本論文提出了一套適用於問答系統的本體論架構。此方法建立的本體論能夠利用我們設計的自然語言理解框架（Natural Language Understanding）處理問答任務（Question Answering），靈活地偵測使用者的潛在意圖並且轉換成通用性的概念組合。而此本體論架構能夠適應於少量資源（low-resource）的條件，並且具有靈活的知識擴充性，可在後續開發中利用部分少量資料所抽取出的知識架構關係，輕鬆地擴充新資料與相近領域知識。
文章中還提供了三個對話系統（Dialogue System）案例來展示此本體論能靈活地使用不同的資料來抽取出使用者意圖並搭建問答任務。並且提供了多輪問答的案例來展示此系統具有靈活的知識擴充性，可以舉一反三充分地使用後續資料增加其性能。

This paper proposes an ontological framework for Question Answering systems. This ontology can be used to handle Question Answering tasks by utilizing proposed Natural language Understanding framework. Through this way, it could flexibly detect user's latent intent and transform it into accessible concepts set. The ontology framework can be adapted to Low-Resource conditions and has flexible knowledge extension. Credit to the framework, the knowledge structure relationship could be extracted from the small amount of data. And the structure can be used in subsequent development to expand new data and similar domain knowledge easily.
Also, three examples of Dialogue System are provided to demonstrate the flexibility of this ontology in this paper. We construct three separated Ontologies by using different data types and build Question Answering tasks. And We also provide multi-turns of Question Answering scenario to demonstrate the flexibility of the systemic expansion. The flexibility could let system expand its knowledge and use subsequent data to increase its ability.

[1] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee and Luke Zettlemoyer. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). p. 2227–2237, 2018.
[2] Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. Improving language understanding by generative pre-training. Technical report, 2018.
[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[4] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov and Quoc V. Le. XLNet: generalized autoregressive pretraining for language understanding. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. p. 5753-5763, 2019.
[5] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
[6] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma and Radu Soricut. ALBERT: A Lite BERT for Self- supervised Learning of Language Representations. In International Conference on Learning Representations, 2020.
[7] Yunyi Yang, Yunhao Li and Xiaojun Quan. In Proceedings of the AAAI Conference on Artificial Intelligence. 35.16: 14230-14238, 2021.
[8] Paweł Budzianowski and Ivan Vulic. It’s GPT-2-How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems. In Proceedings of the 3rd Workshop on Neural Generation and Translation. p. 15-22, 2019.
[9] Tanja Bunk, Daksh Varshneya, Vladimir Vlasov and Alan Nichol. Diet: Lightweight language understanding for dialogue systems. arXiv preprint arXiv:2004.09936, 2020.
[10] Peter Clark, John Thompson and Bruce Porter. A knowledge-based approach to question-answering. In Proceedings of the 1999 AAAI Fall Symposium on Question Answering Systems. p. 43-51,1999.
[11] Ulf Hermjakob, Eduard H. Hovy and Chin-Yew Lin. Knowledge-based question answering. In Proceedings of the Sixth World Multiconference on Systems, Cybernetics, and Informatics, 2000.
[12] Vanessa Lopez, Victoria Uren, Enrico Motta and Michele Pasin. AquaLog: An ontology-driven question answering system for organizational semantic intranets. Journal of Web Semantics, 5.2: 72-105, 2007.
[13] D.S. Wang. A domain-specific question answering system based on ontology and question templates. In 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. IEEE. p. 151-156, 2010.
[14] Kuznetsov V. A., Mochalov V. A. and Mochalova A. V. Ontological-semantic text analysis and the question answering system using data from ontology. In 2016 18th International Conference on Advanced Communication Technology. IEEE. p. 651-658, 2016.
[15] Nicola Guarino, Daniel Oberle, and Steffen Staab. What is an ontology?. In Handbook on ontologies. Springer, Berlin, Heidelberg. p. 1-17, 2009.
[16] Thomas R Gruber. A translation approach to portable ontology specifications. Knowledge acquisition, 5.2: 199-220, 1993.
[17] Wen-Lian Hsu, Shih-Hung Wu and Yi-Shiou Chen. Event identification based on the information map-INFOMAP. In 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace. IEEE. p. 1661-1666, 2001.
[18] Hong-Jie Dai, Chen-Kai Wang, Nai-Wen Chang, Ming-Siang Huang, Jitendra Jonnagaddala, Feng-Duo Wang and Wen-Lian Hsu. Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature. Database, 2019.
[19] Po-Ting Lai, Ming‑Siang Huang, Ting‑Hao Yang, Wen‑Lian Hsu and Richard Tzong‑Han Tsai. Statistical principle-based approach for gene and protein related object recognition. Journal of cheminformatics, 10.1: 1-9, 2019.
[20] Ting-Hao Yang, Yu-Lun Hsieh, Shih-Hung Liu, Yung-Chun Chang and Wen-Lian Hsu. A flexible template generation and matching method with applications for publication reference metadata extraction. Journal of the Association for Information Science and Technology, 72.1: 32-45, 2021.
[21] Hongshen Chen, Xiaorui Liu, Dawei Yin, and Jiliang Tang. A survey on dialogue systems: Recent advances and new frontiers. Acm Sigkdd Explorations Newsletter, 19.2: 25-35, 2017.
[22] Stavros Mallios and Nikolaos Bourbakis. A survey on human machine dialogue systems. In 2016 7th international conference on information, intelligence, systems & applications. IEEE, p. 1-7, 2016.
[23] Jianfeng Gao, Michel Galley and Lihong Li. Neural approaches to conversational AI. In The 41st international ACM SIGIR conference on research & development in information retrieval. p. 1371-1374, 2018.
[24] Amit Mishra and Sanjay Kumar Jain. A survey on question answering systems with classification. Journal of King Saud University-Computer and Information Sciences, 28.3: 345-361, 2016.
[25] Marco Antonio Calijorne Soares and Fernando Silva Parreiras. A literature review on question answering techniques, paradigms and systems. Journal of King Saud University-Computer and Information Sciences, 32.6: 635-646, 2020.
[26] Eleftherios Dimitrakis, Konstantinos Sgontzos and Yannis Tzitzikas. A survey on question answering systems over linked data and documents. Journal of intelligent information systems, 55.2: 233-259, 2020.
[27] Wanyun Cui, Yanghua Xiao, Haixun Wang, Yangqiu Song, Seung-won Hwang and Wei Wang. KBQA: learning question answering over QA corpora and knowledge bases. arXiv preprint arXiv:1903.02419, 2019.
[28] D.S. Wang. A domain-specific question answering system based on ontology and question templates. In 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. IEEE. p. 151-156, 2010.
[29] Xuchen Yao and Benjamin Van Durme. Information extraction over structured data: Question answering with freebase. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). p. 956-966, 2014.
[30] Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su and Xifeng Yan. Table cell search for question answering. In Proceedings of the 25th International Conference on World Wide Web. p. 771-782, 2016.
[31] Jonathan Herzig, Thomas Müller, Syrine Krichene and Julian Martin Eisenschlos. Open domain question answering over tables via dense retrieval. arXiv preprint arXiv:2103.12011, 2021.
[32] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang and Luke Zettlemoyer. QuAC: Question answering in context. arXiv preprint arXiv:1808.07036, 2018.
[33] Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang and Ming Zhou. Gated self-matching networks for reading comprehension and question answering. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). p. 189-198, 2017.
[34] Jiang Bian, Eugene Agichtein, Yandong Liu and Hongyuan Zha. Finding the right facts in the crowd: factoid question answering over social media. In Proceedings of the 17th international conference on World Wide Web. p. 467-476, 2008.
[35] Mohit Iyyer,Jordan Boyd-Graber, Leonardo Claudino, Richard Socher and Hal Daum´e III. A neural network for factoid question answering over paragraphs. In Proceedings of the 2014 conference on empirical methods in natural language processing. p. 633-644, 2014.
[36] Bing Liu and Ian Lane. Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv preprint arXiv:1609.01454, 2016.
[37] Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu and Yun-Nung Chen. Slot-gated modeling for joint slot filling and intent prediction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). p. 753-757, 2018.
[38] Janez Brank, Marko Grobelnik and Dunja Mladenić. A survey of ontology evaluation techniques. In Proceedings of the conference on data mining and data warehouses. p. 166-170, 2005.
[39] Hlomani Hlomani and Deborah Stacey. STACEY, Deborah. Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: A survey. Semantic Web Journal, 1.5: 1-11, 2015
[40] Joe Raad and Christophe Cruz. A survey on ontology evaluation methods. In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, part of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015.
[41] Alexander Ulanov, Georgy Shevlyakov, Nikolay Lyubomishchenko, Pankaj Mehra and Vladimir Polutin. Monte Carlo study of taxonomy evaluation. In 2010 Workshops on Database and Expert Systems Applications. IEEE. p. 164-168, 2010.
[42] Christopher Brewster, Harith Alani, Srinandan Dasmahapatra and YorickWilks. Data driven ontology evaluation. In 4th International Conference on Language Resources and Evaluation. p. 641-644, 2004.
Peng-Hsuan Li, Tsu-Jui,Fu and Wei-Yun Ma. Why attention? Analyze BiLSTM deficiency and its remedies in the case of NER. In Proceedings of the AAAI Conference on Artificial Intelligence. p. 8236-8244, 2020.
[44] Thomas Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. p. 50-57, 1999.