This talk introduces the problem of matching web-scale entity graphs, such as multilingual name graphs and social network graphs, to solve difficult problems such as name translation or social id finding. While existing approaches focus on using textual (or phonetic) similarity or Web co-occurrences, this approach combines the strength of the two and significantly outperforms the state-of-the-arts. We present our evaluation results using real-life entity graphs.

1. Information & Database Systems Lab
Entity Graph Mining and Matching
Seung-won Hwang
Associate Professor
Department of Computer Science and Engineering
POSTECH, Korea

2. Mining Human Intelligence from the Web: Click Graph
 Language-agnostic/data-intensive: e.g., arabic Corpus?
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Are q1 and q2 similar?
Are u3 and u4 similar?

3. Mining at Finer Granularity: Named Entity (NE) Graph
 Person name, Place name, Organization name, Product name
 Newspapers, Web sites, TV programs, …
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Apple
MS
tenure
Co-founder
jobs
gates
complicated
Mac

4. Case I: Matching names with twitter accounts [EDBT11]
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5. Case II: Entity Translation [EMNLP10,CIKM11]
 What are the features?
 How are the features combined?
(using translation as an application scenario)
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NE NE
NE
NE
NE
NE NE
NE
NE
NE
NE NE NE NE
NE
NE
English NE
Chinese
Corpus NE
Corpus
NE
NE NE
NE
NE
NE NE
NE NE
Ge=(Ve, Ee) Gc=(Vc, Ec)

6. NE Translation
 Goal
 Finding a NE in source language into its NE in target language
 Ex) “Obama” (English)  “奥巴马” (Chinese)
 Resources: comparable corpora
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NEE NEE
Features Features
Find!!
NEE NEE
Features Features
Xinhua News Agency (English)
NEE NEC
NEE NEC
NEC NEC
NEE NEC
Features Features
NEC NEC NEE NEC
Features Features
Xinhua News Agency (Chinese)

7. NE Translation Similarity Features
 Entity Name Similarity (E): S.Wan [1], L. Haizhou [2], K. Knight [3]
 Pronunciation similarity between named entities
 Ex) “Obama” and “奥巴马” (pronounced Aobama)
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 Entity Context Similarity (EC): M. Diab [4], H. Ji [5], K. Yu [6]
 Contextual word similarity between named entities
 Ex) The president (总统) Obama (奥巴马)
“As president, Obama signed economic stimulus legislation …”
 Relationship Similarity (R): G.-w.You [7]
 Co-occurrence similarity between pairs of named entities
 Ex) (“Jackie Chan”, “Bill Gates” ) vs. (“成龙”, “比尔·盖茨 ”)

8. Motivation
 Taxonomy Table
Entity Relationship
Using Entity Names E [1,2,3] R You [7]
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Using Textual Context EC [4,5,6] ?
Shao [8]
Research questions:
 Why RC is not used?
 Can all four categories combined?

9. In this paper…
 We propose a new NE translation similarity feature
 Relationship Context similarity (RC)
 Contextual word similarity between named entities
 Ex) pair (“Barack”, “Michelle”)  Spouse
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 We propose new holistic approaches
 Combining all E, EC, R, and RC
 We validate our proposed approach using extensive
experiments

10. Our Framework
 We abstract this problem as…
 Graph Matching of two NE relationship graphs extracted from
comparable corpora
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Populate a decision matrix
R, |Ve|-by-|Vc| matrix
NE NE
NE
NE
NE
NE NE
NE
NE
NE
NE NE NE NE
NE
NE
English NE
Chinese
Corpus NE
Corpus
NE
NE NE
NE
NE
NE NE
NE NE
Ge=(Ve, Ee) Gc=(Vc, Ec)

11. Our Framework
 Overview – 3 Steps
 Initialization
奥巴马 成龙
 Construct NE relationship graphs
 Build an initial pairwise similarity matrix R0 Obama .99 .1 .2
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 Use Entity (E) and Entity Context (EC) similarities
Jackie chan .1
 Iterative reinforcement
 Build a final pairwise similarity matrix R∞
 Use Relationship (R) and Relationship Context (RC) similarities
 Matching
 Find 1:1 matching from R∞
奥巴马 成龙
 Build a binary hard decision matrix R*
Obama .99 .1 .2
Jackie chan .99

12. Initialization
 Constructing NE relationship graphs G = (N, E)
 Extract NEs using entity tagger for each document in each corpus
 Regard NEs that appears more than δ times as Nodes
 Connect two Nodes when they co-occur more than δ times
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 Initializing R0
 Computing entity similarity matrix SE
 Use Edit-Distance (ED) between ‘ei’ and Pinyin representation of ‘cj’
 Ex) ED(“Obama”, “奥巴马”) = ED(“Obama”, “Aobama”)
E
ED(ei , PYC j )
S ij 1
Len(ei ) Len( PYC j )

13. Initialization
 Initializing R0
 Computing entity context similarity matrix SEC
 Context word
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ex) “As president, Obama signed economic stimulus legislation …”
 Context window
CW ( NE , d ) {wi l/2 , wi l/2 1 ,..., wi ( NE ),..., wi l/2 1 , wi l/2 }
 Correlation between a NE and a context word : Log-odd ratios

14. Initialization
 Initializing R0
 Computing entity context similarity matrix SEC
 Projected Context Association Vector
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Obama Score 奥巴马 Score
… … … …
President 0.9 … …
… … 总统 0.85
… … … …
Dictionary
USA
…
美
國
(President, 总统)
…
…
president 统总

15. Initialization
 Initializing R0
 Computing entity context similarity matrix SEC
 Context Similarity between ‘ei’ and ‘cj’
 Compute cosine similarity between two vectors
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EC
CAei CAc j
S ij
CAei CAc j
 Merging SE and SEC
 Min-Max normalization in range [0:1]
 Merge
Rij SijE SijEC

16. Reinforcement
 Intuition
 Two NEs with a strong relationship
 Co-occur frequently  have edge
 Share similar context  have similar relationship context
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NE
NE
context
context
X
Y
context context
NE
NE
English NE Graph Chinese NE Graph
1. Align neighbors
using relationship (R) and relationship context (RC) similarity
2. Update the similarity score

17. Reinforcement
 Iterative Approach
Relationship Context (RC) Similarity between
relation pair (i, u) and (j, v)
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Relationship-based Similarity (R & RC) Entity-based Similarity (E & EC)
t RC
Ruv ( Siu , jv )
Rij 1
t
(1 0
) Rij
t
( u ,v ) k B ( i , j , ) 2k
Ordered set of aligned neighbor pairs of (i, j)
at iteration t
Relationship (R) Similarity of
i’s neighbor u and j’s neighbor v

18. Matching
 Finding 1:1 matching using greedy algorithm
 Steps
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1. Find a translation pair with the highest final similarity score
2. Select the pair and remove the corresponding row and column from R∞
3. Repeat 1. and 2. until the similarity score < threshold
R∞

19. Experiments
 Dataset
 English Gigaword Corpus
 Xinhua News Agency 2008.01~2008.12
 100,746 news documents
 Chinese Gigaword Corpus
Information & Database Systems Lab
 Xinhua News Agency 2008.01~2008.12
 88,029 news documents
 Approaches
 EC : consider Entity context similarity feature only
 E : consider Entity name similarity feature only
 Shao (E+EC) : combine Entity name & Entity Context similarities
 You (E+R) : combine Entity name & Relationship similarities
 Ours
 E+EC+R (when ϒ = 0)
 E+EC+R+RC
 Measure
 Precision, Recall, and F1-score

20. Experiments
 Effectiveness of overall framework
 500 person named entities
 Set λ = 0.15
 5-fold cross-validation for threshold parameter learning
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 Other type of NE (100 Location named entities)

21. Directions
 Graph matching
 Graph cleansing [VLDB11]
 Scalable entity search
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US Presidents
Bill Clinton
William J Clinton
George W. Bush
George H.W. Bush
Dubya

22. Thanks
 Question?
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Visit: www.postech.ac.kr/~swhwang for these papers