Abstract
Community-based question answering (CQA) such as Stack Overflow and Quora face the challenge of providing unsolved questions with high expertise users to obtain high quality answers, which is called question routing. Many existing methods try to tackle this by learning user model from structure and topic information, which suffer from the sparsity issue of CQA data. In this paper, we propose a novel question routing method from the viewpoint of knowledge graph embedding. We integrate topic representations with network structure into a unified Knowledge Graph Question Routing framework, named as KGQR. The extensive experiments carried out on Stack Overflow data suggest that KGQR outperforms other state-of-the-art methods.
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Aslay, Ç., O’Hare, N., Aiello, L.M., Jaimes, A.: Competition-based networks for expert finding. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1033–1036. ACM (2013)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 866–874. ACM (2008)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Guo, J., Xu, S., Bao, S., Yu, Y.: Tapping on the potential of q&a community by recommending answer providers. In: Proceedings of the 17th ACM conference on Information and knowledge management, pp. 921–930. ACM (2008)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 919–922. ACM (2007)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187 (2015)
Liu, D.R., Chen, Y.H., Kao, W.C., Wang, H.W.: Integrating expert profile, reputation and link analysis for expert finding in question-answering websites. Inf. Process. Manag. 49(1), 312–329 (2013)
Liu, J., Song, Y.I., Lin, C.Y.: Competition-based user expertise score estimation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 425–434. ACM (2011)
San Pedro, J., Karatzoglou, A.: Question recommendation for collaborative question answering systems with RankSLDA. In: Proceedings of the 8th ACM Conference on Recommender systems. pp. 193–200. ACM (2014)
Van Dijk, D., Tsagkias, M., De Rijke, M.: Early detection of topical expertise in community question answering. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 995–998. ACM (2015)
Xu, F., Ji, Z., Wang, B.: Dual role model for question recommendation in community question answering. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 771–780. ACM (2012)
Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., Chen, Z.: CQArank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 99–108. ACM (2013)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, pp. 221–230. ACM (2007)
Zhao, Z., Wei, F., Zhou, M., Ng, W.: Cold-start expert finding in community question answering via graph regularization. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9049, pp. 21–38. Springer, Cham (2015). doi:10.1007/978-3-319-18120-2_2
Zhao, Z., Yang, Q., Cai, D., He, X., Zhuang, Y.: Expert finding for community-based question answering via ranking metric network learning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 3000–3006. AAAI Press (2016)
Zhu, H., Chen, E., Xiong, H., Cao, H., Tian, J.: Ranking user authority with relevant knowledge categories for expert finding. World Wide Web 17(5), 1081–1107 (2014)
Acknowledgments
The research was supported in part by National Basic Research Program of China (973 Program, No. 2013CB329605) and National Natural Science Foundation of China (NSFC No. 61370136).
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Liu, Z., Li, K., Qu, D. (2017). Knowledge Graph Based Question Routing for Community Question Answering. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_73
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DOI: https://doi.org/10.1007/978-3-319-70139-4_73
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