科技突破時間、空間的限制，人與人互動交流的維度更廣泛。興起的社交網路平台，成為新形態的社交媒介。其中，直播平台互動最為即時且具有挑戰，現場即時播出節目內容且利用聊天室能立即與觀眾互動對談。直播節目無法後製剪輯與標記輔助資訊，瞬時又大量的資訊內容，對參與者產生龐大的資訊壓力。本研究以「料理」為例，解決直播主與觀眾資訊過載與減少直播過程訊息斷鏈情況，協助直播互動過程更為流暢且提高觀眾的滿意度。提供直播內容標記輔助系統與問答系統，解決上述問題也能增加觀眾持續觀看的意願與滿意度。
研究分為四個階段：(1)建立料理知識本體論、(2)擴充料理知識本體論、(3)料理知識本體論與直播影片連結與(4)料理知識庫建立問答系統。料理知識本體論由自然語言處理食譜文本資料來建立，經由語意擴充與詞向量擴充後，知識涵蓋更廣泛。實驗結果證明對標記直播輔助資訊準確率有顯著提升。此外，料理知識庫建立問答系統即時提供資訊查詢，補足資訊缺口。問答系統經實驗驗證後，準確率與使用滿意度較高於其他系統。

Technology breaks through the limitations of time and space. The interaction channels between people become broader. The emerging social network platform has become a new form of social media. In particular, the live streaming is getting popular and poses challenges to the broadcasters. The live video content is broadcast on the spot and the chatroom can be used to interact with the audience immediately. The live program cannot be post-edited and mark auxiliary information. In addition, the amount of information content creates a huge information pressure on the participants. This thesis uses "cooking" as an example live streaming application to help streamline the interactive process of live streaming and increase audience satisfaction. We aim to provide auxiliary information and question answering system (QA system) for live streaming programs to fill the information gap imposed in the live streaming process in the hope to reduce the pressure of information overload. Solving the above problems can also increase the audience's willingness and satisfaction of continuous watching.
The research is divided into four stages: (1) establishing cooking ontology, (2) expanding cooking ontology, (3) mapping with cooking ontology and stream clips, and (4) establishing question answering system. The cooking ontology is established from recipe text data by NLP. The cooking ontology is expanded on semantic expansion and word-to-vector expansion. The knowledge covers a wider range after expansions. The experimental results show that the accuracy of the auxiliary information of the marking step during the live streaming has been significantly improved. In addition, the cooking ontology enable a QA system to provide real-time information to fill up the information gap. The QA system has been verified via user studies, and its accuracy and user satisfaction are higher than other systems.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章、緒論 1
第一節、研究背景 1
第二節、研究目的 2
第三節、研究貢獻 3
第四節、文章架構 4
第二章、文獻回顧 5
第一節、自然語言建立知識庫 5
第二節、擴充知識庫 7
第三節、料理知識庫應用與料理影像標記 8
第四節、問答系統 11
第三章、研究架構 17
第一節、研究架構 17
第二節、研究流程 18
第四章、料理知識本體論建立、表達及擴張 21
第一節、建立料理知識本體論 21
第二節、擴充料理知識本體論 33
第三節、知識本體論建構評比 36
第五章、知識本體論與直播內容對應方法與成效 43
第一節、直播內容標記 43
第二節、對應方法成效評估 46
第六章、知識庫問答系統建立 52
第一節、定義問題類型與回應範圍 53
第二節、建立問答系統 56
第三節、知識庫建立訓練問答集 58
第四節、問題意圖分類成效評估 65
第五節、問答系統效用與系統滿意度 68
第七章、結論與未來展望 75
第一節、結論 75
第二節、研究限制 76
第三節、未來展望 77
參考文獻 79
附錄一、問答系統實驗畫面 94
附錄二、問答系統滿意度問卷 97
附錄三、QA benchmark dataset 101
附錄四、食物、料理相關知識庫與詞向量 106

Abbas Naqvi, M. H., Jiang, Y., Miao, M., & Naqvi, M. H. (2020). The effect of social influence, trust, and entertainment value on social media use: evidence from Pakistan. Cogent Business & Management, 7(1), 1723825.
Abu-Salih, B. (2021). Domain-specific knowledge graphs: A survey. Journal of Network & Computer Applications, 185, 103076.
Al-Aswadi, F. N., Chan, H. Y., & Gan, K. H. (2020). Automatic ontology construction from text: a review from shallow to deep learning trend. Artificial Intelligence Review, 53(6), 3901-3928.
Alshargi, F., Shekarpour, S., Soru, T., & Sheth, A. (2018). Concept2vec: Metrics for evaluating quality of embeddings for ontological concepts. arXiv preprint arXiv:1803.04488.
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.
Badra, F., Cojan, J., Cordier, A., Lieber, J., Meilender, T., Mille, A., Molli, P., Nauer, E., Napoli, A., & Skaf-Molli, H. (2009). Knowledge acquisition & discovery for the textual case-based cooking system wikitaaable. In 8th International Conference on Case-Based Reasoning-ICCBR 2009, Workshop Proceedings, 249-258.
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., & Faulkner, R.(2018) Relational inductive biases, deep learning & graph networks. arXiv preprint arXiv:1806.01261.
Bettman, J. R. (1979). Memory factors in consumer choice: a review. Journal of Marketing, 43(2), 37-53.
Bick, A., Blandin, A., & Mertens, K. (2021). Work from home before and after the COVID-19 Outbreak. Federal Reserve Bank of Dallas Working Paper. https://doi.org/10.24149/wp2017r2
Bień, M., Gilski, M., Maciejewska, M., Taisner, W., Wisniewski, D., & Lawrynowicz, A. (2020, December). RecipeNLG: A cooking recipes dataset for semi-structured text generation. In Proceedings of the 13th International Conference on Natural Language Generation, 22-28.
Biermann, L., Walter, S., & Cimiano, P. (2018). A guided template-based question answering system over knowledge graphs. In Proceedings of the 21st International Conference on Knowledge Engineering and Knowledge Management.
Bisk, Y., Buys, J., Pichotta, K., & Choi, Y. (2019, June). Benchmarking hierarchical script knowledge. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4077-4085.
Bowman, S., & Willis, C. (2003). We Media: How audiences are shaping the future of news and information,The Media Center at the American Press Institute.
Buitelaar, P., Cimiano, P., & Magnini, B. (2005). Ontology learning from text: an overview. Ontology learning from text: Methods, Evaluation & Applications, 123.
Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., Lehmann, J., & Fischer, A. (2019). Introduction to neural network based approaches for question answering over knowledge graphs. arXiv preprint arXiv:1907.09361.
Chen, Y., Subburathinam, A., Chen, C. H., & Zaki, M. J. (2021, March). Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. arXiv preprint arXiv:2101.01775.
Chen, Y., Wu, L., & Zaki, M. J. (2019). Reinforcement learning based graph-to-sequence model for natural question generation. arXiv preprint arXiv:1908.04942.
Chiu, B., Pyysalo, S., Vulić, I., & Korhonen, A. (2018). Bio-SimVerb & Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. BMC Bioinformatics, 19(1), 33-33.
Kemp, S. (2021). DIGITAL 2021: GLOBAL OVERVIEW REPORT. https://datareportal.com/reports/digital-2021-global-overview-report
Cobley, B., & Boyle, D. (2020). OnionBot: A system for collaborative computational cooking. arXiv preprint arXiv:2011.05039.
Cordier, A., Gaillard, E., & Nauer, E. (2012). Man-Machine collaboration to acquire cooking adaptation knowledge for the taaable case-Based reasoning system, Proceedings of the 21st International Conference on World Wide Web, 1113-1120.
Costa, R., Lima, C., Sarraipa, J., & Jardim-Gonçalves, R. (2016). Facilitating knowledge sharing & reuse in building & construction domain: An ontology-based approach, Journal of Intelligent Manufacturing, 27(1), 263-282.
Cui, W., Xiao, Y., Wang, H., Song, Y., Hwang, S. W., & Wang, W. (2019). KBQA: learning question answering over QA corpora and knowledge bases. arXiv preprint arXiv:1903.02419.
De Clercq, M., Stock, M., De Baets, B., & Waegeman, W. (2016). Data-driven recipe completion using machine learning methods. Trends in Food Science & Technology, 49, 1-13.
De Jaegher, H., Di Paolo, E., & Gallagher, S. (2010). Can social interaction constitute social cognition?. Trends in Cognitive Sciences, 14(10), 441-447.
De Jaegher, H., Pieper, B., Clénin, D., & Fuchs, T. (2017). Grasping intersubjectivity: An invitation to embody social interaction research. Phenomenology and the Cognitive Sciences, 16(3), 491-523.
de Oliveira, N. R., Medeiros, D. S., & Mattos, D. M. (2020, October). A syntactic-relationship approach to construct well-informative knowledge graphs representation. In 4th Conference on Cloud and Internet of Things, 75-82.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K. (2018). Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805
Diefenbach, D., Lopez, V., Singh, K., & Maret, P. (2018). Core techniques of question answering systems over knowledge bases: a survey. Knowledge and Information systems, 55(3), 529-569.
Dimitrakis, E., Sgontzos, K., & Tzitzikas, Y. (2020). A survey on question answering systems over linked data and documents. Journal of Intelligent Information Systems, 55(2), 233-259.
Ding, J., Hu, W., Xu, Q., & Qu, Y. (2019, November). Leveraging frequent query substructures to generate formal queries for complex question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, 2614–2622.
Doman, K., Kuai, C. Y., Takahashi, T., Ide, I., & Murase, H. (2011, January). Video cooKing: towards the synthesis of multimedia cooking recipes. In International Conference on Multimedia Modeling, 135-145.
Dooley, D. M., Griffiths, E. J., Gosal, G. S., Buttigieg, P. L., Hoehndorf, R., Lange, M. C., ... & Hsiao, W. W. (2018). FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration. NPJ Science of Food, 2(1), 1-10.
Fabbri, A. R., Ng, P., Wang, Z., Nallapati, R., & Xiang, B. (2020). Template-based question generation from retrieved sentences for improved unsupervised question answering. arXiv preprint arXiv:2004.11892.
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., & Ruppin, E. (2001, April). Placing search in context: the concept revisited. In Proceedings of the 10th international conference on World Wide Web, 406-414.
Gao, M., Chen, F., & Wang, R. (2018). Improving medical ontology based on word embedding, Proceedings of the 6th International Conference on Bioinformatics & Computational Biology, 121-127.
Griffiths, E., Brinkman, F., Dooley, D., Hsiao, W., Buttigieg, P., & Hoehndorf, R. (2016, January). FoodON:a global farm-to-fork food ontology-the development of a universal food vocabulary. In 2016 Joint International Conference on Biological Ontology and BioCreative-Food, Nutrition, Health and Environment for the 9 Billion, ICBO-BioCreative, 1-2.
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International journal of human-computer studies, 43(5-6), 907-928.
Halevy, A., Noy, N., Sarawagi, S., Whang, S. E., & Yu, X. (2016, April). Discovering structure in the universe of attribute names. In Proceedings of the 25th International Conference on World Wide Web, 939-949.
Harper, C., & Siller, M. (2015). OpenAG: a globally distributed network of food computing. IEEE Pervasive Computing, 14(4), 24-27.
Haussmann, S., Seneviratne, O., Chen, Y., Ne’eman, Y., Codella, J., Chen, C. H., ... & Zaki, M. J. (2019, October). FoodKG: a semantics-driven knowledge graph for food recommendation. In International Semantic Web Conference, 146-162.
Helena H. Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, and Lav R. Varshney. 2020. RecipeGPT: generative pre-training based cooking recipe generation and evaluation system. In Companion Proceedings of the Web Conference 2020. Association for Computing Machinery, New York, NY, USA, 181–184. DOI:https://doi.org/10.1145/3366424.3383536
IJntema, W., Sangers, J., Hogenboom, F., & Frasincar, F. (2012). A lexico-semantic pattern language for learning ontology instances from text. Journal of Web Semantics, 15, 37-50.
Ji, Q., Qi, G., Gao, H., & Wu, T. (2018). Survey on schema induction from knowledge graphs. In China Conference on Knowledge Graph & Semantic Computing: Springer, 136-142.
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Philip, S. Y. (2020). A survey on knowledge graphs: representation, acquisition, and applications. arXiv preprint arXiv:2002.00388.
Jia, Z., Abujabal, A., Roy, R. S., Stroetgen, J., & Weikum, G. (2019). TEQUILA: Temporal Question Answering over Knowledge Bases. arXiv preprint arXiv:1908.03650.
Jain, S., & Dodiya, T. (2014, December). Rule based architecture for medical question answering system. In Proceedings of the Second International Conference on Soft Computing for Problem Solving, 1225-1233.
Kamble, S., & Baskar, S. (2018). Learning to classify marathi questions and identify answer type using machine learning technique. Advances in Machine Learning and Data Science: Recent Achievements and Research Directives, 705, 33.
Katz, E., Blumler, J., & Gurevitch, M. (1974). Uses and gratification theory. Public Opinion Quarterly, 37(4), 509-523.
Khilji, A. F. U. R., Manna, R., Laskar, S. R., Pakray, P., Das, D., Bandyopadhyay, S., & Gelbukh, A. (2020). Question classification and answer extraction for developing a cooking QA system. Computación y Sistemas, 24(2).
Khilji, A. F. U. R., Manna, R., Laskar, S. R., Pakray, P., Das, D., Bandyopadhyay, S., & Gelbukh, A. (2021). CookingQA: answering questions & recommending recipes based on ingredients. Arabian Journal for Science & Engineering, 46(4), 3701-3712.
Kim, S., Lee, G., Lee, S. Y., Lee, S., & Lee, J. (2019, October). Game or live streaming?: motivation and social experience in live mobile quiz shows. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 87-98.
Kondylakis, H., Tsirigotakis, D., Fragkiadakis, G., Panteri, E., Papadakis, A., Fragkakis, A., ... & Papadakis, N. (2020). R2D2: A DBpedia chatbot using triple-pattern like queries. Algorithms, 13(9), 217.
Seljak, B. K., Korošec, P., Eftimov, T., Ocke, M., van der Laan, J., Roe, M., ... & Finglas, P. (2018). Identification of requirements for computer-supported matching of food consumption data with food composition data. Nutrients, 10(4).
Lan, Y., He, G., Jiang, J., Jiang, J., Zhao, W. X., & Wen, J. R. (2021). A survey on complex knowledge base question answering: methods, Challenges and Solutions. arXiv preprint arXiv:2105.11644.
Lange, M. C., Lemay, D. G., & German, J. B. (2007). A multi‐ontology framework to guide agriculture and food towards diet and health. Journal of the Science of Food and Agriculture, 87(8), 1427-1434.
Lee, C.-S., Kao, Y.-F., Kuo, Y.-H., & Wang, M.-H. (2007). Automated ontology construction for unstructured text documents. Data & Knowledge Engineering, 60(3), 547-566.
Lee, H. H., Shu, K., Achananuparp, P., Kokoh Prasetyo, P., Liu, Y., Lim, E. P., & Varshney, L. R. (2020). RecipeGPT: generative pre-training based cooking recipe generation and evaluation system. In Companion Proceedings of the Web Conference 2020, 181–184.
Lee, S. E., Choi, M., & Kim, S. (2019). They pay for a reason! The determinants of fan’s instant sponsorship for content creators. Telematics & Informatics, 45, 101286.
Li, L., Wang, P., Yan, J., Wang, Y., Li, S., Jiang, J., ... & Liu, Y. (2020). Real-world data medical knowledge graph: construction and applications. Artificial intelligence in medicine, 103, 101817.
Liang, S., Stockinger, K., de Farias, T. M., Anisimova, M., & Gil, M. (2021). Querying knowledge graphs in natural language. Journal of Big Data, 8(1), 1-23.
Lilian, J. F., Sundarakantham, K., & Shalinie, S. M. (2021). Anti-negation method for handling negation words in question answering system. The Journal of Supercomputing, 77(5), 4244-4266.
Lima, R., Espinasse, B., & Freitas, F. (2018). OntoILPER: an ontology-and inductive logic programming-based system to extract entities and relations from text. Knowledge and Information Systems, 56(1), 223-255.
Liu, C., Li, X., Zhao, D., Guo, S., Kang, X., Dong, L., & Yao, H. (2019, November). A-gnn: Anchors-aware graph neural networks for node embedding. In International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, 141-153.
Ma, C., & Molnár, B. (2020, March). Use of ontology learning in information system integration: a literature survey. In Asian Conference on Intelligent Information and Database Systems, 342-353.
Manna, R., Das, D., & Gelbukh, A. (2021). Question-Answering & recommendation system on cooking recipes. Computación y Sistemas, 25(1).
Marin, J., Biswas, A., Ofli, F., Hynes, N., Salvador, A., Aytar, Y., ... & Torralba, A. (2019). Recipe1m+: A dataset for learning cross-modal embeddings for cooking recipes & food images. IEEE transactions on pattern analysis & machine intelligence, 43(1), 187-203.
Mehrad, J., & Tajer, P. (2016). Uses and gratification theory in connection with knowledge and information science: A proposed conceptual model. International Journal of Information Science and Management, 14(2).
Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019). Howto100m: learning a text-video embedding by watching hundred million narrated video clips, Proceedings of the IEEE International Conference on Computer Vision, 2630-2640.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 3111-3119.
Min, W., Jiang, S., Liu, L., Rui, Y., & Jain, R. (2019). A survey on food computing. ACM Computing Surveys, 52(5), 1-36.
Mishra, S., & Jain, S. (2019). An intelligent knowledge treasure for military decision support. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 14(3), 55-75.
Mujtaba, D. F., & Mahapatra, N. R. (2020). Towards natural language understanding of procedural text using recipes. In Progress in Computing, Analytics and Networking, 359-367.
Musina, O., Putnik, P., Koubaa, M., Barba, F. J., Greiner, R., Granato, D., & Roohinejad, S. (2017). Application of modern computer algebra systems in food formulations & development: a case study. Trends in Food Science & Technology, 100(64), 48-59.
Nematzadeh, A., Ciampaglia, G. L., Ahn, Y. Y., & Flammini, A. (2019). Information overload in group communication: from conversation to cacophony in the twitch chat. Royal Society Open Science, 6(10), 191412.
Nicholson, D. N., & Greene, C. S. (2020). Constructing knowledge graphs and their biomedical applications. Computational and Structural Biotechnology Journal, 18, 1414-1428.
Oh, K.-J., Hong, M.-D., Yoon, U.-N., & Jo, G. (2016). Automatic generation of interactive cooking video with semantic annotation, Journal of Universal Computer Science, 22(6), 742-760.
Rehurek, R., &Sojka, P. (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45-50.
Peng, S. L., Cherng, B. L., & Chen, H. C. (2013). The effects of classroom goal structures on the creativity of junior high school students. Educational Psychology, 33(5), 540-560.
Qu, Y., Liu, J., Kang, L., Shi, Q., & Ye, D. (2018). Question answering over freebase via attentive RNN with similarity matrix based CNN. arXiv preprint arXiv:1804.03317, 38.
Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks.
Ribeiro, R., Batista, F., Pardal, J. P., Mamede, N. J., & Pinto, H. S. (2006, September). Cooking an ontology. In International Conference on Artificial Intelligence: Methodology, Systems, and Applications (pp. 213-221).
Rodrigues, H. P. (2017). Learning semantic patterns for question generation & question answering. [Unpublished PhD's thesis]. Universidade de Lisboa. https://www.lti.cs.cmu.edu/sites/default/files/rodrigues%2C%20hugo%20-%20CMU-LTI-20-013.pdf
Wijanarko, B. D., Heryadi, Y., Toba, H., & Budiharto, W. (2021). Question generation model based on key-phrase, context-free grammar, and Bloom’s taxonomy. Education and Information Technologies, 26(2), 2207-2223.
Rousseau, S. (2012). Food & social media: you are what you tweet. Lanham, MD: Rowaan & Littlefield Publishers.
Saha, A., Pahuja, V., Khapra, M. M., Sankaranarayanan, K., & Chandar, S. (2018, April). Complex sequential question answering: towards learning to converse over linked question answer pairs with a knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
Salvador, A., Hynes, N., Aytar, Y., Marin, J., Ofli, F., Weber, I., & Torralba, A. (2017). Learning cross-modal embeddings for cooking recipes and food images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3020-3028.
Sener, F., Saraf, R., & Yao, A. (2021). Learning video models from text: zero-shot anticipation for procedural actions. arXiv preprint arXiv:2106.03158.
Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., & Bengio, Y. (2016). Generating factoid questions with recurrent neural networks: the 30m factoid question-answer corpus. arXiv preprint arXiv:1603.06807.
Shamsfard, M., & Barforoush, A. A. (2004). Learning ontologies from natural language texts. International journal of Human-Computer Studies, 60(1), 17-63.
Shidochi, Y., Takahashi, T., Ide, I., &Murase, H. (2009). Finding replaceable materials in cooking recipe texts considering characteristic cooking actions. In Proceedings of the ACM Multimedia 2009 Workshop on Multimedia for Cooking and Eating Activities, 9–14.
Song, L., Wang, A., Su, J., Zhang, Y., Xu, K., Ge, Y., & Yu, D. (2021). Structural information preserving for graph-to-text generation. arXiv preprint arXiv:2102.06749.
Song, L., Wang, Z., Hamza, W., Zhang, Y., & Gildea, D. (2018, June). Leveraging context information for natural question generation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2, 569-574.
Starc, J., & Mladenić, D. (2017). Joint learning of ontology & semantic parser from text, Intelligent Data Analysis, 21(1), 19-38.
Striapunina, K. (2021). Digital media report-video-on-demand 2021. Statista, Statista Digital Market Outlook-Market Report (ed.), Hamburg.
Sun, X., Liu, J., Lyu, Y., He, W., Ma, Y., & Wang, S. (2018). Answer-focused and position-aware neural question generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 3930-3939.
Tang, D., Duan, N., Qin, T., Yan, Z., & Zhou, M. (2017a). Question answering and question generation as dual tasks. arXiv preprint arXiv:1706.02027.
Tang, J. C., Kivran-Swaine, F., Inkpen, K., & Van House, N. (2017b, February). Perspectives on live streaming: Apps, users, and research. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 123-126.
Turing, A. M. (1950). Computing machinery and intelligence.Mind, 433–460.
Varshney, L. R., Pinel, F., Varshney, K. R., Bhattacharjya, D., Schörgendorfer, A., & Chee, Y. M. (2019). A big data approach to computational creativity: The curious case of Chef Watson. IBM Journal of Research and Development, 63(1), 7-1.
Veron, M., Peñas, A., Echegoyen, G., Banerjee, S., Ghannay, S., & Rosset, S. (2020, June). A cooking knowledge graph and benchmark for question answering evaluation in lifelong learning scenarios. In International Conference on Applications of Natural Language to Information Systems, 94-101.
Wang, K., & Chua, T.-S. (2010, August). Exploiting salient patterns for question detection and question retrieval in community-based question answering, Proceedings of the 23rd International Conference on Computational Linguistics: Association for Computational Linguistics, 1155-1163.
Wasim, M., Mahmood, W., Asim, M. N., & Khan, M. U. (2018). Multi-label question classification for factoid and list type questions in biomedical question answering. IEEE Access, 7, 3882-3896.
Wiegand, M., Roth, B., & Klakow, D. (2012). Data-driven knowledge extraction for the food domain. In proceedings of KONVENS, 21-29.
Wilson, T. (2000). Human information behavior, Informing Science, 3, 49-55.
Wong, W., Liu, W., & Bennamoun, M. (2012). Ontology learning from text: a look back & into the future, ACM Computing Surveys, 44(4), 1-36.
Xia, L. (2014). Answer planning based answer generation for cooking question answering system, Journal of Chemical & Pharmaceutical Research, 6(7), 474-480.
Yagcioglu, S., Erdem, A., Erdem, E., & Ikizler-Cinbis, N. (2018). RecipeQA:a challenge dataset for multimodal comprehension of cooking recipes. arXiv preprint arXiv:1809.00812.
Yamakata, Y., Maeta, H., Kadowaki, T., Sasada, T., Imahori, S., & Mori, S. (2017). Cooking recipe search by pairs of ingredient and action—word sequence vs flow-graph representation. Information and Media Technologies, 12, 71-79.
Yao, X., & Van Durme, B. (2014, January). Information extraction over structured data: question answering with freebase. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 956-966.
Yin, W., Yu, M., Xiang, B., Zhou, B., & Schütze, H. (2016). Simple question answering by attentive convolutional neural network. arXiv preprint arXiv:1606.03391.
Youn, J., Naravane, T., & Tagkopoulos, I. (2020). Using word embeddings to learn a better food ontology. Frontiers in artificial intelligence, 3.
Zhao, C., Xiong, C., Qian, X., & Boyd-Graber, J. (2020, April). Complex factoid question answering with a free-text knowledge graph. In Proceedings of The Web Conference 2020, 1205-1216.
Zhao, X., Xing, Z., Kabir, M. A., Sawada, N., Li, J., & Lin, S. W. (2017, February). Hdskg: Harvesting domain specific knowledge graph from content of webpages. In 2017 IEEE 24th international conference on software analysis, evolution and reengineering, 56-67.
Zheng, W., & Zhang, M. (2019). Question answering over knowledge graphs via structural query patterns. arXiv preprint arXiv:1910.09760.
Zheng, W., Yu, J. X., Zou, L., & Cheng, H. (2018). Question answering over knowledge graphs: question understanding via template decomposition. Proceedings of the VLDB Endowment, 11(11), 1373-1386.
Zhou, L., Xu, C., & Corso, J. J. (2018, April). Towards automatic learning of procedures from web instructional videos. arXiv preprint arXiv:1703.09788.
Zhu, G., & Iglesias, C. A. (2015). Sematch: Semantic entity search from knowledge graph.In Joint Proceedings of the 1st International Workshop on Summarizing and Presenting Entities and Ontologies and the 3rd International Workshop on Human Semantic Web Interfaces (SumPre 2015, HSWI 2015) co-located with the 12th Extended Semantic Web Conferen, 1556, 1-12.
Zhu, G., & Iglesias, C. A. (2016). Computing semantic similarity of concepts in knowledge graphs. IEEE Transactions on Knowledge and Data Engineering, 29(1), 72-85.
Zhu, S., Cheng, X., & Su, S. (2020). Knowledge-based question answering by tree-to-sequence learning. Neurocomputing, 372, 64-72.