Abstract
Document priors that encode our prior knowledge about the importance of different documents are essential to an expert finding system. This study proposed a TopicRank-based document priors model for expert finding. TopicRank algorithm is an extension of the DocRank algorithm. Latent dirichlet allocation was used to extract topics of the documents. We assumed there was a link between two documents that share common topics. Link analysis techniques were then used to obtain document priors. The proposed model was evaluated using the CSIRO Enterprise Research Collection and the results showed that the performance of the expert finding system was dramatically improved by introducing TopicRank-based document priors. In particular, Mean Average Precision increased 19.9% while Mean Reciprocal Rank rose as much as 23.4%.
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References
Krisztian, B., Leif, A., Maarten, D.R.: A language modeling framework for expert finding. Inf. Process. Manage. 45, 1–19 (2009)
Li, M., Liu, L., Li, C.-B.: An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems. Expert Syst. Appl. 38, 8586–8596 (2011)
Guan, Z., Miao, G., McLoughlin, R., Yan, X., Cai, D.: Co-occurrence based diffusion for expert search on the web. IEEE Trans. Knowl. Data Eng. 25(5), 1–16 (2012)
Fang, Y., Si, L., Mathur, A.: Discriminative probabilistic models for expert search in heterogeneous information sources. Inf. Retrieval 14, 158–177 (2011)
Yang, K.-W., Huh, S.-Y.: Automatic expert identification using a text categorization technique in knowledge management systems. Expert Syst. Appl. 34, 1445–1455 (2008)
Balog, K., Rijke, M.D.: Combining candidate and document models for expert search. In: Proceedings of the Seventeenth Text Retrieval Conference (TREC 2008), NIST (2008)
Balog, K.: People search in the enterprise. Ph.D. University of Amsterdam, Amsterdam (2008)
Balog, K., Azzopardi, L., Rijke, M.D.: 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, pp. 43–50. ACM, Seattle (2006)
Bordea, G.: Concept extraction applied to the task of expert finding. In: Aroyo, L., Antoniou, G., Hyvönen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6089, pp. 451–456. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13489-0_42
Daud, A., Li, J., Zhou, L., Muhammad, F.: Temporal expert finding through generalized time topic modeling. Knowl. Based Syst. 23, 615–625 (2010)
Deng, H., King, I., Lyu, M.R.: Formal models for expert finding on DBLP bibliography data. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 163–172 (2008)
Jiang, P., Yang, Q., Zhang, C., Niu, Z., Fu, H.: A probability model for related entity retrieval using relation pattern. In: Xiong, H., Lee, W.B. (eds.) KSEM 2011. LNCS, vol. 7091, pp. 318–330. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25975-3_28
Agerri, R., Granados, R., García Serrano, A.: Enrichment of named entities for image photo retrieval. In: Detyniecki, M., García-Serrano, A., Nürnberger, A. (eds.) AMR 2009. LNCS, vol. 6535, pp. 101–110. Springer, Heidelberg (2011). doi:10.1007/978-3-642-18449-9_9
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T., McNamara, D.S., Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis, vol. 427, pp. 424–440. Erlbaum, Hillsdale (2007)
Marmanis, H., Babenko, D.: Algorithms of the Intelligent Web. Manning, Greenwich (2009)
Acknowledgement
This work is supported by the Industrial Research Project of Science and Technology in Shaanxi Province (No. 2016GY-094), the Social Development Research Project of Science and Technology in Shaanxi Province (No. 2016SF-255), and the National Natural Science Foundation of China (Li Baojuan for No. 81301199, Liu Baohong for No. 61374185).
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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. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_33
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DOI: https://doi.org/10.1007/978-981-10-6370-1_33
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