臺灣博碩士論文加值系統

搜尋特定主題的專家在現實世界的許多情況中是一個非常迫切的問題，例如:尋找專業人士來解決特殊的問題或尋找專家學者對特定文件做審查任務。雖然如此，過去的研究只著重在專家候選人的著作內容在被查詢文件中出現的次數來決定該候選人是否符合給定文件的所需專長或審查標準，如此一來搜尋到的專家通常僅擅長於某單一特定領域，因為被查詢主題通常為多領域的應用，而計算關鍵字的次數無法符合多領域的需求，此外每次都需要將全部的專家候選人與被查詢文件比較相當耗費時間，為了解決上述問題，我們提出一個專家搜尋系統稱做“應用眾人智慧於專家推薦系統”。

本系統是依據專家學者出版著作的質量及與被查詢文件的相關度來判定專家是否符合審查委員的專長與資格。本系統利用先行對被查詢文件分類增加查詢效率且利用眾人智慧(Collaborative Intelligence)建置出的語意網路量測專家候選人與被查詢文件之間的相關度，並參考專家的著作質量後決定審查委員的順序。在我們的實驗中顯示: (1) 我們提出的方法在領域分辨與推薦審查之專家的正確率和精確率的調和平均數(F-measure)均達到良好的效率且較之前的方法好; (2) 在辨別被查詢文件領域的方法中，可以利用適應式學習法持續學習未見過的字詞與知識; (3) 被查詢文件先作領域的分類對推薦審查專家的精準度和速度是有幫助的。

In this thesis, expert-finding problem is transformed to a classification issue. We build a knowledge database to represent the expertise characteristic of domain from web information constructed by collaborative intelligence, and an incremental learning method is proposed to update the database. Furthermore, results are ranked by measuring the correlation in the concept network from online encyclopedia. In our experiments, we use the real world dataset which comprise 2,701 experts who are categorized into 8 expertise domains. Our experimental results show that the expertise knowledge extracted from collaborative intelligence can improve efficiency and effect of classification and increase the precision of ranking expert at least 20%.

ABSTRACT i
ACKNOWLEDGEMENTS ii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 The Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Outlines of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Background 10
2.1 Background of Reviewer Recommendation System . . . . . . . . . . 10
2.1.1 Expertise Modeling . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Real World Task: Proposal Reviewer Assignment . . . . . . . . . . . 14
2.3 Collaborative Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Wikipedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 Semantic Relatedness Measuring . . . . . . . . . . . . . . . 16
iii
CONTENTS iv
3 System Architecture 19
3.1 Notation Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Domain Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.1 Domain Characteristic Modeller . . . . . . . . . . . . . . . . 22
3.2.2 Domain Classifier . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Expertise Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 Wiki-Page-Title Relation Parser . . . . . . . . . . . . . . . . 28
3.3.2 Semantic Relatedness Calculator . . . . . . . . . . . . . . . . 30
3.4 Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Experiments 34
4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Experimental Measure . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.1 Performance Analysis of Classifier . . . . . . . . . . . . . . . 40
4.3.2 Correct Rate of Expert-Finding . . . . . . . . . . . . . . . . 43
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5 Conclusion and FurtherWork 50
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

[1] K. Balog and M. de Rijke, “Finding experts and their details in e-mail corpora,”in WWW ’06: Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland, 2006, pp. 1035–1036.
[2] K. Balog, M. de Rijke, andW.Weerkamp, “Bloggers as experts: Feed distillation using expert retrieval models,” in SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Singapore, 2008, pp. 753–754.
[3] C.-C. Chou, K.-H. Yang, and H.-M. Lee, “Aefs ’07: Authoritative expert finding system based on a language model and social network analysis,” in Proceedings of 12th conference on Artificial Intelligence and Applications(TAAI), 2007, pp. 412–419.
[4] G. Demartini, “Finding experts using wikipedia,” in FEWS ’07: Finding Experts on the Web with Semantics Workshop at ISWC 2007 + ASWC 2007, Korea,
November 2007, pp. 33–41.
[5] S. T. Dumais and J. Nielsen, “Automating the assignment of submitted
manuscripts to reviewers,” in SIGIR ’92: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information
retrieval, Copenhagen, Denmark, 1992, pp. 233–244.
[6] H. Fang and C. Zhai, “Probabilistic models for expert finding,” in ECIR07: Advances in Information Retrieval, 29th European Conference on IR Research, Rome, Italy, April 2007, pp. 418–430.
[7] C. Fellbaum, WordNet: An Electronical Lexical Database. Cambridge, MA:
The MIT Press, 1998.
[8] L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan, G. Wolfman, and E. Ruppin, “Placing search in context: the concept revisited,” ACM Trans. Inf. Syst., vol. 20, no. 1, pp. 116–131, January 2002.
[9] E. Gabrilovich and S. Markovitch, “Computing Semantic Relatedness using
Wikipedia-based Explicit Semantic Analysis,” in Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007, pp. 1606–1611.
[10] S. Hettich and M. J. Pazzani, “Mining for proposal reviewers: lessons learned at the national science foundation,” in KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining,
Philadelphia, PA, USA, 2006, pp. 862–871.
[11] C. Leacock and M. Chodorow, Combining local context and WordNet similarity
for word sense identification, in Wordnet: A Lexical Reference System and its
Application, C. Fellbaum, Ed. Cambridge, MA: MIT Press, 1998.
[12] P. Liu, K. Liu, and J. Liu, “Ontology-based expertise matching system within academia,” in WiCom ’07: International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, China, Sept. 2007, pp. 5431–5434.
[13] M. Mika, M. Ciaramita, H. Zaragoza, and J. Atserias, “Learning to tag and tagging to learn: A case study on wikipedia,” Intelligent Systems, IEEE, vol. 23,no. 5, pp. 26–33, Sept.-Oct. 2008.
[14] D. Mimno and A. McCallum, “Expertise modeling for matching papers with
reviewers,” in KDD ’07: Proceedings of the 13th ACM SIGKDD international
conference on Knowledge discovery and data mining, San Jose, California, USA,
2007, pp. 500–509.
[15] T. M. Mitchell, Machine learning. New York: McGraw-Hill, 1997.
[16] T. Oreilly, “What is web 2.0: Design patterns and business models for the next generation of software,” Social Science Research Network Working Paper Series. [Online]. Available: http://oreilly.com/web2/archive/what-is-web-20.html
[17] S. Patwardhan, T. Pedersen, and S. Banerjee, “Senserelate::targetword - a generalized framework for word sense disambiguation,” in Proceedings of the ACL
Interactive Poster and Demonstration Sessions, Ann Arbor, MI, June 2005, pp. 73–76.
[18] D. Petkova and W. B. Croft, “Hierarchical language models for expert finding in enterprise corpora,” in ICTAI ’06: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, Washington, DC, USA, 2006, pp. 599–608.
[19] S. P. Ponzetto and M. Strube, “Knowledge derived from wikipedia for computing semantic relatedness,” Journal of Artificial Intelligence Research, vol. 30, pp. 181–212, 2007.
[20] R. Rada, H. Mili, E. Bicknell, and M. Blettner, “Development and application of a metric on semantic nets,” Systems, Man and Cybernetics, IEEE Transactions on, vol. 19, no. 1, pp. 17–30, 1989.
[21] P. Schonhofen, “Annotating documents by wikipedia concepts,” vol. 1, Los
Alamitos, CA, USA, 2008, pp. 461–467.
[22] F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: a core of semantic knowledge unifying wordnet and wikipedia,” in WWW ’07: Proceedings of the 16th
international conference on World Wide Web, Banff, Alberta, Canada, 2007, pp. 697–706.
[23] C. Wagner, “Wiki: A technology for conversational knowledge management and
group collaboration,” Communications of the AIS, vol. 13, pp. 256–289, 2004.
[24] F. Wang, B. Chen, and Z. Miao, “A survey on reviewer assignment problem,”
in IEA/AIE ’08: Proceedings of the 21st international conference on Industrial,
Engineering and Other Applications of Applied Intelligent Systems, Wroclaw, Poland, 2008, pp. 718–727.
[25] P. Wang and C. Domeniconi, “Building semantic kernels for text classification using wikipedia,” in KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, Las Vegas, Nevada,USA, 2008, pp. 713–721.
[26] K.-H. Yang, C.-Y. Chen, H.-M. Lee, and J.-M. Ho, “Efs:expert finding system based on wikipedia link pattern analysis,” in SMC ’08: IEEE International Conference on Systems, Man and Cybernetics, Suntec, Singapore, October 2008, pp. 631 – 635.
[27] K.-H. Yang, T.-L. Kuo, H.-M. Lee, and J.-M. Ho, “A reviewer recommendation
system based on collaborative intelligence,” in to appear in WI’09: The 2009
IEEE / WIC / ACM International Conferences on Web Intelligence, Milano, Italy,
2009.
[28] D. Yimam, “Expert finding systems for organizations: Domain analysis and the demoir approach,” in ECSCW ’99: Beyond Knowledge Management: Management
Expertise Workshop, 2000, pp. 276–283.
[29] J. Zhang, M. S. Ackerman, and L. Adamic, “Expertise networks in online communities: structure and algorithms,” in WWW ’07: Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, 2007, pp.
221–230.