Abstract
IT support is a vital and integral part of technology adoption. Conventionally, IT support service providers heavily rely on human effort and expertise to respond to user queries. Given the cost-benefit and 24 \(\times \) 7 availability for answering user questions, Virtual Assistants (VA) are highly applicable in the technical support domain. In this paper, we describe a novel methodology for building interactive virtual assistants for IT support using Dynamic Faceted Search (DFS). Given a question, dynamic facets are generated automatically, enabling the user to refine and narrow down their intent. To do so we leverage knowledge automatically induced from textual content and existing Semantic Web resources such as Wikidata. Such knowledge is then used to dynamically generate facets interactively based on the user’s responses as shown in the demo video (https://ibm.box.com/v/iswc2020-dfs). The experiments on two real-world datasets in the IT support domain show the effectiveness of DFS in refining the user’s queries and efficiently identifying possible solutions to their technical problems.
N. Mihindukulasooriya and R. Mahindru—Equal contributions.
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Notes
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A VA capable of fully automatic dialog generation is out of scope for DFS in the context of this work.
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For example, interactive problem diagnosis containing test and action steps; and process automation to invoke enterprise endpoints. for common questions.
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K = 5 in our experiments.
- 7.
0.41– 0.60 is considered as moderate [12].
References
Aliannejadi, M., Zamani, H., Crestani, F., Croft, W.B.: Asking clarifying questions in open-domain information-seeking conversations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2019, pp. 475–484 (2019)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
Belkin, N.J.: Anomalous states of knowledge as a basis for information retrieval (1980)
Castelli, V., et al.: The TechQA Dataset. ArXiv abs/1911.02984 (2019)
Chowdhury, M.F.M., Farrell, R.: An efficient approach for super and nested term indexing and retrieval. CoRR abs/1905.09761 (2019). http://arxiv.org/abs/1905.09761
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., Zhou, M.: Superagent: a customer service chatbot for e-commerce websites. In: ACL : System Demonstrations (2017)
Dash, D., Rao, J., Megiddo, N., Ailamaki, A., Lohman, G.: Dynamic faceted search for discovery-driven analysis. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (2008)
Dash, S., Chowdhury, M.F.M., Gliozzo, A., Mihindukulasooriya, N., Fauceglia, N.R.: Hypernym detection using strict partial order networks. In: AAAI (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL 2019 (2019)
Fauceglia, N.R., Gliozzo, A., Dash, S., Chowdhury, M.F.M., Mihindukulasooriya, N.: Automatic taxonomy induction and expansion. In: EMNLP : System Demonstrations (2019)
Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: COLING (1992)
McHugh, M.L.: Interrater reliability: the kappa statistic. Biochemia Med. Biochemia Med. 22(3), 276–282 (2012)
Radlinski, F., Craswell, N.: A theoretical framework for conversational search. In: CHIIR 2017, pp. 117–126. ACM (2017)
Safran, N.: Psychology of the searcher, April 2015. https://www.immagic.com/eLibrary/ARCHIVES/GENERAL/BNILE_US/B150428S.pdf
Tunkelang, D.: Faceted search. Synth. Lect. Inf. Concepts Retrieval Serv. 1(1), 1–80 (2009)
Usbeck, R., Gusmita, R.H., Ngomo, A.N., Saleem, M.: 9th challenge on question answering over linked data. In: ISWC 2018. CEUR, vol. 2241, pp. 58–64 (2018)
Vakulenko, S.: Knowledge-based conversational search. CoRR abs/1912.06859 (2019). http://arxiv.org/abs/1912.06859
Vakulenko, S., de Rijke, M., Cochez, M., Savenkov, V., Polleres, A.: Measuring semantic coherence of a conversation. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 634–651. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_37
Yang, L., et al.: Response ranking with deep matching networks and external knowledge in information-seeking conversation systems. In: SIGIR 2018, pp. 245–254 (2018)
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Mihindukulasooriya, N. et al. (2020). Dynamic Faceted Search for Technical Support Exploiting Induced Knowledge. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_42
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