Declarative analysis of noisy information networks

Declarative Analysis of NoisyInformation Networks

Walaa Eldin MoustafaGalileo Namata

Amol DeshpandeLise Getoor

University of Maryland

Outline

Motivations/Contributions

Framework

Declarative Language

Implementation

Results

Related and Future Work

Motivation

Motivation

• Users/objects are modeled as nodes, relationships as edges

• The observed networks are noisy and incomplete.– Some users may have more than one account– Communication may contain a lot of spam

• Missing attributes, links, having multiple references to the same entity

• Need to extract underlying information network.

Inference Operations

• Attribute Prediction– To predict values of missing attributes

• Link Prediction– To predict missing links

• Entity Resolution– To predict if two references refer to the same entity

• These prediction tasks can use:– Local node information– Relational information surrounding the node

Attribute Prediction

Automatic RuleRefinement for

Information Extraction

Join Optimization of Information Extraction

Output: Quality Matters!

A Statistical Model for Multilingual Entity

Detection and Tracking

Why Not?

Tracing Lineage Beyond Relational Operators

An Annotation Management System for

Relational Databases

Language Model Based Arabic Word

Segmentation.

DB NL ?

Legend

Use links between nodes (collective attribute prediction) [Sen et al., AI Magazine 2008]

Task: Predict topic of the paper








Why Not?





Segmentation.

DB NL ?

Legend


P1P2








Why Not?





Segmentation.

DB NL ?

Legend


P2 P1

Link Prediction

• Goal: Predict new links• Using local similarity• Using relational similarity [Liben-Nowell et al.,

CIKM 2003]

Divesh Srivastava

Divesh Srivastava

Vladislav Shkapenyuk

Vladislav Shkapenyuk

Nick Koudas

Nick Koudas

Avishek Saha

Avishek Saha

Graham CormodeGraham

CormodeFlip KornFlip Korn

Lukasz GolabLukasz Golab

Theodore Johnson

Theodore Johnson

Entity Resolution

• Goal: to deduce that two references refer to the same entity

• Can be based on node attributes (local)– e.g. string similarity between titles or author

names

• Local information only may not be enough

Jian LiJian Li Jian LiJian Li

Entity Resolution

William RobertsWilliam Roberts

Petre StoicaPetre Stoica

Jian LiJian Li

Prabhu Babu

Prabhu Babu

Amol Deshpande

Amol Deshpande

Samir KhullerSamir

Khuller

Barna SahaBarna Saha

Jian LiJian Li

Use links between the nodes (collective entity resolution) [Bhattacharya et al., TKDD 2007]

Joint Inference

• Each task helps others get better predictions.• How to combine the tasks?

– One after other (pipelined), or interleaved?

• GAIA:– A Java library for applying multiple joint AP, LP, ER

learning and inference tasks: [Namata et al., MLG 2009, Namata et al., KDUD 2009]

– Inference can be pipelined or interleaved.

Our Goal and Contributions

• Motivation: To support declarative network inference

• Desiderata:– User declaratively specifies the prediction features

• Local features• Relational features

– Declaratively specify tasks• Attribute prediction, Link prediction, Entity resolution

– Specify arbitrary interleaving or pipelining– Support for complex prediction functions

Handle all that efficiently

Outline


Framework


Implementation

Results


Unifying Framework

Specify the domainSpecify the domain

Compute featuresCompute features

Make Predictions, and Compute Confidence in the Predictions


Choose Which Predictions to Apply


For attribute prediction, the domain is a subset of the graph nodes.

For link prediction and entity resolution, the domain is a subset of pairs of nodes.

Unifying Framework







Local: word frequency, income, etc.Relational: degree, clustering coeff., no. of neighbors with each attribute value, common neighbors between pairs of nodes, etc.

Unifying Framework







Attribute prediction: the missing attribute

Link prediction: add link or not?

Entity resolution: merge two nodes or not?

Unifying Framework

Specify the DomainSpecify the Domain

Compute FeaturesCompute Features





After predictions are made, the graph changes:Attribute prediction changes local attributes.Link prediction changes the graph links.Entity resolution changes both local attributes and graph links.

Outline


Framework


Implementation

Results


Datalog

• Use Datalog to express:– Domains– Local and relational features

• Extend Datalog with operational semantics (vs. fix-point semantics) to express:– Predictions (in the form of updates)– Iteration

Specifying Features

Degree:Degree(X, COUNT<Y>) :-Edge(X, Y)

Number of Neighbors with attribute ‘A’NumNeighbors(X, COUNT<Y>) :− Edge(X, Y), Node(Y, Att=’A’)

Clustering CoefficientNeighborCluster(X, COUNT<Y,Z>) :−Edge(X,Y), Edge(X,Z), Edge(Y,Z)ClusteringCoeff(X, C) :−NeighborCluster(X,N), Degree(X,D), C=2*N/(D*(D-1))

Jaccard CoefficientIntersectionCount(X, Y, COUNT<Z>) :−Edge(X, Z), Edge(Y, Z)UnionCount(X, Y, D) :−Degree(X,D1), Degree(Y,D2), D=D1+D2-D3, IntersectionCount(X, Y, D3) Jaccard(X, Y, J) :−IntersectionCount(X, Y, N), UnionCount(X, Y, D), J=N/D

Specifying Domains

• Domains are used to restrict the space of computation for the prediction elements.

• Space for this feature is |V|2

Similarity(X, Y, S) :−Node(X, Att=V1), Node(Y, Att=V1), S=f(V1, V2)

• Using this domain the space becomes |E|:DOMAIN D(X,Y) :- Edge(X, Y)

• Other DOMAIN predicates:– Equality– Locality sensitive hashing– String similarity joins– Traverse edges

Feature Vector

• Features of prediction elements are combined in a single predicate to create the feature vector:DOMAIN D(X, Y) :- …{

P1(X, Y, F1) :- ……Pn(X, Y, Fn) :- …Features(X, Y, F1, …, Fn) :- P1(X, Y, F1) , …, Pn(X, Y, Fn)

}

Update Operation

DEFINE Merge(X, Y){

INSERT Edge(X, Z) :- Edge(Y, Z)DELETE Edge(Y, Z)UPDATE Node(X, A=ANew) :- Node(X,A=AX), Node(Y,A=AY), ANew=(AX+AY)/2UPDATE Node(X, B=BNew) :- Node(X,B=BX), Node(X,B=BX), BNew=max(BX,BY)DELETE Node(Y)

}Merge(X, Y) :- Features (X, Y, F1,…,Fn), predict-

ER(F1,…,Fn) = true, confidence-ER(F1,…,Fn) > 0.95

Prediction and Confidence Functions

• The prediction and confidence functions are user defined functions

• Can be based on logistic regression, Bayes classifier, or any other classification algorithm

• The confidence is the class membership value – In logistic regression, the confidence can be the

value of the logistic function– In Bayes classifier, the confidence can be the

posterior probability value

Iteration

• Iteration is supported by ITERATE construct.• Takes the number of iterations as a

parameter, or * to iterate until no more predictions.

• ITERATE (*){

MERGE(X,Y) :-Features (X, Y, F1,…,Fn),predict-ER(F1,…,Fn) = true,confidence-ER(F1,…,Fn) IN TOP

10%

Pipelining

DOMAIN ER(X,Y) :- ….{

ER1(X,Y,F1) :- …ER2(X,Y,F1) :- …Features-ER(X,Y,F1,F2) :- …

}

DOMAIN LP(X,Y) :- ….{

LP1(X,Y,F1) :- …LP2(X,Y,F1) :- …Features-LP(X,Y,F1,F2) :- …

}

ITERATE(*){

INSERT EDGE(X,Y) :- FT-LP(X,Y,F1,F2), predict-LP(X,Y,F1,F2), confidence-LP(X,Y,F1,F2IN TOP 10%

}ITERATE(*){

MERGE(X,Y) :- FT-ER(X,Y,F1,F2), predict-ER(X,Y,F1,F2), confidence-ER(X,Y,F1,F2)IN TOP 10%

}

Interleaving

DOMAIN ER(X,Y) :- ….{

ER1(X,Y,F1) :- …ER2(X,Y,F1) :- …Features-ER(X,Y,F1,F2) :- …

}

DOMAIN LP(X,Y) :- ….{

LP1(X,Y,F1) :- …LP2(X,Y,F1) :- …Features-LP(X,Y,F1,F2) :- …

}

ITERATE(*){

INSERT EDGE(X,Y) :- FT-LP(X,Y,F1,F2), predict-LP(X,Y,F1,F2), confidence-LP(X,Y,F1,F2IN TOP 10%

MERGE(X,Y) :- FT-ER(X,Y,F1,F2), predict-ER(X,Y,F1,F2), confidence-ER(X,Y,F1,F2)IN TOP 10%

}

Outline


Framework


Implementation

Results


Implementation

• Prototype based on Java Berkeley DB• Implemented a query parser, plan generator,

query evaluation engine• Incremental maintenance:

– Aggregate/non-aggregate incremental maintenance

– DOMAIN maintenance

Incremental Maintenance

• Predicates in the program correspond to materialized tables (key/value maps).

• Every set of changes done by AP, LP, or ER are logged into two change tables ΔNodes and ΔEdges.– Insertions: |Record | +1 |– Deletions: |Record | -1 |– Updates: deletion followed by an insertion

• Aggregate maintenance is performed by aggregating the change table then refreshing the old table.

• DOMAIN:

DOMAIN L(X):- Subgoals of L{

P1(X,Y) :- Subgoals of P1}

L(X) :- Subgoals of LP1’(X) :- L(X), Subgoals of P1P1(X) :- L(X) >> Subgoals of P1

Outline


Framework


Implementation

Results


Synthetic Experiements

• Synthetic graphs. Generated using forest fire, and preferential attachment generation models.

• Three tasks:– Attribute Prediction, Link Prediction and Entity Resolution

• Two approaches:– Recomputing features after every iteration– Incremental maintenance

• Varied parameters:– Graph size– Graph density– Confidence threshold (update size)

Changing Graph Size

• Varied the graph size from 20K nodes and 200K edges to 100K nodes and 1M edges

Comparison with Derby

• Compared the evaluation of 4 features: degree, clustering coefficient, common neighbors and Jaccard.

Real-world Experiment

• Real-world PubMed graph– Set of publications from the medical domain, their

abstracts, and citations

• 50,634 publications, 115,323 citation edges• Task: Attribute prediction

– Predict if the paper is categorized as Cognition, Learning, Perception or Thinking

• Choose top 10% predictions after each iteration, for 10 iterations

• Incremental: 28 minutes. Recompute: 42 minutes

Program

DOMAIN Uncommitted(X):-Node(X,Committed=‘no’){

ThinkingNeighbors(X,Count<Y>):- Edge(X,Y), Node(Y,Label=‘Thinking’)PerceptionNeighbors(X,Count<Y>):- Edge(X,Y), Node(Y,Label=‘Perception’)CognitionNeighbors(X,Count<Y>):- Edge(X,Y), Node(Y,Label=‘Cognition’)LearningNeighbors(X,Count<Y>):- Edge(X,Y), Node(Y,Label=‘Learning’)Features-AP(X,A,B,C,D,Abstract):- ThinkingNeighbors(X,A),

PerceptionNeighbors(X,B), CognitionNeighbors(X,C), LearningNeighbors(X,D),Node(X,Abstract, _,_)

}ITERATE(10) {

UPDATE Node(X,_,P,‘yes’):- Features-AP(X,A,B,C,D,Text),P = predict-AP(X,A,B,C,D,Text),confidence-AP(X,A,B,C,D,Text) IN TOP 10%

}

Outline


Framework


Implementation

Results


Related Work

• Dedupalog [Arasu et al., ICDE 2009]:– Datalog-based entity resolution

• User defines hard and soft rules for deduplication• System satisfies hard rules and minimizes violations to

soft rules when deduplicating references

• Swoosh [Benjelloun et al., VLDBJ 2008]:– Generic Entity resolution

• Match function for pairs of nodes (based on a set of features)

• Merge function determines which pairs should be merged

Conclusions and Ongoing Work

• Conclusions:– We built a declarative system to specify graph

inference operations– We implemented the system on top of Berkeley DB

and implemented incremental maintenance techniques

• Future work:– Direct computation of top-k predictions– Multi-query evaluation (especially on graphs)– Employing a graph DB engine (e.g. Neo4j)– Support recursive queries and recursive view

maintenance

References• [Sen et al., AI Magazine 2008]

– Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad: Collective Classification in Network Data. AI Magazine 29(3): 93-106 (2008)

• [Liben-Nowell et al., CIKM 2003]– David Liben-Nowell, Jon M. Kleinberg: The link prediction problem for social networks. CIKM

2003.• [Bhattacharya et al., TKDD 2007]

– I. Bhattacharya and L. Getoor. Collective entity resolution in relational data. ACM TKDD, 1:1–36, 2007.

• [Namata et al., MLG 2009]– G. Namata and L. Getoor: A Pipeline Approach to Graph Identification. MLG Workshop, 2009.

• [Namata et al., KDUD 2009]– G. Namata and L. Getoor: Identifying Graphs From Noisy and Incomplete Data. SIGKDD

Workshop on Knowledge Discovery from Uncertain Data, 2009.• [Arasu et al., ICDE 2009]

– A. Arasu, C. Re, and D. Suciu. Large-scale deduplication with constraints using dedupalog. In ICDE, 2009

• [Benjelloun et al., VLDBJ 2008]– O. Benjelloun, H. Garcia-Molina, D. Menestrina, Q. Su, S. E. Whang,and J. Widom. Swoosh: a

generic approach to entity resolution. The VLDB Journal, 2008.

Documents

Declarative analysis of noisy information networks