Lab

SERC & CSA, Indian Institute of Science

Matt Gardner Bryan Kisiel

From Big Text to Big Knowledge

Carnegie Mellon University

IISc Overview

Indian Institute of Science (IISc), Bangalore

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6

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6











Shivani Agarwal





ALGORITHMIC ALGEBRA




K. Gopinath







Bhavana

DATABASE SYSTEMS





THEORY



Satish Govindarajan

6











Shivani Agarwal





ALGORITHMIC ALGEBRA




K. Gopinath







Bhavana

DATABASE SYSTEMS





THEORY

COMPUTER SYSTEMS



Satish Govindarajan

6











Shivani Agarwal





ALGORITHMIC ALGEBRA




K. Gopinath







Bhavana

DATABASE SYSTEMS





THEORY

COMPUTER SYSTEMS

INTELLIGENT SYSTEMS



Satish Govindarajan

Publications (2008-2013)

■ Number of Publications Books and Monographs 11 Book Chapters 25 Journal Publications 151 Conference Publications 260

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Research Programs at IISc� Ph.D. and M.Sc [Engg] � Min. Qualification:

➢ ME / M Tech or BE / B Tech or equivalent degree in any Engineering discipline or

➢ M Sc or equivalent degree in Mathematics, Physics, Statistics, Electronics, Instrumentation or Computer Sciences or

➢ Master’s in Computer Application. � Selection process

➢ Shortlisting (GATE scores) and Interview

Come Join Us!

Back to Text & Knowledge

Thesis

13

Background knowledge is key to Intelligent Decision Making

Thesis

13


?

Thesis

13


inventedCharacter

Explosion of Unstructured Text Data

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14

Sources: http://royal.pingdom.com/2012/01/17/internet-2011-in-numbers/, http://blog.twitter.com/2011/06/200-million-tweets-per-day.html

300 million new websites added in 2011 alone (a 117% growth)

http://royal.pingdom.com/2012/01/17/internet-2011-in-numbers/

http://blog.twitter.com/2011/06/200-million-tweets-per-day.html


500 million Tweets per day (circa Oct 2012)

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500 million Tweets per day (circa Oct 2012)Time to read for one person: 31years

14






500 million Tweets per day (circa Oct 2012)Time to read for one person: 31years

14



Need to harvest knowledge from unstructured text data



What is Knowledge HarvesBng from Unstructured Text?

15


15

... Luke Ravenstahl is the current Mayor of

Pittsburgh ...

... After the death of then-mayor Bob O’Connor, Luke Ravenstahl became the

mayor in September 2006 ...

Document 1 Document 2


15

Person

Luke Ravenstahl

Bob O’Connor


Pittsburgh ...





15

Person

Luke Ravenstahl

Bob O’Connor

LocaBon

PiIsburgh


Pittsburgh ...





15

Person

Luke Ravenstahl

Bob O’Connor

LocaBon

PiIsburgh

MayorOf

MayorOf


Pittsburgh ...





15

Person MayorOf

Bob O’Connor PiIsburgh

Valid UnLl Sep/2006


Pittsburgh ...





15

Person MayorOf

Bob O’Connor PiIsburgh

Valid UnLl Sep/2006

Person MayorOf

Luke Ravenstahl PiIsburgh

Valid From Sep/2006


Pittsburgh ...




Use of Harvested Knowledge: Google Knowledge Graph

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16

Improved Web Search

Experience, facilitated by Harvested Knowledge


16

Improved Web Search


No Structured Information


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Improved Web Search


No Structured Information

http://venturebeat.com/2013/01/22/larry-page-on-googles-knowledge-graph-were-still-at-1-of-where-we-want-to-be/

“We’re sBll at 1 percent of where we should be.” -- Larry Page (Google CEO) on Knowledge Graph [Jan 22, 2013]

New paradigm for Machine Learning:

Never Ending Learning agent

17


Never Ending Learning agentPersistent soSware individual

17


Never Ending Learning agentPersistent soSware individualLearns many funcLons / knowledge types

17


Never Ending Learning agentPersistent soSware individualLearns many funcLons / knowledge typesLearns easier things first, then more difficult

17


Never Ending Learning agentPersistent soSware individualLearns many funcLons / knowledge typesLearns easier things first, then more difficultThe more it learns, the more it can learn next

17


Never Ending Learning agentPersistent soSware individualLearns many funcLons / knowledge typesLearns easier things first, then more difficultThe more it learns, the more it can learn nextLearns from experience, and from advice

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NELL: Never Ending Language Learner

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NELL: Never Ending Language LearnerInputs:

18

NELL: Never Ending Language LearnerInputs:•iniLal ontology

18

NELL: Never Ending Language LearnerInputs:•iniLal ontology •few seed examples of each ontology predicate

18

NELL: Never Ending Language LearnerInputs:•iniLal ontology •few seed examples of each ontology predicate•the web

18

NELL: Never Ending Language LearnerInputs:•iniLal ontology •few seed examples of each ontology predicate•the web•occasional interacLon with human trainers

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The task:

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The task:• run 24x7, forever

18


The task:• run 24x7, forever• each day:

18



• extract more facts from the web

18



• extract more facts from the web • learn to read (perform #1) beIer than yesterday

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NELL Today

19

NELL TodayRunning 24x7, since January, 12, 2010

Result: KB with > 70 million candidate beliefs, growing daily learning to reason, as well as read automaLcally extending its ontology

19

NELL Knowledge Fragment

20

Globe and Mail

Stanley Cup

hockey

NHL

Toronto

CFRB

HockeyTeam

playhasClass

wonToronto

Maple Leafs

home town

city paper league

Sundin

Milson

writer

radio

Air Canada Centre

team stadium

Canada

city stadium

politiciancountry

Miller

airport

member

Toskala

Pearson

Skydome

Connaught

Sunnybrook

hospital

city company

skates helmet

uses equipment

wonRed

Wings

Detroit

hometown

GM

city company

competes with

Toyota

plays in

league

Prius

Corrola

createdHino

acquired

automobile

economic sector

city stadium

climbing

football

uses equipment


20

Globe and Mail

Stanley Cup

hockey

NHL

Toronto

CFRB

HockeyTeam

playhasClass

wonToronto

Maple Leafs

home town

city paper league

Sundin

Milson

writer

radio

Air Canada Centre

team stadium

Canada

city stadium

politiciancountry

Miller

airport

member

Toskala

Pearson

Skydome

Connaught

Sunnybrook

hospital

city company

skates helmet

uses equipment

wonRed

Wings

Detroit

hometown

GM

city company

competes with

Toyota

plays in

league

Prius

Corrola

createdHino

acquired

automobile

economic sector

city stadium

climbing

football

uses equipment

NELL KB: hIp://rtw.ml.cmu.edu TwiIer: @cmunell


20

Globe and Mail

Stanley Cup

hockey

NHL

Toronto

CFRB

HockeyTeam

playhasClass

wonToronto

Maple Leafs

home town

city paper league

Sundin

Milson

writer

radio

Air Canada Centre

team stadium

Canada

city stadium

politiciancountry

Miller

airport

member

Toskala

Pearson

Skydome

Connaught

Sunnybrook

hospital

city company

skates helmet

uses equipment

wonRed

Wings

Detroit

hometown

GM

city company

competes with

Toyota

plays in

league

Prius

Corrola

createdHino

acquired

automobile

economic sector

city stadium

climbing

football

uses equipment


Which relation?


20

Globe and Mail

Stanley Cup

hockey

NHL

Toronto

CFRB

HockeyTeam

playhasClass

wonToronto

Maple Leafs

home town

city paper league

Sundin

Milson

writer

radio

Air Canada Centre

team stadium

Canada

city stadium

politiciancountry

Miller

airport

member

Toskala

Pearson

Skydome

Connaught

Sunnybrook

hospital

city company

skates helmet

uses equipment

wonRed

Wings

Detroit

hometown

GM

city company

competes with

Toyota

plays in

league

Prius

Corrola

createdHino

acquired

automobile

economic sector

city stadium

climbing

football

uses equipment


When?: Temporal Scoping

Which relation?

Other Related Efforts

21

AAAI 2015

NELL’s Growth over Time

24

NELL’s Accuracy over Time

Need knowledge to be …

25


•Available or inferable

25



•Fresh (temporally scoped)

25

KB Inference

If: x1 competes with

(x1,x2)

x2 economic sector (x2,

x3)

x3

Then: economic sector (x1, x3)

PRA: Inference by KB Random Walks [Lao et al, EMNLP 2011]

KB:

Random walk path type:

logistic function for R(x,y) ith feature: probability of arriving at node y starting at node x, and taking a random walk along path type i

model Pr(R(x,y)):

x competes with ? economic

sector y

Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.6 0.20 … … …

Pittsburgh

Pennsylvania

CityInS

tate

CityInState-1

CityInState -1

PhiladelphiaHarisburg

…(14)

U.S.

Feature Value

Logistic Regresssion

Weight

CityLocatedInCountry(Pittsburgh) = U.S. p=0.58

CityLocatedInCountry

DeltaPPG

AtLocation -1 AtLocationAtlanta

DallasTokyo

Japan

CityLocatedInCountry(Pittsburgh) = ?

City

Loca

tedI

nCou

ntry

[Lao et al, EMNLP 2011]

PRA: learned path types

CityLocatedInCountry(city, country):

8.04 cityliesonriver, cityliesonriver-1, citylocatedincountry

5.42 hasofficeincity-1, hasofficeincity, citylocatedincountry 4.98 cityalsoknownas, cityalsoknownas, citylocatedincountry 2.85 citycapitalofcountry,citylocatedincountry-1,citylocatedincountry

2.29 agentactsinlocation-1, agentactsinlocation, citylocatedincountry

1.22 statehascapital-1, statelocatedincountry 0.66 citycapitalofcountry . . . 7 of the 2985 learned paths for CityLocatedInCountry

PRA: Challenges

30

PRA: Challenges

• Works great when the KB graph is well connected

30

PRA: Challenges

• Works great when the KB graph is well connected

‣But, sparsity in the KB graph is the main challenge we wanted to solve!

30

Subject-Verb-Object (SVO) Data

Web

Former President Bill Clinton was born in Hope ...

President Obama was born in Honolulu, while ...

31

Subject-Verb-Object (SVO) Data

Web

Former President Bill Clinton was born in Hope ...

President Obama was born in Honolulu, while ...

SVO“Bill Clinton”, “was born in”, “Hope”“Obama”, “was born in” , “Honolulu”

Extract 600m Subject-Verb-Object (SVO) triples from a parsed web corpus of 230 billion tokens

31

Our Approach: PRA over KB + SVO Graph (Gardner et al., EMNLP 2013)

32

http://talukdar.net/papers/latent_pra_emnlp13.pdf


32

AlexRodriguez(concept)

NY Yankees(concept)

WorldSeries

(concept)

teamPlaysIn

KB Relation Label



32


NY Yankees(concept)

WorldSeries

(concept)

teamPlaysIn

KB Relation Label

“plays for”

“bats for”

AlexRodriguez NY Yankees

mention mention



32


NY Yankees(concept)

WorldSeries

(concept)

teamPlaysIn

KB Relation Label

“plays for”

“bats for”

AlexRodriguez NY Yankees

mention mention

Lexicalized edges can explode number of paths, feature sparsity => Latent PRA


Latent PRA (Discretized)

33


33

Step 1: Embed lexicalized edge labels


33


“plays for”

(A R

od., N

Y Yan

kees

)

“bats for”

(B. Jo

nes,

NY

Mes

)


33


DimensionalityReduction

“plays for”

(A R

od., N

Y Yan

kees

)

“bats for”

(B. Jo

nes,

NY

Mes

)


33



“plays for”

(A R

od., N

Y Yan

kees

)

“bats for”

(B. Jo

nes,

NY

Mes

)

L1 L2 L3

“plays for”

“bats for”

0.9 0.01 -0.3

0.6 0.01 -0.4

LatentDimensions


33



“plays for”

(A R

od., N

Y Yan

kees

)

“bats for”

(B. Jo

nes,

NY

Mes

)

L1 L2 L3

“plays for”

“bats for”

0.9 0.01 -0.3

0.6 0.01 -0.4

LatentDimensions

Discretize


33


“plays for”

“bats for”

+L1 -L3

+L1 -L3


“plays for”

(A R

od., N

Y Yan

kees

)

“bats for”

(B. Jo

nes,

NY

Mes

)

L1 L2 L3

“plays for”

“bats for”

0.9 0.01 -0.3

0.6 0.01 -0.4

LatentDimensions

Discretize


34


34

Step 1I: Use discretized embeddings as edge label


34


“+L1”Alex

Rodriguez NY Yankees

mention mention

“-L3”


34


“+L1”Alex

Rodriguez NY Yankees

mention mention

“-L3”

Example:• “lies on”, “runs through”, “flows through” all get

mapped to same discretized latent dimensions (relevant for cityLiesOnRiver relation)

Latent PRA Experiments

35


35

NELL

MR

R

0

0.225

0.45

0.675

0.9

KB +SVO +Latent (Disc.)

0.90.76

0.64


35

NELL

MR

R

0

0.225

0.45

0.675

0.9


0.90.76

0.64

Freebase

MR

R

0.6

0.613

0.625

0.638

0.65


0.650.64

0.61


35

NELL

MR

R

0

0.225

0.45

0.675

0.9


0.90.76

0.64

Freebase

MR

R

0.6

0.613

0.625

0.638

0.65


0.650.64

0.61

• SVO data helps• Latent features help further

Latent PRA: Summary

36

Latent PRA: Summary

• Makes PRA robust to KB sparsity

36

Latent PRA: Summary


• Increases KB coverage, makes lexicalized edge labels possible

36

Latent PRA: Summary



• Combines global and local statistics

36

Latent PRA: Summary



• Combines global and local statistics

• Brings Ontological and OpenIE together

36

Upcoming EMNLP 2015 papers to overcome sparsity in NELL



•Fresh (temporally scoped) (WSDM ’12, CIKM ‘12)

38

Source of Weak Supervision: Doc PublicaBon Time ~ Fact Time

39


39

President Clinton

December 23, 1995


39

President Clinton

December 23, 1995

AssumpLon: Fact is true at the Lme of document publicaLon

Temporal Profile of Facts

40

Temporal Profile of Facts

Key Idea: Temporally scoping mulLple facts jointly can reduce uncertainty

40

Temporal Constraint Examples

41

Temporal Constraint Examples

Across RelaBons

41

Is CollecBve Inference EffecBve?


CollecBve Temporal Scoping

Independent Temporal Scoping


CollecBve Temporal Scoping

Independent Temporal Scoping

CollecLve temporal scoping improves performance compared to temporally scoping each fact

separately

Learning Temporal Constraints [Talukdar, Wijaya, Mitchell, CIKM 2012]

43


43

-‐ Constraints are automaLcally learned by GraphOrder, proposed graph-‐based SSL algorithm for ordering


43

-‐ Constraints are automaLcally learned by GraphOrder, proposed graph-‐based SSL algorithm for ordering

-‐ Exploits dependency parsed corpus of 16 billion tokens

Broader Interest: Latent Conceptual OrganizaBon in Brains and Text

44

Text Data


44

Text Data

Focus of Past Research


44

Text Data


fMRI Brain State


44

Text Data

View 1View 2

Latent Conceptual OrganizaLon in Humans


fMRI Brain State


44

Text Data

View 1View 2



fMRI Brain State

cat

StarSem 12, COLING 12, CoNLL 13, KDD 14, SDM 14, ACL 14, PLoS ONE]

Current

cat


44

Text Data

View 1View 2


fMRI Brain State

cat

StarSem 12, COLING 12, CoNLL 13, KDD 14, SDM 14, ACL 14, PLoS ONE]

Current

cat

Future: Jointly model both Text and Brain Data

Results Highlight

45

Results Highlight

45

+

Results Highlight

45

+ =

Leila&Wehbe&

[Fedorenko&et&al.&&2012]&

[Wehbe&et&al.,&2014]&

Author's personal copy

E. Fedorenko et al. / Neuropsychologia 50 (2012) 499– 513 505

Fig. 5. Top: A probabilistic overlap map showing in each voxel how many of the 25 individual subjects show a significant (at p < .05, FDR-corrected) effect for the Sen-tences > Nonwords contrast. Bottom: The main functional parcels derived from the probabilistic overlap map using an image parcellation (watershed) algorithm, as describedin more detail in Fedorenko et al. (2010).

nonword-list a memory probe was presented (a word in the sentences and word-listconditions, and a nonword in the jabberwocky and nonword-list conditions), andparticipants had to decide whether the probe was present in the preceding stimulus.As discussed in Fedorenko et al. (2010), the two versions of the task (passive readingvs. reading with a memory probe at the end) produced similar activation patterns;we therefore collapsed across the two subsets of the subjects in our analyses in thatpaper and we do the same here. Each participant completed between 6 and 8 runs(i.e., between 24 and 32 blocks per condition; see Fedorenko et al., 2010, for detailsof the timing).

In Section 3, we report the results of: (a) region-of-interest-based (ROI-based)MVPA analyses on a set of key language-sensitive regions and (b) whole-brainsearchlight-style analyses (Kriegeskorte et al., 2006).

2.1. ROI-based analyses

We chose to use as ROIs for our MVPA analyses the thirteen group-level func-tional parcels6 (Fig. 5, bottom) that were derived from the probabilistic overlapmap for the Sentences > Nonword-lists activations7 (Fig. 5, top), as described inFedorenko et al. (2010). These group-based ROIs represent the locations whereindividual activations tend to be found most consistently across subjects. So, forany given subject, a parcel will include some voxels that respond reliably morestrongly to Sentences than Nonwords, and some voxels that do not show this prop-erty. We chose to use these group-level parcels instead of subject-specific functionalROIs in these analyses for two reasons. First, it has been previously demonstrated

6 These parcels were created in order to systematize and automate the proce-dure for defining subject-specific functional ROIs (fROIs): in particular, for any givenregion, an individual subject’s fROI is defined by intersecting the relevant parcel withthe subject’s thresholded activation map. In other words, these functional parcelsserve as spatial constraints on the selection of subject-specific voxels, akin to usingborders of anatomical regions (see Julian, Fedorenko, & Kanwisher, submitted, foran extension of this method to ventral visual regions).

7 Although these group-level functional parcels were created from the 25 subjectswhose data we examine here, non-independence issues (Vul & Kanwisher, 2009) donot arise in examining the discriminability between word lists and jabberwockysentences because the data from those conditions were not used in creating theparcels. Some non-independence is present when we examine the discriminabilityamong all four conditions (Section 3.1). This non-independence should be taken intoconsideration when interpreting the results from the ROI-based analyses. However,the fact that the results of the whole-brain searchlight analyses, which do not suf-fer from such non-independence problems, look similar to those of the ROI-basedanalyses largely alleviates the concerns.

(Haxby et al., 2001; Kriegeskorte et al., 2006) that even voxels that do not show aparticular functional signature relevant to the to-be-discriminated conditions cancontribute to classification accuracy. For example, Haxby et al. (2001) showed thatremoving voxels from the ventral visual regions that respond most strongly to somevisual category does not strongly affect the ability to discriminate that categoryfrom other categories. Consequently, voxels in the vicinity of language-sensitiveregions in each individual subject may contain information about various aspectsof linguistic knowledge even though they do not show the functional signature oflanguage-sensitive voxels. And second, because we wanted to examine neural activ-ity patterns across all four conditions, we could not use any of the conditions fordefining subject-specific fROIs. (However, in addition to these whole-parcel-basedanalyses, we did conduct one analysis where we looked at the ability of subject-specific functional ROIs (fROIs), defined by the Sentences > Nonword-lists contrast,to discriminate between word lists and jabberwocky sentences. The results of thisanalysis are reported in Appendix A.)

For each condition we divided the data into odd-numbered and even-numberedruns (each subject performed between 6 and 8 runs total). Then, for each subjectand for each ROI, and across the two independent halves of the data, we computedthe within- vs. between-condition spatial correlations for each pair of conditions (asschematically shown in Fig. 4 above), considering all the voxels within the parcel.For example, to see how well the pattern of activity for the Sentences condition isdiscriminated from the pattern of activity for the Word-lists condition, we computed(i) a within-condition correlation value for the Sentences condition by comparing thepattern of activity for the Sentences condition in the odd vs. even runs (all the r valuesare Fisher-transformed); (ii) a within-condition correlation value for the Word-listscondition by comparing the pattern of activity for the Word-lists condition in theodd vs. even runs; and (iii) a between-condition correlation value by comparing thepattern of activation for the Sentences condition in the odd/even runs and for theWord-lists condition in the even/odd runs (these two values are averaged to createone between-condition value). Finally, for each ROI we performed an F-test on thewithin vs. between-condition correlation values across subjects to see whether thewithin-condition values are reliably higher than the between-condition values. Ifso, this would suggest that the distinction between the two conditions in questionis represented in the relevant ROI.

We deviated from Haxby’s analysis strategies in one way. In particular, Haxbyapplied centering to his data by subtracting the mean level of activation of a voxelfrom the activation level for each of the conditions. This is equivalent to consideringthe activation from each condition with respect to a baseline activation level com-puted as the average activation across all conditions, instead of using an independentfixation baseline as we used in our analyses. The centering procedure potentiallyincreases sensitivity of the MVPAs by removing one source of variance from acrossthe voxels and leaving only between-condition differences in play. However, cen-tering also introduces between-condition dependencies in the estimation of thewithin-condition similarity measures, which complicates their interpretation.

a"

b"

Seman(cs"Characters" Syntax"

Dialog" Mo(on"Visual"

[Wehbe et al., PLOS ONE 2014]

From Strings to Things and Beyond


Construct


Construct

New Data

Maintain


Construct

New Data

Maintain Apply

Ongoing Research at MALL Lab• Continuous KB evaluation

• Temporal Micro Reading

• Representation Learning

• Large-scale Learning

• Goal-directed KB expansion

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Prakhar Pjha (IISc)

Arabinda Moni (IISc)

Yogesh D. (BTech, IIT BHU)

Chandrahas (IISc)

Uday Saini (BTech, IIT Ropar)

Madhav N. (IISc)

3 PhD, 3 Masters, 4 Project Assistants, 1 Intern

External CollaboraBon & Support

49

Final Thoughts

50

Final Thoughts

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Unprecedented opportunity to bring world knowledge into AI systems -‐-‐ focus of my research

Big Text Big Knowledge

Thank You! [email protected] www.talukdar.net

Machine Learning

Big Data Processing

Natural Language Processing

Decisions

from big text to big knowledge - indian statistical …acmsc/wbda2015/slides/ppt/ppt-isi...from big...

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