Advertisement

Applied Intelligence

, Volume 49, Issue 2, pp 463–477 | Cite as

Formulation of a hybrid expertise retrieval system in community question answering services

  • Dipankar KunduEmail author
  • Deba Prasad Mandal
Article
  • 87 Downloads

Abstract

In this paper, we propose a hybrid expertise retrieval system for community question answering services. The proposed system consists of two segments: a text based segment and a network based segment. For a given question, the text based segment estimates users’ knowledge introducing two new concepts: question hardness and question answerer association. The network based segment, moreover, incorporates users’ relative performances into the network structure. We denote the outputs of these two segments as knowledge score and authority score, respectively. We aggregate these two scores using a fusion technique to quantify the expertise of a given user for a given question. We have generated four datasets by downloading questions and answers from Yahoo! Answers. The performance of the proposed system is found to be superior than that of 18 state-of-the-art algorithms on these four real-world datasets.

Keywords

Expertise retrieval Community question answering Language model Question hardness Answer quality Social network analysis 

References

  1. 1.
    Liu X, Croft WB, Koll M (2005) Finding experts in community-based question-answering services. In: Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, pp 315–316Google Scholar
  2. 2.
    Kabutoya Y, Iwata T, Shiohara H, Fujimura K (2010) Effective question recommendation based on multiple features for question answering communities. In: Proceedings of the fourth international AAAI conference on weblogs and social mediaGoogle Scholar
  3. 3.
    Li B, King I (2010) Routing questions to appropriate answerers in community question answering services. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, pp 1585–1588Google Scholar
  4. 4.
    Li B, King I, Lyu MR (2011) Question routing in community question answering: putting category in its place. In: Proceedings of the 20th ACM international conference on information and knowledge management. ACM, pp 2041–2044Google Scholar
  5. 5.
    Zhang J, Ackerman MS, Adamic L (2007) Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 221–230Google Scholar
  6. 6.
    Jurczyk P, Agichtein E (2007) Hits on question answer portals: exploration of link analysis for author ranking. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 845–846Google Scholar
  7. 7.
    Jurczyk P, Agichtein E (2007) Discovering authorities in question answer communities by using link analysis. In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management. ACM, pp 919–922Google Scholar
  8. 8.
    Chen L, Nayak R (2008) Expertise analysis in a question answer portal for author ranking. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 01. IEEE Computer Society, pp 134–140Google Scholar
  9. 9.
    Schall D, Skopik F (2011) An analysis of the structure and dynamics of large-scale q/a communities. In: Eder J, Bielikova M, Tjoa A M (eds) Advances in databases and information systems. Springer, Berlin, pp 285–301Google Scholar
  10. 10.
    Aslay Ç, O’Hare N, Aiello LM, Jaimes A (2013) Competition-based networks for expert finding. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 1033–1036Google Scholar
  11. 11.
    Shahriari M, Parekodi S, Klamma R (2015) Community-aware ranking algorithms for expert identification in question-answer forums. In: Proceedings of the 15th international conference on knowledge technologies and data-driven business - i-KNOW ’15, pp 1–8Google Scholar
  12. 12.
    Kao WC, Liu DR, Wang SW (2010) Expert finding in question-answering websites: a novel hybrid approach. In: Proceedings of the 2010 ACM symposium on applied computing. ACM, pp 867–871Google Scholar
  13. 13.
    Liu DR, Chen YH, Kao WC, Wang HW (2013) Integrating expert profile, reputation and link analysis for expert finding in question-answering websites. Inf Process Manag 49(1):312–329CrossRefGoogle Scholar
  14. 14.
    Wang GA, Jiao J, Abrahams AS, Fan W, Zhang Z (2013) ExpertRank: a topic-aware expert finding algorithm for online knowledge communities. Decis Support Syst 54(3):1442–1451CrossRefGoogle Scholar
  15. 15.
    Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the web. Tech. rep., Stanford InfoLabGoogle Scholar
  16. 16.
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Lu Y, Quan X, Ni X, Liu W, Xu Y (2009) Latent link analysis for expert finding in user-interactive question answering services. In: Proceedings of the fifth international conference on semantics, knowledge and grid, 2009. SKG 2009. IEEE, pp 54–59Google Scholar
  18. 18.
    Li H, Jin S, Li S (2015) A hybrid model for experts finding in community question answering. In: Proceedings of the - 2015 international conference on cyber-enabled distributed computing and knowledge discovery, CyberC 2015, pp 176–184Google Scholar
  19. 19.
    Zhou G, Lai S, Liu K, Zhao J (2012) Topic-sensitive probabilistic model for expert finding in question answer communities. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM, pp 1662–1666Google Scholar
  20. 20.
    Yang L, Qiu M, Gottipati S, Zhu F, Jiang J, Sun H, Chen Z (2013) Cqarank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM international conference on information & knowledge management. ACM, pp 99–108Google Scholar
  21. 21.
    Liu X, Ye S, Li X, Luo Y, Rao Y (2015) ZhihuRank: a topic-sensitive expert finding algorithm in community question answering websites. In: Proceedings of the international conference on web-based learning. Springer, pp 165–173Google Scholar
  22. 22.
    Dror G, Koren Y, Maarek Y, Szpektor I (2011) I want to answer; who has a question?: Yahoo! answers recommender system. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1109–1117Google Scholar
  23. 23.
    Zhou TC, Lyu MR, King I (2012) A classification-based approach to question routing in community question answering. In: Proceedings of the 21st international conference on World Wide Web. ACM, pp 783–790Google Scholar
  24. 24.
    Zhao Z, Zhang L, He X, Ng W (2015) Expert finding for question answering via graph regularized matrix completion. IEEE Trans Knowl Data Eng 27(4):993–1004CrossRefGoogle Scholar
  25. 25.
    Yan Z, Zhou J (2015) Optimal answerer ranking for new questions in community question answering. Inf Process Manag 51(1):163–178CrossRefGoogle Scholar
  26. 26.
    Zhao Z, Yang Q, Cai D, He X, Zhuang Y (2016) Expert finding for community-based question answering via ranking metric network learning. In: IJCAI, pp 3000–3006Google Scholar
  27. 27.
    Azzam A, Tazi N, Hossny A (2017) Text-based question routing for question answering communities via deep learning. In: Proceedings of the symposium on applied computing. ACM, pp 1674–1678Google Scholar
  28. 28.
    Neshati M, Fallahnejad Z, Beigy H (2017) On dynamicity of expert finding in community question answering. Inf Process Manag 53(5):1026–1042CrossRefGoogle Scholar
  29. 29.
    Cheng X, Zhu S, Su S, Chen G (2017) A multi-objective optimization approach for question routing in community question answering sevices. IEEE Trans Knowl Data Eng 29(9):1779–1792CrossRefGoogle Scholar
  30. 30.
    Balog K, Bogers T, Azzopardi L, De Rijke M, Van Den Bosch A (2007) Broad expertise retrieval in sparse data environments. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 551–558Google Scholar
  31. 31.
    Xu Z, Ramanathan J (2016) Thread-based probabilistic models for expert finding in enterprise Microblogs. Expert Syst Appl 43:286–297CrossRefGoogle Scholar
  32. 32.
    Deng H, King I, Lyu MR (2008) Formal models for expert finding on dblp bibliography data. In: Proceedings of the eighth IEEE international conference on data mining, 2008. ICDM’08. IEEE, pp 163–172Google Scholar
  33. 33.
    Deng H, King I, Lyu MR (2012) Enhanced models for expertise retrieval using community-aware strategies. IEEE Trans Syst Man Cybern Part B (Cybern) 42(1):93–106CrossRefGoogle Scholar
  34. 34.
    Neshati M, Hashemi SH, Beigy H (2014) Expertise finding in bibliographic network: topic dominance learning approach. IEEE Trans Cybern 44(12):2646–2657CrossRefGoogle Scholar
  35. 35.
    Horowitz D, Kamvar SD (2010) The anatomy of a large-scale social search engine. In: Proceedings of the 19th international conference on World wide web. ACM, pp 431–440Google Scholar
  36. 36.
    Balog K, De Rijke M, Weerkamp W (2008) Bloggers as experts: feed distillation using expert retrieval models. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 753–754Google Scholar
  37. 37.
    Jin J, Li Y, Zhong X, Zhai L (2015) Why users contribute knowledge to online communities: an empirical study of an online social Q&A community. Inf Manag 52(7):840–849CrossRefGoogle Scholar
  38. 38.
    Liu Z, Jansen BJ (2016) Understanding and predicting question subjectivity in social question and answering. IEEE Trans Comput Soc Syst 3(1):32–41CrossRefGoogle Scholar
  39. 39.
    Zhou G, Zhou Y, He T, Wu W (2016) Learning semantic representation with neural networks for community question answering retrieval. Know-Based Syst 93(C):75–83CrossRefGoogle Scholar
  40. 40.
    Figueroa A, Gȯmez-Pantoja C, Herrera I (2016) Search clicks analysis for discovering temporally anchored questions in community question answering. Expert Syst Appl 50:89–99CrossRefGoogle Scholar
  41. 41.
    Espina A, Figueroa A (2017) Why was this asked? Automatically recognizing multiple motivations behind community question-answering questions. Expert Syst Appl 80:126–135CrossRefGoogle Scholar
  42. 42.
    Ahmed T, Srivastava A (2017) An automated approach to estimate human interest. Appl Intell 47 (4):1186–1207CrossRefGoogle Scholar
  43. 43.
    Zhang Z, Xu G, Zhang P, Wang Y (2017) Personalized recommendation algorithm for social networks based on comprehensive trust. Appl Intell 47(3):659–669CrossRefGoogle Scholar
  44. 44.
    Wu F, Duan X, Xiao J, Zhao Z, Tang S, Zhang Y, Zhuang Y (2017) Temporal interaction and causal influence in community-based question answering. IEEE Trans Knowl Data Eng 29(10):2304–2317CrossRefGoogle Scholar
  45. 45.
    Neshati M (2017) On early detection of high voted Q&A on stack overflow. Inf Process Manag 53(4):780–798CrossRefGoogle Scholar
  46. 46.
    Romeo S, Da San Martino G, Belinkov Y, Barrȯn-Cedeṅo A, Eldesouki M, Darwish K, Mubarak H, Glass J, Moschitti A (2017) Language processing and learning models for community question answering in Arabic. Inf Process Manag 0:1–17Google Scholar
  47. 47.
    Wang F, Wu W, Li Z, Zhou M (2017) Named entity disambiguation for questions in community question answering. Know-Based Syst 126(C):68–77CrossRefGoogle Scholar
  48. 48.
    Kiritchenko S, Matwin S, Nock R, Famili AF (2006) Learning and evaluation in the presence of class hierarchies: application to text categorization. In: Lamontagne L, Marchand M (eds) Advances in artificial intelligence. Springer, Berlin, pp 395–406Google Scholar
  49. 49.
    Kosmopoulos A, Partalas I, Gaussier E, Paliouras G, Androutsopoulos I (2015) Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Min Knowl Discov 29(3):820–865MathSciNetCrossRefGoogle Scholar
  50. 50.
    Miller DRH, Leek T, Schwartz RM (1999) A hidden Markov model information retrieval system. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 214–221Google Scholar
  51. 51.
    Lavrenko V, Croft WB (2001) Relevance based language models. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 120–127Google Scholar
  52. 52.
    Liu X, Croft WB (2004) Cluster-based retrieval using language models. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 186–193Google Scholar
  53. 53.
    Cao Y, Liu J, Bao S, Li H (2005) Research on expert search at enterprise track of TREC 2005. In: TRECGoogle Scholar
  54. 54.
    Zhang J, Tang J, Li J (2007) Expert finding in a social network. In: International conference on database systems for advanced applications. Springer, pp 1066–1069Google Scholar
  55. 55.
    Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  56. 56.
    Kolda TG, Sun J (2008) Scalable tensor decompositions for multi-aspect data mining. In: Eighth IEEE International conference on data mining, 2008. ICDM’08. IEEE, pp 363–372Google Scholar
  57. 57.
    Huang PS, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 2333–2338Google Scholar
  58. 58.
    Ponte JM, Croft WB (1998) A language modeling approach to information retrieval. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 275–281Google Scholar
  59. 59.
    Liu X, Bollen J, Nelson ML, de Sompel HV (2005) Co-authorship networks in the digital library research community. Inf Process Manag 41(6):1462–1480CrossRefGoogle Scholar
  60. 60.
    Li L, Shang Y, Zhang W (2002) Improvement of hits-based algorithms on web documents. In: Proceedings of the 11th international conference on World Wide Web, WWW ’02. ACM, New York, pp 527–535Google Scholar
  61. 61.
    Zhang M, Song R, Lin C, Ma S, Jiang Z, Jin Y, Liu Y, Zhao L, Ma S (2003) Expansion-based technologies in finding relevant and new information: thu trec 2002: novelty track experiments. NIST Spec Publ SP 251:586–590Google Scholar
  62. 62.
    Chang S, Pal A (2013) Routing questions for collaborative answering in community question answering. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, pp 494–501Google Scholar
  63. 63.
    Balog K, Azzopardi L, De Rijke M (2006) Formal models for expert finding in enterprise corpora. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 43–50Google Scholar
  64. 64.
    Yeniterzi R (2016) Effective and efficient approaches to retrieving and using expertise in social media. SIGIR Forum 49(2):152– 153CrossRefGoogle Scholar
  65. 65.
    Wang L, Wu B, Yang J, Peng S (2016) Personalized recommendation for new questions in community question answering. In: Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining. ASONAM, pp 901–908Google Scholar
  66. 66.
    Riahi F, Zolaktaf Z, Shafiei M, Milios E (2012) Finding expert users in community question answering. In: Proceedings of the 21st international conference on world wide web. WWW ’12 Companion. ACM, New York, pp 791–798Google Scholar
  67. 67.
    Liu J, Jia B, Xu H, Liu B, Gao D, Li B (2017) A topicrank based document priors model for expert finding. In: Fei M, Ma S, Li X, Sun X, Jia L, Su Z (eds) Advanced computational methods in life system modeling and simulation. Springer, Singapore, pp 334–341Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations