Research Interests :  Their Dynamics, Structures and Applications in Personalized Web Search Yi Zeng 1 , Erzhong Zhou 1 , ...
Web Intelligence Consortium
The Large Knowledge Collider Project 13 partner institutions (from 11 countries, 2 from Asia) <ul><li>a  platform  for inf...
Motivation <ul><li>Vague/Incomplete   queries over  large scale  data. </li></ul><ul><li>(How to get more  refined queries...
The Acquisition, Structure and  Dynamics of Research Interests <ul><li>Why? </li></ul><ul><li>Human Learning Theory [Brans...
Different Interests Evaluation Functions <ul><li>(Frequency)  Cumulative  Interest   :  </li></ul>An analysis of cumulativ...
Weights of Interests <ul><li>Users’ interests will be distracted if they hold various interests at the same time. </li></u...
Obtaining the Retained Interests <ul><li>Except for  frequency ,  what else  is important to  correctly obtain  retained i...
Obtaining the Retained Interests  (cont.) <ul><li>( Frequency and Recency )  Exponential Model  for Interest Retention : <...
Obtaining the  Top N  Interests A comparative study of total research interests from 1990 to 2008 and retained interests i...
Building and Analyzing  the Structure of Research Interests Observed Phenomenon: [1] main research interests ( pivotal nod...
Statistical Characteristics on  the Dynamics of Total Research Interests <ul><li>Not  a   pure random Process ! </li></ul>...
Interests with Self Organized Criticality <ul><li>Self organized criticality   [Barabasi 2002] </li></ul><ul><li>( The win...
Timing characteristics of research interests Dynamic characteristics of  lnterest Longest Duration  and  Interest Cumulati...
Explanations on the Observed  Power Law Distributions <ul><li>What causes the “ Scale-free Phenomenon ” in research intere...
A Comparative Study of  Different Interest Evaluation Methods Interests Longest Duration Interests Cumulative Duration Zhi...
Social Network based  Group  Interest s Models <ul><li>An example of Group Interest . </li></ul>How to acquire the top N i...
Overlap of User Interests and Group Interests Top 9 interests retention of a user and his group interests retention. (Rica...
A Step Forward : Semantic Similarity ---- Obtaining More Accurate Interest Descriptions <ul><li>Consistent interests witho...
Semantic Similarity and Interests Re-ranking Semantic Similarity judges by  Normalized Google Distance [Rudi and Paul 2007...
<ul><li>A comparative study of interests ranking without and with re-ranking strategy  </li></ul>Semantic Similarity and I...
Similarity Measures <ul><li>Google Similarity Distance </li></ul><ul><li>The whole  Google file system as the knowledge ba...
Evaluations on  Normalized Medline Distance (NMD) Experts evaluated 30 medline term pairs Pearson Correlation: NMD gets th...
Motivation for User Interests Description <ul><li>Large scale  data vs  most relevant  data for a  specific user. </li></u...
Defining User Interests <ul><li>Interest :  the activities  that you  enjoy doing  and the  subjects  that you  like to sp...
The e-FOAF: interest Vocabulary <ul><li>An extension  of current  FOAF vocabulary  in the  Semantic Web community . </li><...
Integration of WI and e-FOAF:interests  by FOAF community By Balthasar A.C. Schopman from Vrije University Amsterdam
Integration of WI and e-FOAF:interests  by FOAF community (cont.) <ul><li>Based on  Vocamp 2010 </li></ul><ul><li>By Bob F...
Computer Scientists’ Research Interests Dataset <ul><li>We analyzed research interests of  all the computer scientists in ...
The Utilization of e-FOAF:interests Vocabulary <ul><li>Accessing user interests and downloading them as an RDF file. </li>...
Bring User Interests to  Literature Search Refinement <ul><li>User interests </li></ul><ul><li>“  They come to formal educ...
Search Refinement by Interests  from Different Perspectives <ul><li>Vague/incomplete queries  may produce  too many  resul...
DBLP-SSE : DBLP Search Support Engine The DBLP dataset Web Semantic Knowledge Sub datasets pre-selection *  Web   Intellig...
Search Results without any Refinement
Search Results with  Interests-based Refinement http://www.wici-lab.org/wici/dblp-sse/
User Evaluation of Refinement Strategy <ul><li>Participants 7 DBLP authors: </li></ul><ul><li>Preference order 100% :  </l...
Scalability for Query Time With selection: approximately 80% of the time can be saved. equivalent to  Refined query based ...
The Effect of Query Constraints Numbers
Recall and Spent Time (Unrefined queries vs Interest-based Selection <ul><li>As the data goes to  larger scale , getting  ...
Context-Aware Linked Life Data Search <ul><li>Utilizing user interests to refine vague and incomplete search </li></ul>
Publications related to this talk <ul><li>Research Interests : Their Dynamics, Structures and Applications in Web Search R...
Thank you! URL:   http://www.wici-lab.org/wici/~yizeng  Email:  yizeng@bjut.edu.cn
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Research Interests : Their Dynamics, Structures and Applications in Personalized Web Search

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About how user interests (more specifically research interests of scientists) can be quantitatively analized and used in personalized Web search (Invited talk at Microsoft Research Asia NLC Group).

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Research Interests : Their Dynamics, Structures and Applications in Personalized Web Search

  1. 1. Research Interests : Their Dynamics, Structures and Applications in Personalized Web Search Yi Zeng 1 , Erzhong Zhou 1 , Xu Ren 1 , Yulin Qin 1,3 , Ning Zhong 1,2 , Zhisheng Huang 4 1. International WIC Institute, Beijing University of Technology, China 2. Maebashi Institute of Technology, Japan 3. Carnegie Mellon University, USA 4. Vrije University Amsterdam, the Netherlands
  2. 2. Web Intelligence Consortium
  3. 3. The Large Knowledge Collider Project 13 partner institutions (from 11 countries, 2 from Asia) <ul><li>a platform for infinitely scalable querying and reasoning on the linked data-web. </li></ul>
  4. 4. Motivation <ul><li>Vague/Incomplete queries over large scale data. </li></ul><ul><li>(How to get more refined queries to reduce the size of the result set?). </li></ul><ul><li>Large scale data vs most relevant data for a specific user. </li></ul><ul><li>Diversity for different users in the context of large scale data. </li></ul><ul><li>Realizing Diversity of Users by user interests . </li></ul><ul><li>Understanding the structural and dynamical characteristics of user interests is the foundation for its utilization in Web search refinement . </li></ul>
  5. 5. The Acquisition, Structure and Dynamics of Research Interests <ul><li>Why? </li></ul><ul><li>Human Learning Theory [Bransford 2000] </li></ul><ul><li>Basic Level Advantage [Rogers 2007] </li></ul><ul><li>How? </li></ul><ul><li>Identifying key interests </li></ul><ul><li>Utilizing interests for the unification of knowledge retrieval and reasoning. </li></ul><ul><li>What if the interests are dynamic changing? And is it really changing all the time? And how? </li></ul>
  6. 6. Different Interests Evaluation Functions <ul><li>(Frequency) Cumulative Interest : </li></ul>An analysis of cumulative interests in different time intervals. (Paul Erdos, with more than 1400 papers involved) <ul><li>Statistical Characteristics analysis: </li></ul><ul><li>All the plots are distributed around a strait line, and by Shapiro wilks measurement, the significance value is 0.058, which is greater than 0.05, hence the distribution of Erdos’s publication number over years is a normal distribution. </li></ul><ul><li>Cumulative Interests of an author may follow different kinds of distributions. </li></ul>The “ Basic level advantage ” [ Rogers2007 ]. Concepts in a basic level -- > more frequently than other terms [Wisniewski1989].
  7. 7. Weights of Interests <ul><li>Users’ interests will be distracted if they hold various interests at the same time. </li></ul><ul><li>For each of the interests, they have ups and downs. It can be discovered by the change of relative weights of the interests compared to other interests. </li></ul>An analysis of Ricardo Baeza-Yates’ weighted interests w(t(i), j).
  8. 8. Obtaining the Retained Interests <ul><li>Except for frequency , what else is important to correctly obtain retained interests? </li></ul><ul><li>Forgetting mechanism in cognitive memory retention </li></ul><ul><li>(exponential function model, power function model) [Anderson, Schooler 1991]. </li></ul>Pictures from: [Schooler 1993] Schooler, L. J. & Anderson, J. R.: Recency and Context: An Environmental Analysis of Memory. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society, pp. 889-894, 1993. (Frequency and Recency) Memory Retention:
  9. 9. Obtaining the Retained Interests (cont.) <ul><li>( Frequency and Recency ) Exponential Model for Interest Retention : </li></ul><ul><li>( Frequency and Recency ) Power Model for Interest Retention : </li></ul>[Zeng 2009a] Cognitive Memory Retention Based Starting Point for Query Extension and Granular Selection, Yi Zeng, Haiyan Zhou, Ning Zhong, Yulin Qin, Shengfu Lu, Yiyu Yao, Yang Gao. In: Cognitive Memory Component (v1), LarKC deliverable 2-3-1 , Coordinated by Jose Quesada and Yi Zeng , March 30, 2009 . [Zeng 2009b] Yi Zeng, Yiyu Yao, Ning Zhong. DBLP-SSE: A DBLP Search Support Engine, In: Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Milan, Italy, September 15-18, 2009 . [Maanen 2009] Leendert van Maanen, Julian N. Marewski.: Recommender Systems for Literature Selection: A Competition between Decision Making and Memory Models, CogSci 2009, July 31-August 1, 2009 .
  10. 10. Obtaining the Top N Interests A comparative study of total research interests from 1990 to 2008 and retained interests in 2009 (based on both the power law and exponential law models). Difference on the contribution values from papers published in different years. <ul><li>Retained interest vs future interests . </li></ul><ul><li>publication numbers are within [200, 300] </li></ul><ul><li>top 9 interests </li></ul><ul><li>2001 to 2008 </li></ul><ul><li>140 persons </li></ul><ul><li>51.14% predict 5 out of 9 interests. </li></ul><ul><li>Spearman rank correlation: rho = 0.66 </li></ul><ul><li>1-tail t-test: 0.02 (close to statistically significant) </li></ul>
  11. 11. Building and Analyzing the Structure of Research Interests Observed Phenomenon: [1] main research interests ( pivotal nodes ) are dynamically changing all the time. With older ones disappear and new ones emerged . [2] Relations among research interests varies as time passed ( strengthen or weaken ). [3] main research interests are closely related to each other. (The closeness is getting stronger from time to time, which made the degree of separation around 2-3. It indicates that for an author, research interests are not isolated but highly relevant . [4] Many top research interests (pivotal nodes) remain active in the interest network (e.g. search, analysis, match). Figure 7 . Ricardos research interest dynamic evolution network from 1991 to 2009. (Based on DBLP publication list, with 232 papers involved). The network is a graph with weighted edges and weighted vertices . An Author’s Research Interest Evolution Network
  12. 12. Statistical Characteristics on the Dynamics of Total Research Interests <ul><li>Not a pure random Process ! </li></ul><ul><li>There might be some universal characteristics and hidden rules ! </li></ul>Pictures from math.ucsd.edu. and math.tsukuba.ac.jp Figure 2 : Power-law distribution on weights of research interests for Leonhard Euler (Publication list is from Euler's Archive, with 856 papers ), Paul Erdos (publication list is from Erdos' publication collection project (1929-1989) and MathSciNet (1990-2004), with 1437 papers involved. Translation of titles from German, French, Hungarian has been made by google translation and Babylon translation ), and Ricardo Baeza-Yates (from DBLP). (With processing on meaningless words, tense, singular, plural form, third person, etc.
  13. 13. Interests with Self Organized Criticality <ul><li>Self organized criticality [Barabasi 2002] </li></ul><ul><li>( The winner takes all ) </li></ul>Figure 11 . Zdzislaw Pawlak’s Interest statistics showing Self organized criticality. Rough Set Figure 12 . Zdzislaw Pawlak’s Interest connection network (1984-2008, with 62.1% interests directly connected to “rough”).
  14. 14. Timing characteristics of research interests Dynamic characteristics of lnterest Longest Duration and Interest Cumulative Duration . Figure 9 : Ricardo's research interest lasting time and appear time distribution statistics. <ul><li>a few large spikes in the plot , corresponding to very long interest longest duration and interest cumulative duration for some research interests : non-Poisson process ; </li></ul>(by linear fit ) Inspired by Human Dynamics [Barabasi 2005] Figure 9(b) : the probability of having n research interests whose lasting time is a fixed time interval . statistical distribution approximation :
  15. 15. Explanations on the Observed Power Law Distributions <ul><li>What causes the “ Scale-free Phenomenon ” in research interests? </li></ul><ul><li>‘ the rich get richer’ effect [Simon 1955] ( preferential attachment [Barabasi 1999]) </li></ul><ul><li>Researchers are likely to work around a few more general topics and the more specific topics are changing from time to time, but around or very related with these general topics. </li></ul>The picture is from: Peter Csermely. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks, Springer, 2006. [Simon 1955] Simon, H.: On a class of skew distribution functions. Biometrika 42, 425–440, 1955. [Barabasi 1999] Barabasi, A.L. and Albert, R. : Emergence of scaling in random networks. Science 286, 509–512. ‘ the rich get richer’ effect [Simon1955]
  16. 16. A Comparative Study of Different Interest Evaluation Methods Interests Longest Duration Interests Cumulative Duration Zhisheng Huang’s Interests Evaluation from CI, ILD and ICD
  17. 17. Social Network based Group Interest s Models <ul><li>An example of Group Interest . </li></ul>How to acquire the top N interests? <ul><li>Group Interest Function: </li></ul>Carlos Castillo Ricardo A. Baeza-Yates Web PageRank Network Spam Search Detection Analysis Link Content Web Search Retrieval Information Query Analysis Challenge Engine Mining
  18. 18. Overlap of User Interests and Group Interests Top 9 interests retention of a user and his group interests retention. (Ricardo A. Baeza-Yates, based on May 2008 version of SwetoDBLP). … Model … Analysis … Text … Challenge 14 Analysis 1.26 Minining 18 Query 2.10 Engine 19 System 2.14 Query 26 Information 2.27 Information 28 Web 3.19 Retrieval 30 Retrieval 5.59 Search 35 Search 7.81 Web Top 9 Group Retained Interests Top 9 Retained Interests
  19. 19. A Step Forward : Semantic Similarity ---- Obtaining More Accurate Interest Descriptions <ul><li>Consistent interests without consideration of semantic similarity. </li></ul>Consistent interests with consideration of semantic similarity. Carlos Castillo Ricardo A. Baeza-Yates Web PageRank Network Spam Search Detection Analysis Link Content Web Search Retrieval Information Query Analysis Challenge Engine Mining Carlos Castillo Ricardo A. Baeza-Yates Web PageRank Network Spam Search Detection Analysis Link Content Web Search Retrieval Information Query Analysis Challenge Engine Mining
  20. 20. Semantic Similarity and Interests Re-ranking Semantic Similarity judges by Normalized Google Distance [Rudi and Paul 2007] Normalized Google Distance Google, Bing as the Knowledge base. 0.080 reasoning ontology 0.460 pagerank Query 0.332 ontology logic 0.497 pagerank retrieval 0.050 semantic reasoning 0.403 query retrieval -0.003 semantic ontology 0.490 pagerank search 0.276 semantic logic 0.483 query search 0.239 reasoning logic 0.529 retrieval search NGD interest y interest x NGD interest y interest x
  21. 21. <ul><li>A comparative study of interests ranking without and with re-ranking strategy </li></ul>Semantic Similarity and Interests Re-ranking (cont.) Interests Re-ranking Function Dynamic Agent Prolog Dynamic Agent Prolog Agent Dynamic Inconsistent Agent Dynamic Inconsistent Prolog Prolog Dynamic Logic Prolog Dynamic Inconsistent Inconsistent Logic Prolog Logic Reasoning Web Web Reasoning Inconsistent Inconsistent Semantic Logic Logic Semantic Web Reasoning Logic Reasoning Reasoning Ontology Reasoning Semantic Ontology Semantic Semantic Web Semantic Web Web Ontology Ontology Agent Ontology Ontology Agent Interests Ranking Perspectives With semantic similarity based re-ranking (b) Without semantic similarity based re-ranking (a)
  22. 22. Similarity Measures <ul><li>Google Similarity Distance </li></ul><ul><li>The whole Google file system as the knowledge base </li></ul><ul><li>It is simply not accurate since there are many noisy data from different field. </li></ul><ul><li>Medline Similarity Distance </li></ul><ul><li>We ask the right person the right question! </li></ul><ul><li>Domain specific knowledge source is needed to acquire more accurate and professional answers. </li></ul>Just ask me! I know everything and believe me! I know Chemistry I know Medical Science I know Cognition I know Mathematics My Question is about Medical Science
  23. 23. Evaluations on Normalized Medline Distance (NMD) Experts evaluated 30 medline term pairs Pearson Correlation: NMD gets the highest value among the measures, 0.792 T-test significance: 0.995 Experts from AstraZeneca evaluated 90 randomly generated pairs Pearson Correlation: NMD: 0.736 vs NGD:0.531 Average: Experts:0.590, NMD:0.390, NGD:0.289 NMD is closer to experts’ evaluation
  24. 24. Motivation for User Interests Description <ul><li>Large scale data vs most relevant data for a specific user. </li></ul><ul><li>User interests serve as the foundation for removing scalability problems by diversity of user backgrounds and needs . </li></ul><ul><li>Based on the idea of Linked data , it will be very useful if user interests data can be shared across various applications. </li></ul><ul><li>Consistent description and representation of user interests are needed so that the integration and sharing of user interests data will be easier. </li></ul>The Linked Open Data figure is from http://richard.cyganiak.de/2007/10/lod/
  25. 25. Defining User Interests <ul><li>Interest : the activities that you enjoy doing and the subjects that you like to spend time learning about . </li></ul><ul><li>------ Cambridge Advanced Learner's Dictionary. </li></ul><ul><li>A user interest is the subject that an agent wants to participate, get to know, </li></ul><ul><li>learn about, or be involved . </li></ul><ul><li>User interests need to be described from various perspectives . </li></ul><ul><li>It is better that each perspective can be quantitatively evaluated . </li></ul><ul><li>User Interest can be described as a five tuple : </li></ul>< Interest URI, AgentURI, Property(i), Vaule(i), Time(i) >
  26. 26. The e-FOAF: interest Vocabulary <ul><li>An extension of current FOAF vocabulary in the Semantic Web community . </li></ul><ul><li>Following the definition of “user interests” in the above slide. </li></ul><ul><li>Describe user interests quantitatively from various perspectives . </li></ul>Attribute e-foaf:cumulative_interest_value Attribute e-foaf:interest_co-occur_with Attribute e-foaf:interest_has_synonym Attribute e-foaf:interest_appeare_time Attribute e-foaf:interest_appeared_in Attribute e-foaf:interest_value_updatetime Attribute e-foaf:interest_value Class e-foaf:interest E-foaf:interest Basic Attribute e-foaf:interest_longest_duration Attribute e-foaf:retained_interest_value Attribute e-foaf:interest_cumulative_duration E-foaf:interest Complement E-foaf:interest complete Type Vocabulary Vocabulary Branch
  27. 27. Integration of WI and e-FOAF:interests by FOAF community By Balthasar A.C. Schopman from Vrije University Amsterdam
  28. 28. Integration of WI and e-FOAF:interests by FOAF community (cont.) <ul><li>Based on Vocamp 2010 </li></ul><ul><li>By Bob Ferris (SMI), the picture is from: </li></ul><ul><li>http://smiy.sourceforge.net/wi/spec/weightedinterests.html </li></ul>The wi:ComplexInterest concept as graph with relations: This photo is taken by Professor Lora Aroyo from Vrije University Amsterdam at Vocamp 2010.
  29. 29. Computer Scientists’ Research Interests Dataset <ul><li>We analyzed research interests of all the computer scientists in DBLP from different perspectives . </li></ul><ul><li>We released the “computer scientists’ research interest RDF dataset : </li></ul><ul><li>http://wiki.larkc.eu/csri-rdf ” (0.19 billion triples) </li></ul>
  30. 30. The Utilization of e-FOAF:interests Vocabulary <ul><li>Accessing user interests and downloading them as an RDF file. </li></ul><ul><li>The utilization of the interests dataset. </li></ul>The SPARQL endpoint for DBLP user interests is available at http://www.wici-lab.org/wici/dblp-sse/ Dieter & Frank 2007 
  31. 31. Bring User Interests to Literature Search Refinement <ul><li>User interests </li></ul><ul><li>“ They come to formal education with a range of prior knowledge , skills, beliefs, and concepts that significantly influence what they notice about the environment and how they organize and interpret it . This, in turn, affects their abilities to remember, reason, solve problems, and acquire new knowledge . ” [Bransford 2000] </li></ul><ul><li>Human acquire new knowledge based on pre-existing knowledge . People with different background knowledge will have various personal understanding of the same knowledge source. </li></ul><ul><li>Literature search systems are for researchers to acquire knowledge for their needs based on their queries . </li></ul>Pre-existing Knowledge Search + Acquired Knowledge Useful literatures that are relevant to the query and authors’ research interests
  32. 32. Search Refinement by Interests from Different Perspectives <ul><li>Vague/incomplete queries may produce too many results that the users have to wade through . </li></ul><ul><li>Research interests may be very related with search tasks . </li></ul><ul><li>Research interests can be evaluated from various perspectives . </li></ul><ul><li>(1) Cumulative Interests; </li></ul><ul><li>(2) Retained Interests; </li></ul><ul><li>(3) Interests Longest Duration ; </li></ul><ul><li>(4) Interests Cumulative Duration ; </li></ul><ul><li>( 5 ) Group interests; </li></ul>
  33. 33. DBLP-SSE : DBLP Search Support Engine The DBLP dataset Web Semantic Knowledge Sub datasets pre-selection * Web Intelligence and Artificial Intelligence in Education. * Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-A New Standard for System Diagnostics. * Semantic Model for Artificial Intelligence Based on Molecular Computing . * Open Information Systems Semantics for Distributed Artificial Intelligence . * Artificial Intelligence and Financial Services . * … with current interests constraints (Top 5 results) List 2 : * PROLOG Programming for Artificial Intelligence , Second Edition. * Artificial Intelligence Architectures for Composition and Performance Environment. * Artificial Intelligence in Music Education: A Critical Review. * Music, Intelligence and Artificiality. Artificial Intelligence and Music Education. * Musical Knowledge: What can Artificial Intelligence Bring to the Musician? * ... without current interests constraints (Top 5 results) List 1 : Artificial Intelligence Query : Web, Service, Semantic, Architecture, Model, Ontology, Knowledge, Computing, Language Top 9 interests Dieter Fensel Log in
  34. 34. Search Results without any Refinement
  35. 35. Search Results with Interests-based Refinement http://www.wici-lab.org/wici/dblp-sse/
  36. 36. User Evaluation of Refinement Strategy <ul><li>Participants 7 DBLP authors: </li></ul><ul><li>Preference order 100% : </li></ul><ul><li>Preference order 100% : </li></ul><ul><li>Preference order 83.3% : </li></ul><ul><li>Preference order 16.7% : </li></ul>Social Relation Based Search Refinement: Let Your Friends Help You!. Xu Ren, Yi Zeng, Yulin Qin, Ning Zhong, Zhisheng Huang, Yan Wang, and Cong Wang. Proceedings of the 2010 International Conference on Active Media Technology, Lecture Notes in Computer Science 6335, 475-485, 2010.
  37. 37. Scalability for Query Time With selection: approximately 80% of the time can be saved. equivalent to Refined query based on interests much closer to user needs may be very far from user needs Results the fastest much slower medium Query Time Interest based selection before querying Refined query based on interests Unrefined query
  38. 38. The Effect of Query Constraints Numbers
  39. 39. Recall and Spent Time (Unrefined queries vs Interest-based Selection <ul><li>As the data goes to larger scale , getting almost the same recall compared to unrefined queries, the ratio of spent time is almost linear growing. </li></ul><ul><li>Some times one can get bigger recall while the ratio of spent time is lower . </li></ul>
  40. 40. Context-Aware Linked Life Data Search <ul><li>Utilizing user interests to refine vague and incomplete search </li></ul>
  41. 41. Publications related to this talk <ul><li>Research Interests : Their Dynamics, Structures and Applications in Web Search Refinement . Yi Zeng, Erzhong Zhou, Yulin Qin, and Ning Zhong. Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Toronto, Canada, August 31- September 3, 2010. </li></ul><ul><li>User Interests: Definition, Vocabulary, and Utilization in Unifying Search and Reasoning . Yi Zeng, Yan Wang, Zhisheng Huang, Danica Damljanovic, Ning Zhong, and Cong Wang. Proceedings of the 2010 International Conference on Active Media Technology, Lecture Notes in Computer Science 6335, 98-107, 2010. </li></ul><ul><li>Social Relation Based Search Refinement: Let Your Friends Help You! . Xu Ren, Yi Zeng, Yulin Qin, Ning Zhong, Zhisheng Huang, Yan Wang, and Cong Wang. Proceedings of the 2010 International Conference on Active Media Technology, Lecture Notes in Computer Science 6335, 475-485, 2010. </li></ul><ul><li>Normalized Medline Distance and Its Utilization in Context-aware Life Science Literature Search . Yan Wang, Cong Wang, Yi Zeng, Zhisheng Huang, Vassil Momtchev, Bo Andersson, Xu Ren, and Ning Zhong. Proceedings of the 4th Chinese Semantic Web Symposium, August 19-21, 2010 (Recommended to Tsinghua Science and Technology, Elsevier). </li></ul><ul><li>User-centric Query Refinement and Processing Using Granularity Based Strategies . Yi Zeng, Ning Zhong, Yan Wang, Yulin Qin, Zhisheng Huang, Haiyan Zhou, Yiyu Yao, and Frank van Harmelen. Knowledge and Information Systems, Springer. </li></ul><ul><li>DBLP-SSE: A DBLP Search Support Engine , Yi Zeng, Yiyu Yao, Ning Zhong. In: Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Milan, Italy, September 15-18, 2009. </li></ul>
  42. 42. Thank you! URL: http://www.wici-lab.org/wici/~yizeng Email: yizeng@bjut.edu.cn

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