1. Mining Academic Community Jan-Ming Ho hohoiis.sinica.edu.tw C omputer S ystem and C ommunication L ab I nstitute of I nformation S cience Academia Sinica

2. 2 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

3. 3 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.

4. 4 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

5. 5 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

6. 6 Libra Academic Search  http://libra.msra.cn 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

7. 7

8. 8

9. 9 DBconnect: Conference

10. 10 DBconnect: Topic

11. 11 DBconnect: Author

12. 12 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”

13. 13 ArnetMiner

14. 14 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

15. 15 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)

16. 16 PLF: A Publication List Web Page Finder for Researchers

17. 17 Agenda  Introduction  Publication List Web Page Finder, PLF  Performance Evaluation  Conclusion, Future Work

18. 18 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).

19. 19 Problem “Publication List Web Page?”

20. 20 Definition of Publication List [Affiliation] Institute of Information Science, Academia Sinica citation string Affiliated Personal Publication List Web Page (APPL) a web page belongs to the affiliated web site of a specific person with the given name.

21. 21 Agenda  Introduction  Publication List Web Page Finder, PLF  Performance Evaluation  Conclusion, Future Work

22. 22 Process Flow

23. 23 Basic Concept A publication list web page may contain many citation strings

24. 24 Agenda  Introduction  Publication List Web Page Finder, PLF  Performance Evaluation  Conclusion, Future Work

25. 25 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 others02211% single-group 112060% multi-group 23517.5% 3168% 473.5%

26. 26 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: NotationDefinition RaRa 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

27. 27 Parameter Analysis for Single-Group (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. (m, n)

28. 28 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.

29. 29 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 system’s performance; for example, an oversized m may degrade the performance. 2.The parameter n has little influence on the system’s performance. 3.The PLF system outperforms the other two approaches on both the single-group and the multi-group datasets. (given name + keyword)

30. 30 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.

31. 31 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

32. 32 Extracting Citation Strings from Web Pages

33. 33 Extract Citation Records Structured Data Extract Web Page

34. 34 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

35. 35 Challenges: Complex Layouts of Publication List Pages

36. 36 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.

37. 37 DOM Tree Presentation of Web page

38. 38 Architecture of Citation Extraction System

39. 39 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

40. 40 BibPro : A Citation Parser based on Sequence Alignment Techniques

41. 41 System Goal

42. 42 Basic Idea(1/2)  Encode citation to protein sequence  Only keep the citation style information  order of fields  field separators AuthorTitle Journal yearpage … … ADTDLDYRPHS protein sequence

43. 43 Basic Idea(2/2)  To determine citation style by the order of punctuation marks and reserved words

44. 44 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

45. 45 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: @ # $ % ^ & * + = \ | ~ _ / ! ? 。 I: ( [ { < 「 K: ) ] } > 」 D:. G: " “ ” R:, C: - : E: ' ` Z: ; B: blank

46. 46 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

47. 47 RESULT FORM (Known Answer)

48. 48 BASE FORM (Unknow Answer)

49. 49 System Structure  System PreProcess (Template Generating System)  Citation Crawler  Template Builder  Online Parsing (Parsing System)  Template Matching  Metadata Extraction

50. 50 Citation Crawler

51. 51 BLAST-powered Template Matching

52. 52 Evaluation for CiteSeer DataSet  Consider the inconsistency between the Citation String and BibTex file(metadata)  Old Measurement:  New Measurement:

53. 53 Definition  Token parsedfield : denote tokens that appear in the parsed subfield  Token query citation : denote tokens that appear in the query citation string  Token BibTex field : denote tokens that appear in the specific subfield in the BibTex file  Token BibTex : denote all tokens that appear in the BibTex file These tokens don' t include punctuation

54. 54 Compare with ParaCite  DataSet  Collected from CiteSeer  Training Set: 2416  Testing Set: 4131  ParaCite  Using default template Database add template to its database isn’t easy  Test Testing Set  Our System  Using training template Database (Training Set)  Test Testing Set

55. 55 Experimental Results ParaCiteAutorTitleJournalPageIssueYearScore new Eva32.90%73.35%29.83%4.58%25.05%77.04%50.22% ParaCiteAutorTitleJournalPageIssueYearScore old Eva99.08%62.72%30.46%100.00%93.96%99.70%78.81% OurAuthorTitleJournalVolumnPageIssueMonthYearScore new Eva93.73%73.32%51.34%83.52%94.62%85.11%89.18%96.49%84.80% OurAuthorTitleJournalVolumnPageIssueMonthYearScore old Eva90.58%89.51%67.66%93.58%96.69%91.79%99.49%99.50%91.45%

56. 56 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

57. 57 Evaluation for clean DataSet  Ciation String is fully composed of corresponding metadata

58. 58 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)

59. 59 Result AuthorTitleJournalVolumnPageIssueYearOverall average APA99.67%96.38%97.06%98.99%98.71%98.12%99.42%98.33% IEEE98.72%98.12%99.12%99.30%98.40%98.39%99.40%98.78% ACM97.14%95.01%93.93%97.19%97.92%97.03%98.88%96.73% ISR99.48%96.17%96.96%99.15%98.55%98.39%99.35%98.29% MISQ98.59%97.99%98.98%99.41%98.83%98.61%99.54%98.85% JMIS91.95%87.90%90.46%99.23%98.76%98.03%99.46%95.11% Average97.59%95.26%96.09%98.88%98.53%98.09%99.34%97.68%

60. 60 Evaluation for Cora DataSet  500 records  Be used as benchmark for many papers (HMM, SVM, CRF)

61. 61 Evaluation  Divide words into four kinds:  TP,FP,TN,FN  Four metrics:  Word Accuracy: (TP+TN)/(TP+FP+FN+TN)  Precision: TP/(TP+FP)  Recall: TP/(TP+FN)  F1-measure: (2*Precision*Recall)/(Precision+Recall)

62. 62 Our System acc.F1. Author97.17%93.98% Title94.17%90.13% Journal93.58%83.27% Volume99.21%84.62% Page99.21%92.09% Date99.92%98.96%

63. 63 Mining Translations of Chinese Names from Web Corpora by Using a Query Expansion Technique and Support Vector Machine

64. 64 Agenda  Introduction  Proposed Approach  Experiments  Conclusions and Future Work

65. 65 Background  Most of academic information can be found on the Web  Scholar Google, DBLP etc.

66. 66 Problems in Searching Chinese Name Only Chinese Corpus

67. 67 Challenges in Chinese Name Translation  Many pronunciation rules in different areas  陳  Chen (Taiwan) 陳  Tsun (Hong Kong) 陳  Tan (Fukien)  Some additional words exist.  Ex: 黃光明 (Kwang-Ming Frank Hwang) Ex: 張韻詩 (Jane Win-Shih Liu)

68. 68 Common Chinese Name Translation Format Name FormatExamples Type-1. (Chinese given name) (Surname) or (Surname), (Chinese given name) 劉豐哲 (Fon-Che Liu) 黃田漢 (Ng Tian Hann) 林牛 (Ngau Lam) Type-2. (Merged Chinese given name) (Surname) 吳德琪 (Derchyi Wu) Type-3. (Western first name) (Surname) 趙蓮菊 (Anne Chao) Type-4. (Chinese given name) (Western first name) (Surname) 黃光明 (Kwang-Ming Frank Hwang) Type-5. (Abbreviated Chinese given name) (Surname) 張秀瑜 (S.-Y. Chang) Type-6. (Western first name) (Abbreviated Chinese given name) (Surname) 李昭勝 (Jack-C. Lee) Type-7. (Chinese given name) (Abbreviated Chinese given name) (Surname) 蔡桂紅 (Gwei-Hung H. Tsai) Type-8. (Chinese given name) (Unpredictable Surname) 張韻詩 (Jane Win-Shih Liu)

69. 69 Goal  Design an automatic mechanism to translate a given Chinese name into its related English name

70. 70 Agenda  Introduction  Proposed Approach  Experiments  Conclusions and Future Work

71. 71 Concepts of Proposed Approach No corresponding translations

72. 72 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.

73. 73 System Architecture Chinese names Query expander Candidate extractor SVM-based name selector Chinese surname database Western first name database On-line dictionary Chinese pronunciation database Returned Web page snippets Name candidates Translated English names Chinese names Query expander Chinese surname database Returned Web page snippets Candidate extractor Western first name database On-line dictionary Name candidates SVM-based name selector Chinese pronunciation database Translated English names

74. 74 Query Expander  Goal : To retrieve Web page snippets that contain both a person’s Chinese name and the translation of the person’s 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 person’s Chinese name to the surname’s 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) + (Surname’s translation)”.

75. 75 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. 陳威達 ( Wei-Da Chen) The distance from the person’s Chinese name ( 陳威達 ) to the surname’s translation (Chen) is 3.

76. 76 Candidate Extractor  Goal : To extract possible candidates from the retrieved Web page snippets.  Steps: 1.Removing all HTML tags. 2.Identifying out all the positions of the Chinese surnames existing in the snippets.  Chinese surname database 3.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.

77. 77 System Architecture 4/10 - Candidate extractor Step1 Identifying out all the positions of the Chinese surnames existing in the snippets. 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. The extracted terms will be the name translation candidates and be sent to SVM-based name selector for processing

78. 78 SVM-based Name Selector  Goal: To extract each candidate’s features and utilize them to determine whether the candidate is the correct translation of the input Chinese name.  Features: 1.The phonetic feature: –Phonetic similarity  Soundex algorithm 2.The distant feature: –Smallest distance (between the Chinese name and the translation candidates) –Number of appearance in the neighborhood

79. 79 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. Number of appearance in the neighborhood of the candidate “win-shih”: 2 Smallest distance 2

80. 80 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.

81. 81 Agenda  Introduction  Proposed Approach  Experiments  Conclusions and Future Work

82. 82 Testing Environment and Dataset 1/3  The following tool are used:  Cambridge on-line dictionary Cambridge on-line dictionary  Google search engine Google search engine  LIBSVM LIBSVM  Two datasets are used:  Dataset I (training & testing):  Collected from the Directory of scholars of Institute of Mathematics.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.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.

83. 83 Testing Environment and Dataset 2/3 Name formatExample Dataset IDataset II #%#% Type-1. (Chinese given name) (Surname) or (Surname), (Chinese given name) 丁建文 (Jen-Wen Ding) 丁德榮 (Der-Rong Din) 歐陽明 (Ming Ouhyang) 1924.3%100089.5% Type-2. (Merged Chinese given name) (Surname) 蔡丕裕 (Piyu Tsai) 1012.8%423.8% Type-3. (Western first name) (Surname) 賴友仁 (Eugene Lai) 911.5%90.8% Type-4. (Chinese given name) (Western first name) (Surname) 劉立頌 (Alan Li-Sung liu) 陳嘉懿 (Jia-Yih Joy Chen) 楊豐瑞 (Fongray Frank Young) 1417.9%504.5% Type-5. (Abbreviated Chinese given name) (Surname) 洪英超 (I.-C. Hung) 33.8%00% Type-6. (Western first name) (Abbreviated Chinese given name) (Surname) 曾秋蓉 (Judy C. R. Tseng) 810.3%90.8% Type-7. (Chinese given name) (Abbreviated Chinese given name) (Surname) 黃哲志 (Tetz C. Huang) 33.8%30.4% Type-8. (Chinese given name) (Unpredictable Surname) 張肇健 (Trieu-Kien Truong) 1215.4%40.4%

84. 84 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  A i : The alignment accuracy of candidate i.  N d : The number of testing data.  N cc : The number of correct translation.  Performance measurement: Top-1 to Top-5 alignment accuracy.

85. 85 Results and Analysis 1/3 - Overall performance on Dataset I 70.5% top-1 accuracy 91% top-5 accuracy

86. 86 Results and Analysis 2/3 - Overall performance on Dataset II 57.9% top-1 accuracy 86.2% top-5 accuracy

87. 87 Results and Analysis 3/3 - Performance of each name type Our system performs better in type-1, type-2, type-4, type-6. Name format Example Type-1 丁建文 (Jen-Wen Ding) 丁德榮 (Der-Rong Din) 歐陽明 (Ming Ouhyang) Type-2 蔡丕裕 (Piyu Tsai) Type-3 賴友仁 (Eugene Lai) Type-4 劉立頌 (Alan Li-Sung liu) 陳嘉懿 (Jia-Yih Joy Chen) Type-5 洪英超 (I.-C. Hung) Type-6 曾秋蓉 (Judy C. R. Tseng) Type-7 黃哲志 (Tetz C. Huang) Type-8 張肇健 (Trieu-Kien Truong)

88. 88 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.

89. 89 Limitations  Uncommon surname  Rely on Web resources  Search engine selecting  No name disambiguation

90. 90 Agenda  Introduction  Proposed Approach  Experiments  Conclusions

91. 91 Conclusions  Mining information through Web corpora is effective for dealing with person name translation problem  Name ambiguity problem arises frequently

92. 92 Thank You Jan-Ming Ho hoho@iis.sinica.edu.tw Institute of Information Science Academia Sinica