Mining Academic Community

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Mining Academic Community

• Jan-Ming Ho
• hohoiis.sinica.edu.tw
• Computer System and Communication Lab
• Institute of Information Science
• Academia Sinica

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What is Community?

• In Graph Theory
• densely connected groups of vertices, with
  sparser connection between groups
• In Social Network Analysis
• groups of entities that share similar properties
  or connect to each other via certain relations
• A social network is a structure made up of nodes,
  representing entities from different conceptual
  groups, that are linked with different types of
  relations

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Why is Community Important?

• Interesting data with community structure
• researcher collaboration, friendship network,
  WWW, Massive Multi-player on-line gaming,
  electronic communications.
• Groups of web pages that link to more web pages
  in the community than pages outside correspond to
  web pages on related topics
• Groups in social networks correspond to social
  communities, which can be used to understand
  organizational structure, academic collaboration,
  shared interests and affinities, etc.

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Motivation

• Understand the research network between authors,
  conferences and topics (rank entities by
  relevance for given entities)
• Find and justifiably recommend research
  collaborators for given authors
• Explore the academic social network
• Find out most important papers, researchers and
  venues for a given topic

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Related Systems

• Many digital library systems exist
• ACM Digital Library
• IEEExplorer
• DBLP
• Citeseer
• Libra
• DBConnect
• Problems
• The coverage of dataset is not large enough
• Name ambiguous problem exists in
• Web pages
• Citation records

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Libra Academic Search

• http//libra.msra.cn
• Free computer science bibliography search engine
• A test-bed for object-level vertical search
  research
• Currently the following types of paper-related
  objects can be searched
• Papers, Authors, Conferences, Journals, Research
  Communities

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DBconnect Conference
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DBconnect Topic
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DBconnect Author
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ZoomInfo
(1) People Directory (2) Developer Tools (3)
Social Network, Profile Statistics, Employment
History (4) Ability to identify ambiguous?! Ex.
Can get 21 different people called Bing Liu
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ArnetMiner
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Our goal

• Developing an automatic system to
• Explore the academic social network
• Find out most important papers, researchers and
  venues for a given topic
• Provide solutions for existent problems
• Collecting larger citation datasets
• Retrieving data from web pages
• Publication list finder
• Extracting citation strings from web pages
• Citation parser
• Multilingual data sources
• Chinese and English corpuses
• Name dissemination mechanism in
• Web pages
• Citation records

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Our contributions

• Kai-Hsiang Yang, Kun-Yan Chiou, Hahn-Ming Lee,
  and Jan-Ming Ho, "Web Appearance Disambiguation
  of Personal Names Based on Network Motif," in the
  2006 IEEE/WIC/ACM International Conference on Web
  Intelligence (WI 2006), Hong Kong, Dec. 18-22,
  2006
• Kai-Hsiang Yang, Jen-Ming Chung and Jan-Ming Ho,
  "PLF A Publication List Web Page Finder for
  Researchers," in Proceedings of the 2007
  IEEE/WIC/ACM International Conference on Web
  Intelligence (WI 2007), Silicon Valley, USA, Nov.
  2-5, 2007
• Kai-Hsiang Yang, Wei-Da Chen, Hahn-Ming Lee and
  Jan-Ming Ho, "Mining Translations of Chinese Name
  from Web Corpora by Using Query Expansion
  Technique and Support Vector Machine," in
  Proceedings of the 2007 IEEE/WIC/ACM
  International Conference on Web Intelligence (WI
  2007), Silicon Valley, USA, Nov. 2-5, 2007
• Chia-Ching Chou, Kai-Hsiang Yang and Hahn-Ming
  Lee, "AEFS Authoritative Expert Finding System
  Based on a Language Model and Social Network
  Analysis," in Proceedings of the 12th Conference
  on Artificial Intelligence and Applications
  (TAAI2007), Nov 16-17, 2007
• Chien-Chih Chen, Kai-Hsiang Yang and Jan-Ming Ho,
  "BibPro A Citation Parser Based on Sequence
  Alignment Techniques," will appear in Proceedings
  of the IEEE 22nd International Conference on
  Advanced Information Networking and Applications
  (AINA-08)

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PLF A Publication List Web Page Finder for
Researchers
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Agenda

• Introduction
• Publication List Web Page Finder, PLF
• Performance Evaluation
• Conclusion, Future Work

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Overview of a Publication List Web Page

• Keep abreast of state-of-the-art research
• Contains citations not found elsewhere.
• May provide some reference materials, such as
  slides and talks.
• Challenges
• How to find the publication list web pages
• Only with the given name .
• Various versions or Multiple copies
• An author may have many affiliations.
• Name ambiguity problem
• E.g., Dr. Bing Liu, we found that 26 people share
  the same name by inquiring to ZoomInfo (people
  search engine).

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Problem
Publication List Web Page?
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Definition of Publication List
Affiliated Personal Publication List Web Page
(APPL) a web page belongs to the affiliated web
site of a specific person with the given name.
Affiliation Institute of Information Science,
Academia Sinica
citation string
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Agenda

• Introduction
• Publication List Web Page Finder, PLF
• Performance Evaluation
• Conclusion, Future Work

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Process Flow
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Basic Concept
A publication list web page may
contain many citation strings
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Agenda

• Introduction
• Publication List Web Page Finder, PLF
• Performance Evaluation
• Conclusion, Future Work

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Dataset

• Scenario
• Seminar members have usually published major
  research works
• We randomly collected 200 names from the WWW 06
  Conference Committee website

APPL Types APPL people population
others 0 22 11
single-group 1 120 60
multi-group 2 35 17.5
3 16 8
4 7 3.5
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Experiment Evaluation

• Evaluation metrics
• We consider the top-5 results derived by each
  link and focus on the top-5 recall metric, which
  is calculated by

Notation Definition
Ra the number of publication list web pages belonging to researchers listed in the dataset
R the number of publication list web pages contained in the top-5 results
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Parameter Analysis for Single-Group
(m, n)
(m, n)
(a) Fixed n mixed with different scale m
(b) Fixed m mixed with different scale n

• Figure (a)
• When m increases, the recall rate also
  increases.
• Figure (b)
• System performance may be constrained by m.

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Parameter Analysis for Multi-Group
(a) Fixed n mixed with different scale m
(b) Fixed m mixed with different scale n

• Figure (a)
• It is clear that the performance when m 40 is
  always better than the other settings.
• Figure (b)
• The best performance (top-5 recall is 70)
  occurs when n 75.

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Performance Evaluations
(a)Performance of approaches in single-group
(b)Performance of different ways in multi-group

1. The parameter m has a strong influence on the
   systems performance for example, an oversized m
   may degrade the performance.
2. The parameter n has little influence on the
   systems performance.
3. The PLF system outperforms the other two
   approaches on both the single-group and the
   multi-group datasets.

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Conclusion

• We have defined the problem of finding the
  publication list web pages of a researcher, and
  proposed PLF system
• Ongoing work
• Name ambiguity problem
• How to merge the multiple publication list web
  pages for a specific person into a single page.

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Discussion Name Ambiguity Problem

• Scenario
• We take the name Bing Liu
• as an example
• Analyze manually
• Observation
• Citation Count
• Name translation problem
• Partial matching problem

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Extracting Citation Strings from Web Pages
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Extract Citation Records
Extract
Web Page
Structured Data
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Challenges

• The formats of publication list web pages vary
• There are no fixed syntactic rules for parsing
  citation records
• Hence, We can not apply simple rules to extract
  citation records automatically

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Challenges Complex Layouts of Publication List
Pages
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Ideas

• The semantic structure of web pages is organized
  by visual arrangement.
• We can utilize semi-structure information (visual
  ) of web pages to help extraction task.
• With hierarchical structure and geometric
  information, DOM tree is not only a great
  structure to present Web pages, but also very
  helpful for visual pattern analysis.

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DOM Tree Presentation of Web page
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Architecture of Citation Extraction System
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Modules of Citation Extraction System

• Common Style Finder
• find out all common style patterns for each level
  of granularity in web pages
• Citation Extractor
• explore data regions with common style patterns
• distill extraction rules from those data regions
• rank extraction patterns based on a normal word
  count distribution probability

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BibPro A Citation Parser based on Sequence
Alignment Techniques
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System Goal
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Basic Idea(1/2)

• Encode citation to protein sequence
• Only keep the citation style information
• order of fields
• field separators

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Basic Idea(2/2)

• To determine citation style by the order of
  punctuation marks and reserved words

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How to encode citation to protein sequence?

• Keep the citation style information
• Which field should be included? (only can use 23
  symbol)
• Which punctuation are used to separate fields?
• By observing different citation styles, we define
  an encode table to translate each token of
  citation to an amino acid symbol

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Encode Table
A Author T Title L Journal F Volumn value W Issue value H Page value M Month Y Year X noise (unrecognized token) S Issue key. e.g. no, No P Page key. e.g. pp, page V Volume key. e.g. Vol, vo N numeral Q _at_ \ _ / ! ? ? I ( lt ? K ) gt ? D . G " R , C - E ' Z B blank
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How to using protein sequence to extract metadata?

• Transform extraction problem to sequence
  alignment problem
• Form translation
• Unknown Answer
• BASE FORM
• ALIGN FORM
• INDEX FORM
• Known Answer
• RESULT FORM
• STYLE FORM
• INDEX FORM

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RESULT FORM (Known Answer)
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BASE FORM (Unknow Answer)
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System Structure

• System PreProcess
• (Template Generating System)
• Citation Crawler
• Template Builder
• Online Parsing
• (Parsing System)
• Template Matching
• Metadata Extraction

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Citation Crawler
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BLAST-powered Template Matching
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Evaluation for CiteSeer DataSet

• Consider the inconsistency between the Citation
  String and BibTex file(metadata)
• Old Measurement
• New Measurement

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Definition

• Tokenparsedfield denote tokens that appear in
  the parsed subfield
• Tokenquery citation denote tokens that appear in
  the query citation string
• TokenBibTex field denote tokens that appear in
  the specific subfield in the BibTex file
• TokenBibTex denote all tokens that appear in
  the BibTex file
• These tokens don' t include punctuation

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Compare with ParaCite

• DataSet
• Collected from CiteSeer
• Training Set 2416
• Testing Set 4131
• ParaCite
• Using default template Database
• add template to its database isnt easy
• Test Testing Set
• Our System
• Using training template Database (Training Set)
• Test Testing Set

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Experimental Results
ParaCite Autor Title Journal Page Issue Year Score
new Eva 32.90 73.35 29.83 4.58 25.05 77.04 50.22

ParaCite Autor Title Journal Page Issue Year Score
old Eva 99.08 62.72 30.46 100.00 93.96 99.70 78.81

Our Author Title Journal Volumn Page Issue Month Year Score
new Eva 93.73 73.32 51.34 83.52 94.62 85.11 89.18 96.49 84.80

Our Author Title Journal Volumn Page Issue Month Year Score
old Eva 90.58 89.51 67.66 93.58 96.69 91.79 99.49 99.50 91.45
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Analysis

• ParaCite only can extract one author name
• Old evaluation have a problem it is highly
  probable that you will obtain high accuracy, if
  you extract less information

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Evaluation for clean DataSet

• Ciation String is fully composed of corresponding
  metadata

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Compare with INFOMAP

• DataSet
• Includes 160000 record
• Training Dataset 10000 X 6 (JMIS, ACM, IEEE,
  APA, MISQ, and ISR)
• Testing Dataset 10000 X 6 (JMIS, ACM, IEEE, APA,
  MISQ, and ISR)

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Result
Author Title Journal Volumn Page Issue Year Overall average
APA 99.67 96.38 97.06 98.99 98.71 98.12 99.42 98.33
IEEE 98.72 98.12 99.12 99.30 98.40 98.39 99.40 98.78
ACM 97.14 95.01 93.93 97.19 97.92 97.03 98.88 96.73
ISR 99.48 96.17 96.96 99.15 98.55 98.39 99.35 98.29
MISQ 98.59 97.99 98.98 99.41 98.83 98.61 99.54 98.85
JMIS 91.95 87.90 90.46 99.23 98.76 98.03 99.46 95.11
Average 97.59 95.26 96.09 98.88 98.53 98.09 99.34 97.68
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Evaluation for Cora DataSet

• 500 records
• Be used as benchmark for many papers
• (HMM, SVM, CRF)

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Evaluation

• Divide words into four kinds
• TP,FP,TN,FN
• Four metrics
• Word Accuracy (TPTN)/(TPFPFNTN)
• Precision TP/(TPFP)
• Recall TP/(TPFN)
• F1-measure (2PrecisionRecall)/(PrecisionRecall
  )

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Our System Our System Our System
acc. F1.
Author 97.17 93.98
Title 94.17 90.13
Journal 93.58 83.27
Volume 99.21 84.62
Page 99.21 92.09
Date 99.92 98.96
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Mining Translations of Chinese Names from Web
Corpora by Using a Query Expansion Technique and
Support Vector Machine
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Agenda

• Introduction
• Proposed Approach
• Experiments
• Conclusions and Future Work

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Background

• Most of academic information can be found on the
  Web
• Scholar Google, DBLP etc.

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Problems in Searching Chinese Name
Only Chinese Corpus
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Challenges in Chinese Name Translation

• Many pronunciation rules in different areas
• ? ? Chen (Taiwan)
• ? ? Tsun (Hong Kong)
• ? ? Tan (Fukien)
• Some additional words exist.
• Ex ??? (Kwang-Ming Frank Hwang)
• Ex ??? (Jane Win-Shih Liu)

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Common Chinese Name Translation Format
Name Format Examples
Type-1. (Chinese given name) (Surname) or (Surname), (Chinese given name) ??? (Fon-Che Liu) ??? (Ng Tian Hann) ?? (Ngau Lam)
Type-2. (Merged Chinese given name) (Surname) ??? (Derchyi Wu)
Type-3. (Western first name) (Surname) ??? (Anne Chao)
Type-4. (Chinese given name) (Western first name) (Surname) ??? (Kwang-Ming Frank Hwang)
Type-5. (Abbreviated Chinese given name) (Surname) ??? (S.-Y. Chang)
Type-6. (Western first name) (Abbreviated Chinese given name) (Surname) ??? (Jack-C. Lee)
Type-7. (Chinese given name) (Abbreviated Chinese given name) (Surname) ???(Gwei-Hung H. Tsai)
Type-8. (Chinese given name) (Unpredictable Surname) ???(Jane Win-Shih Liu)
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Goal

• Design an automatic mechanism to translate a
  given Chinese name into its related English name

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Agenda

• Introduction
• Proposed Approach
• Experiments
• Conclusions and Future Work

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Concepts of Proposed Approach
No corresponding translations
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Three Major Techniques

• Query expansion technique
• ? Translation of the surname
• Obtaining the related Web page snippets of the
  Chinese name translation.
• Solve the problem of the unrelated term existing
  in the name translation.
• Knowledge-based method
• ? Chinese surname database, A common dictionary,
  Western first name database
• Obtaining all the name-like terms from the
  returned Web page snippets.
• SVM
• ? Chinese pronunciation database, the phonetic
  feature and the distant feature,
  selectedatraining samples
• Selecting the appropriate Chinese name
  translations from the candidates.

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System Architecture
Returned Web page snippets
Returned Web page snippets
Name candidates
Name candidates
Chinese names
Chinese names
Query expander
Candidate extractor
SVM-based name selector
Query expander
Candidate extractor
SVM-based name selector
Translated English names
Translated English names
Chinese surname database
Western first name database
Chinese pronunciation database
Chinese surname database
Western first name database
Chinese pronunciation database
On-line dictionary
On-line dictionary
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Query Expander

• Goal
• To retrieve Web page snippets that contain both
  a persons Chinese name and the translation of
  the persons surname.
• Name splitter
• Determining whether the input Chinese name
  contains a compound surname
• ? Chinese surname database
• Dividing the input Chinese name into a Surname
  part and a given name part.
• Surname translator
• Selecting appropriate surname translations.
• ? Chinese surname database
• The strength of relationship between each surname
  translation and the person is determined by the
  distance from the persons Chinese name to the
  surnames translation.
• Web page retriever
• Making the concept of the query word more
  clearly.
• Retrieving the related Web pages back.
• The new query word will be (Chinese name)
  (Surnames translation).

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Distance from Two Terms

• Calculation of the distance from two terms
  where D is the distance, N is the number of
  non-words between the two terms.

???( Wei-Da Chen)
The distance from the persons Chinese name (???)
to the surnames translation (Chen) is 3.
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Candidate Extractor

• Goal
• To extract possible candidates from the
  retrieved Web page snippets.
• Steps
• Removing all HTML tags.
• Identifying out all the positions of the Chinese
  surnames existing in the snippets.
• ? Chinese surname database
• Extracting any English terms near each surname in
  the snippets if the term has one of the following
  properties
• The term cannot be found in a common dictionary.
• The term is a Western first name.
• The length of the term is 1.
• ?At most three English terms in the
  neighborhood of the surname will be extracted.

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System Architecture 4/10 - Candidate extractor
Step1 Identifying out all the
positions of the Chinese surnames existing in the
snippets.
The extracted terms will be the name translation
candidates and be sent to SVM-based name selector
for processing

• Step2 Extracting
  any English terms near each surname in the
  snippets if the term has one of the following
  properties
• The term cannot be found in a common
  dictionary.
• The term is a Western first name.
• The length of the term is 1.

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SVM-based Name Selector

• Goal
• To extract each candidates features and utilize
  them to determine whether the candidate is the
  correct translation of the input Chinese name.
• Features
• The phonetic feature
• Phonetic similarity
• ? Soundex algorithm
• The distant feature
• Smallest distance (between the Chinese name and
  the translation candidates)
• Number of appearance in the neighborhood

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Distant Features

• The neighborhood
• The close area of each occurrence of the Chinese
  name.
• The close area is defined by a given threshold of
  distance of number of words.

Smallest distance 2
Number of appearance in the neighborhood of the
candidate win-shih 2
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Summary

• Query expansion technique
• Retrieving related Web pages.
• Knowledge-based method
• Extracting appropriate name translation
  candidates from the retrieved Web pages.
• SVM
• Learning the verification rule and
• Selecting appropriate name translation from
  extracted candidates.

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Agenda

• Introduction
• Proposed Approach
• Experiments
• Conclusions and Future Work

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Testing Environment and Dataset 1/3

• The following tool are used
• Cambridge on-line dictionary
• Google search engine
• LIBSVM
• Two datasets are used
• Dataset I (training testing)
• Collected from the Directory of scholars of
  Institute of Mathematics.
• Contains 78 pieces of data.
• Dataset II (testing)
• Collected by our program from the Website of the
  Directory of Division of Computer Science of
  National Science Council.
• Contains 1,157 pieces of data, and the name
  translations of 40 data are not existed in Google.

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Testing Environment and Dataset 2/3
Name format Example Dataset I Dataset I Dataset II Dataset II
Name format Example
Type-1. (Chinese given name) (Surname) or (Surname), (Chinese given name) ???(Jen-Wen Ding) ???(Der-Rong Din) ???(Ming Ouhyang) 19 24.3 1000 89.5
Type-2. (Merged Chinese given name) (Surname) ???(Piyu Tsai) 10 12.8 42 3.8
Type-3. (Western first name) (Surname) ???(Eugene Lai) 9 11.5 9 0.8
Type-4. (Chinese given name) (Western first name) (Surname) ???(Alan Li-Sung liu) ???(Jia-Yih Joy Chen) ???(Fongray Frank Young) 14 17.9 50 4.5
Type-5. (Abbreviated Chinese given name) (Surname) ???(I.-C. Hung) 3 3.8 0 0
Type-6. (Western first name) (Abbreviated Chinese given name) (Surname) ???(Judy C. R. Tseng) 8 10.3 9 0.8
Type-7. (Chinese given name) (Abbreviated Chinese given name) (Surname) ???(Tetz C. Huang) 3 3.8 3 0.4
Type-8. (Chinese given name) (Unpredictable Surname) ???(Trieu-Kien Truong) 12 15.4 4 0.4
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Testing Environment and Dataset 3/3

• The alignment accuracy
• Proposed by Huang (2005).
• The probability of selecting the correct answers
  when the searched snippets contain the correct
  answers.
• A
• where
• Ai The alignment accuracy of candidate i.
• Nd The number of testing data.
• Ncc The number of correct translation.
• Performance measurement Top-1 to Top-5 alignment
  accuracy.

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Results and Analysis 1/3- Overall performance on
Dataset I
70.5 top-1 accuracy 91 top-5 accuracy
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Results and Analysis 2/3 - Overall performance
on Dataset II
57.9 top-1 accuracy 86.2 top-5 accuracy
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Results and Analysis 3/3 - Performance of each
name type
Name format Example
Type-1 ???(Jen-Wen Ding) ???(Der-Rong Din) ???(Ming Ouhyang)
Type-2 ???(Piyu Tsai)
Type-3 ???(Eugene Lai)
Type-4 ???(Alan Li-Sung liu) ???(Jia-Yih Joy Chen)
Type-5 ???(I.-C. Hung)
Type-6 ???(Judy C. R. Tseng)
Type-7 ???(Tetz C. Huang)
Type-8 ???(Trieu-Kien Truong)
Our system performs better in type-1, type-2,
type-4, type-6.
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Discussions

• Major reason for the low performance on Type-3,
  Type-5, Type-7 and Type-8
• The lack of Web information.
• Usually more than one correct name translations
  for an input Chinese name are found out.
• The name ambiguity problem.

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Limitations

• Uncommon surname
• Rely on Web resources
• Search engine selecting
• No name disambiguation

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Agenda

• Introduction
• Proposed Approach
• Experiments
• Conclusions

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Conclusions

• Mining information through Web corpora is
  effective for dealing with person name
  translation problem
• Name ambiguity problem arises frequently

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Thank You Jan-Ming Ho hoho_at_iis.sinica.edu.tw Ins
titute of Information Science Academia Sinica