Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm
Subject Areas : Expert Systems
Mojtaba Sharifian
1
,
Neda Abdolvand
2
,
Saeedeh Rajaee Harandi
3
1 - Qazvin Branch, Islamic Azad University
2 - Alzahra University
3 - Alzahra University
Keywords: Online Communities, Experts Finding , Ant Colony Algorithm, Word Net,
Abstract :
Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user’s questions with the intended domain. The proposed algorithm determines the level of people’s expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community
[1] T., Lappas, K., Liu, & E, Terzi. “A survey of algorithms and systems for expert location in social networks.” In Social Network Data Analytics (pp. 215-241). Springer, Boston, MA. 2011.
[2] A., Kardan, A., Omidvar and F., Farahmandnia "Expert finding on social network with link analysis approach." In 2011 19th Iranian Conference on Electrical Engineering, pp. 1-6. IEEE, 2011.
[3] A., El-Korany. “Integrated Expert Recommendation Model For Online Communities.” Arxiv preprint arxiv: 1311.3394. 201.
[4] A., Daud, M., Ahmad, M. S. I., Malik & D., Che. “Using machine learning techniques for rising star prediction in co-author network.” Scientometrics, Vol. 102, No. 2. 2015. pp. 1687-1711.
[5] L. A., Adamic, J., Zhang, E., Bakshy, & M. S., Ackerman. “Knowledge sharing and yahoo answers: everyone knows something.” In Proceedings of the 17th international conference on World Wide Web (pp. 665-674). ACM. 2008, April.
[6] H., Gui, Q., Zhu, L., Liu, A., Zhang, & J., Han. “Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings.” arXiv preprint arXiv: 1803.03370. 2018.
[7] M., Neshati, Z., Fallahnejad, & H., Beigy. “On dynamicity of expert finding in community question answering.” Information Processing & Management, Vol. 53, No. 5. 2017. pp. 1026-1042.
[8] X., Ding, B., Liu & P. S., Yu “A holistic lexicon-based approach to opinion mining.” In Proceedings of the 2008 international conference on web search and data mining (pp. 231-240). ACM. 200.
[9] M., Karimzadehgan, R. W., White & M., Richardson. “Enhancing expert finding using organizational hierarchies.” In European Conference on Information Retrieval (pp. 177-188). Springer, Berlin, Heidelberg. 2009.
[10] S. B., Sriramoju. “Heat Diffusion Based Search for Experts on World Wide Web.” International Journal of Science and Research (IJSR), https://www. Ijsr. Net/archive/v6i11/v6i11. Php, Vol. 6, No. 11. 2017. pp. 632-635.
[11] X., Liu, W. B., Croft & M., Koll. “Finding experts in community-based question-answering services.” In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 315-316). ACM. 2005, October.
[12] S., Ravi, B., Pang, V., Rastogi & R., Kumar. “Great Question! Question Quality in Community Q&A.” ICWSM, Vol.14, No. 2014. pp. 426-435.
[13] P., Brusilovsky, & E., Millán. “User models for adaptive hypermedia and adaptive educational systems.” In The adaptive web (pp. 3-53). Springer, Berlin, Heidelberg. 2007.
[14] K., Stephens-Martinez, M. A., Hearst & A., Fox. “Monitoring moocs: which information sources do instructors value?” In Proceedings of the first ACM conference on Learning@ scale conference, (pp. 79-88). ACM. 2014.
[15] S., Yuan, Y., Zhang, J., Tang, W., Hall, & J. B., Cabotà. “Expert finding in community question answering: a review.” Artificial Intelligence Review, Vol. 53, No. 2, 2020, pp. 843-874.
[16] M., Dadkhaha, M., Lagziana, F., Rahim-niaa, & K., Kimiafar. “The potential of business intelligence tools for expert finding.” Journal of Intelligence Studies in Business, Vol. 9, No. 2. 2019. pp. 82-95.
[17] J., Zhang, J., Tang & J., Li. “Expert finding in a social network.” In International Conference on Database Systems for Advanced Applications, (pp. 1066-1069). Springer, Berlin, Heidelberg. 2007.
[18] Z., Zhao, L., Zhang, X., He & W., Ng. “Expert finding for question answering via graph regularized matrix completion.” IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 4. 2015. pp. 993-1004.
[19] P., Cifariello, P., Ferragina, & M., Ponza. “Wiser: A semantic approach for expert finding in academia based on entity linking.” Information Systems, Vol. 82 No.2019, 2019. pp. 1-16.
[20] T. V., Rampisela, & E., Yulianti. “Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI. In 2020 8th International Conference on Information and Communication Technology (ICoICT) pp. 1-6. IEEE. (2020, June).
[21] A., Omidvar, M., Garakani, & H. R., Safarpour. “Context based user ranking in forums for expert finding using WordNet dictionary and social network analysis.” Information Technology and Management, Vol. 15, No. 1. 2014. pp. 51-63.
[22] F., Yupeng, R., Xiang, Y., Liu, M., Zhang, and Sh., Ma. "Finding experts using social network analysis." In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 77-80. IEEE Computer Society, 2007.
[23] H., Ziaimatin, T., Groza, G., Bordea, P., Buitelaar, & J., Hunter. “Expertise Profiling in Evolving Knowledge curation Platforms.” GSTF Journal on Computing (JoC), Vol. 2, No. 3. 2018. pp. 118-127.
[24] G., Zhou, S., Lai, K., Liu & J., Zhao. “Topic-sensitive probabilistic model for expert finding in question answer communities.” In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 1662-1666). ACM. 2012.
[25] M., Rafiei & A. A., Kardan. “A novel method for expert finding in online communities based on concept map and PageRank.” Human-centric computing and information sciences, Vol.5, No. 1. 2015. pp. 10-28.
[26] H., Li, S., Jin & L. I., Shudong. “A hybrid model for experts finding in community question answering.” In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2015 International Conference on (pp. 176-185). IEEE. 2015.
[27] G. A., Wang, J., Jiao, A. S., Abrahams, W., Fan & Z., Zhang. “Expert Rank: A topic-aware expert finding algorithm for online knowledge communities.” Decision Support Systems, Vol. 54, No. 3. 2013. pp. 1442-1451.
[28] Z., Dorrani, & M. S., Mahmoodi “Noisy images edge detection: Ant colony optimization algorithm.” Journal of AI and Data Mining, Vol 4, No 1. 2016. pp. 77-83.
[29] M. B., Dowlatshahi & V., Derhami. “Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search.” Journal of AI and Data Mining, Vol. 5, No 2. 2017. pp. 169-18.
[30] G., Anuradha, G. L., Devi & M. P., Babu. “Antrank: An Ant Colony Algorithm for Ranking Web Pages.” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 3, No. 2. 2014. pp. 208-212.
[31] S., Setayesh, A., Harounabadi & A. M., Rahmani. “Presentation of an Extended Version of the PageRank Algorithm to Rank Web Pages Inspired by Ant Colony Algorithm.” International Journal of Computer Applications, Vol. 85, No. 17. 2014. pp. 7-10.
[32] D. R., Liu, Y. H., Chen, W. C., Kao & H. W., Wang. “Integrating expert profile, reputation and link analysis for expert finding in question-answering websites.” Information processing & management, Vol. 49, No. 1. 2013. pp. 312-329.
[33] L., Silva, L., Goel & E., Mousavidin. “Exploring the dynamics of blog communities: the case of MetaFilter.” Information Systems Journal, Vol. 19, No.1. 2009. pp. 55-81.
[34] S. H., Hashemi, M., Neshati, & H., Beigy. “Expertise retrieval in bibliographic network: a topic dominance learning approach.” In Proceedings of the 22nd ACM international conference on Information & Knowledge Management (2013, October), pp. 1117-1126.
[35] R., Navigli. “Word sense disambiguation: A survey.” ACM Computing Surveys (CSUR), Vol. 41, No.2. 2009. pp. 10-79.
[36] M., Rowe. “Mining User Development Signals for Online Community Churner Detection.” ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 10, No. 3, 2016. pp. 1-29.
[37] M., Neshati, D., Hiemstra, E., Asgari, & H., Beigy. „Integration of scientific and social networks.” World Wide Web, Vol. 17, No. 5, 2014. pp. 1051-1079.
[38] A. D., Well & J. L., Myers. Research design & statistical analysis. Psychology Press. USA. 2003.
[39] D., Petkova & W. B., Croft “Hierarchical language models for expert finding in enterprise corpora.” International Journal on Artificial Intelligence Tools, Vol. 17, No.1. 2008. pp. 5-18.
