Literatura científica selecionada sobre o tema "Task oriented dialogue system"
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Artigos de revistas sobre o assunto "Task oriented dialogue system"
Xiang, Lu, Junnan Zhu, Yang Zhao, Yu Zhou e Chengqing Zong. "Robust Cross-lingual Task-oriented Dialogue". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 6 (30 de novembro de 2021): 1–24. http://dx.doi.org/10.1145/3457571.
Texto completo da fonteMatsumoto, Kazuyuki, Manabu Sasayama e Taiga Kirihara. "Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks". Sensors 22, n.º 2 (17 de janeiro de 2022): 694. http://dx.doi.org/10.3390/s22020694.
Texto completo da fonteZhu, Qi, Kaili Huang, Zheng Zhang, Xiaoyan Zhu e Minlie Huang. "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset". Transactions of the Association for Computational Linguistics 8 (julho de 2020): 281–95. http://dx.doi.org/10.1162/tacl_a_00314.
Texto completo da fonteWu, Zhong, Qiping She e Chuan Zhou. "Intelligent Customer Service System Optimization Based on Artificial Intelligence". Journal of Organizational and End User Computing 36, n.º 1 (7 de fevereiro de 2024): 1–27. http://dx.doi.org/10.4018/joeuc.336923.
Texto completo da fonteZhang, Zheng, Minlie Huang, Zhongzhou Zhao, Feng Ji, Haiqing Chen e Xiaoyan Zhu. "Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems". ACM Transactions on Information Systems 37, n.º 3 (20 de julho de 2019): 1–30. http://dx.doi.org/10.1145/3317612.
Texto completo da fonteIizuka, Shinya, Shota Mochizuki, Atsumoto Ohashi, Sanae Yamashita, Ao Guo e Ryuichiro Higashinaka. "Clarifying the Dialogue-Level Performance of GPT-3.5 and GPT-4 in Task-Oriented and Non-Task-Oriented Dialogue Systems". Proceedings of the AAAI Symposium Series 2, n.º 1 (22 de janeiro de 2024): 182–86. http://dx.doi.org/10.1609/aaaiss.v2i1.27668.
Texto completo da fonteZENG, Ya, Li WAN, Qiuhong LUO e Mao CHEN. "A Hierarchical Memory Model for Task-Oriented Dialogue System". IEICE Transactions on Information and Systems E105.D, n.º 8 (1 de agosto de 2022): 1481–89. http://dx.doi.org/10.1587/transinf.2022edp7001.
Texto completo da fonteMORIN, PHILIPPE, JEAN-PAUL HATON, JEAN-MARIE PIERREL, GUENTHER RUSKE e WALTER WEIGEL. "A MULTILINGUAL APPROACH TO TASK-ORIENTED MAN-MACHINE DIALOGUE BY VOICE". International Journal of Pattern Recognition and Artificial Intelligence 02, n.º 03 (setembro de 1988): 573–88. http://dx.doi.org/10.1142/s0218001488000339.
Texto completo da fonteYoung, Tom, Frank Xing, Vlad Pandelea, Jinjie Ni e Erik Cambria. "Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 10 (28 de junho de 2022): 11622–29. http://dx.doi.org/10.1609/aaai.v36i10.21416.
Texto completo da fonteDong, Xuelian, e Jiale Chen. "PluDG: enhancing task-oriented dialogue system with knowledge graph plug-in module". PeerJ Computer Science 9 (24 de novembro de 2023): e1707. http://dx.doi.org/10.7717/peerj-cs.1707.
Texto completo da fonteTeses / dissertações sobre o assunto "Task oriented dialogue system"
ISOMURA, Naoki, Fujio TORIUMI e Kenichiro ISHII. "EVALUATION METHOD OF NON-TASK-ORIENTED DIALOGUE SYSTEM BY HMM". INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10479.
Texto completo da fonteYang, Fan. "Directing the flow of conversation in task-oriented dialogue". Full text open access at:, 2008. http://content.ohsu.edu/u?/etd,625.
Texto completo da fonteBalaraman, Vevake. "Leveraging Domain Information for Data Driven Task-Oriented Dialogue Systems". Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339652.
Texto completo da fonteLinné, Christoffer, e Pontus Olausson. "Crowdsourcing av data för Hybrid Code Networks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281968.
Texto completo da fonteUppgiftsorienterade dialogsystem är ett populärt sätt för företag att generera extra värde både internt och för kunder. Moderna modeller för dessa dialogsystem som använder neurala nätverk för att möjliggöra träning direkt på skriftliga dialoger är väldigt datahungriga, vilket försvårar implementationen av dessa. Crowdsourcing är en attraktiv lösning för att generera denna typ av träningsdata, men metoden kommer även med flera svårigheter. Vi introducerar en ny metod för generering av träningsdata som bygger på parallell crowdsourcing av dialoger, samt crowdsourcad kvalitetsgranskning. Vi använder denna metod för att samla in ett litet dataset som utspelar sig inom domänen busschaufför-resenär. Vi menar att denna metod erbjuder ett effektivt sätt att samla in nya, högkvalitativa dataset. Hybrid Code Networks är en modell för dialogsystem som kombinerar ett neuralt nätverk med domänspecifik kunskap, och som på så sätt kräver en betydligt mindre mängd träningsdata än andra liknande dialogsystem för att uppnå jämförbar prestanda. Genom att kombinera Hybrid Code Networks med vår nya metod för generering av träningsdata menar vi att man kan sänka tröskeln för att implementera uppgiftsorienterade dialogsystem på domäner med otillräcklig träningsdata. Vi implementerar Hybrid Code Networks och tränar implementationen på det insamlade datasetet, och uppnår goda resultat.
Veron, Mathilde. "Systèmes de dialogue apprenant tout au long de leur vie : de l'élaboration à l'évaluation". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG089.
Texto completo da fonteTask-oriented dialogue systems, more commonly known as chatbots, are intended to perform tasks and provide the information required by a user in a conversation in a specific domain (e.g., train booking). These systems have been widely adopted by many companies. However, they suffer in practice from some limitations: (1) they are dependent on the training data needed to obtain a performing system, (2) they lack flexibility and perform poorly as soon as the case encountered in practice moves away from the data seen during development, and (3) it is difficult to adapt them over time to new elements that appear given the inevitable evolution of the world, of the requirements of the designers and users. Thus, we apply Lifelong Learning (LL) to task-oriented dialogue systems. We define LL as the ability of a system to be applied to and learn multiple tasks over time, in production, autonomously, continuously, and interactively. Three steps must be performed in autonomy by the system: (1) Detect the presence of a new element, (2) extract and identify the new element, and (3) adapt the system components associated with this element. As part of this thesis and given the complexity of LL, we focus our work on three subproblems associated with LL dialogue systems. As a first step, we propose a first methodology for the continuous and time-dependent evaluation of on-the-job learning dialogue systems. This type of learning is close to LL but puts aside the multitask aspect. We also describe a task-oriented dialogue system capable of improving its slot detection on-the-job via the autonomous annotation of data collected during its interactions. We evaluate this system through two adaptation methods using our methodology and show interest in a continuous evaluation over time. As a second step, we focus on the innovative study of interlingual transfer when applying continual learning to a language sequence. Indeed, transfer and continual learning are two main aspects of LL. We perform this study on the slot-filling task using multilingual BERT. We observe substantial forward transfer capabilities despite the presence of forgetting and demonstrate the capabilities of a model trained in a continual manner. As a third step, we study inter-domain transfer in the context of zero-shot learning. We carry out this study on a task that requires considering the whole dialogue and not only the current turn, which corresponds to the dialogue state tracking task. We first study the generalization and transfer capabilities of an existing model on new slot values. Then, we propose some model variants and a method able to improve the zero-shot performance of the model on new types of slots belonging to a new domain
Bouguelia, Sara. "Modèles de dialogue et reconnaissance d'intentions composites dans les conversations Utilisateur-Chatbot orientées tâches". Electronic Thesis or Diss., Lyon 1, 2023. http://www.theses.fr/2023LYO10106.
Texto completo da fonteDialogue Systems (or simply chatbots) are in very high demand these days. They enable the understanding of user needs (or user intents), expressed in natural language, and on fulfilling such intents by invoking the appropriate back-end APIs (Application Programming Interfaces). Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans' most innate ability, the use of natural language). The continuous improvement in Artificial Intelligence (AI), Natural Language Processing (NLP), and the countless number of devices allow performing real-world tasks (e.g., making a reservation) by using natural language-based interactions between users and a large number of software enabled services.Nonetheless, chatbot development is still in its preliminary stage, and there are several theoretical and technical challenges that need to be addressed. One of the challenges stems from the wide range of utterance variations in open-end human-chatbot interactions. Additionally, there is a vast space of software services that may be unknown at development time. Natural human conversations can be rich, potentially ambiguous, and express complex and context-dependent intents. Traditional business process and service composition modeling and orchestration techniques are limited to support such conversations because they usually assume a priori expectation of what information and applications will be accessed and how users will explore these sources and services. Limiting conversations to a process model means that we can only support a small fraction of possible conversations. While existing advances in NLP and Machine Learning (ML) techniques automate various tasks such as intent recognition, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual, ad-hoc and costly process.This thesis project aims at advancing the fundamental understanding of cognitive services engineering. In this thesis we contribute novel abstractions and techniques focusing on the synthesis of API calls to support a broad range of potentially complex user intents. We propose reusable and extensible techniques to recognize and realize complex intents during humans-chatbots-services interactions. These abstractions and techniques seek to unlock the seamless and scalable integration of natural language-based conversations with software-enabled services
Schaub, Léon-Paul. "Dimensions mémorielles de l'interaction écrite humain-machine ˸ une approche cognitive par les modèles mnémoniques pour la détection et la correction des incohérences du système dans les dialogues orientés-tâche". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG023.
Texto completo da fonteIn this work, we are interested in the place of task-oriented dialogue systems in both automatic language processing and human-machine interaction. In particular, we focus on the difference in information processing and memory use, from one turn to the next, by humans and machines, during a written chat conversation. After having studied the mechanisms of memory retention and recall in humans during a dialogue, in particular during the accomplishment of a task, we hypothesize that one of the elements that may explain why the performance of machines remains below that of humans, is the ability to possess not only an image of the user, but also an image of oneself, explicitly summoned during the inferences linked to the continuation of the dialogue. This translates into the following three axes for the system. First, by the anticipation, at a given turn of speech, of the next turn of the user. Secondly, by the detection of an inconsistency in one's own utterance, facilitated, as we demonstrate, by the anticipation of the user's next turn as an additional cue. Finally, by predicting the number of remaining turns in the dialogue in order to have a better vision of the dialogue progression, taking into account the potential presence of an incoherence in one's own utterance, this is what we call the dual model of the system, which represents both the user and the image that the system sends to the user. To implement these features, we exploit end-to-end memory networks, a recurrent neural network model that has the specificity not only to handle long dialogue histories (such as an RNN or an LSTM) but also to create reflection jumps, allowing to filter the information contained in both the user's utterance and the dialogue history. In addition, these three reflection jumps serve as a "natural" attention mechanism for the memory network, similar to a transformer decoder. For our study, we enhance a type of memory network called WMM2Seq (sequence-based working memory network) by adding our three features. This model is inspired by cognitive models of memory, presenting the concepts of episodic memory, semantic memory and working memory. It performs well on dialogue response generation tasks on the DSTC2 (human-machine in the restaurant domain) and MultiWOZ (multi-domain created with Wizard of Oz) corpora; these are the corpora we use for our experiments. The three axes mentioned above bring two main contributions to the existing. Firstly, it adds complexity to the intelligence of the dialogue system by providing it with a safeguard (detected inconsistencies). Second, it optimizes both the processing of information in the dialogue (more accurate or richer answers) and the duration of the dialogue. We evaluate the performance of our system with firstly the F1 score for the entities detected in each speech turn, secondly the BLEU score for the fluency of the system utterance and thirdly the joint accuracy for the success of the dialogue. The results obtained show that it would be interesting to direct research towards more cognitive models of memory management in order to reduce the performance gap in a human-machine dialogue
Kowtko, J. C. "The function of intonation in task-oriented dialogue". Thesis, University of Edinburgh, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508706.
Texto completo da fonteCarletta, Jean. "Risk-taking and recovery in task-oriented dialogue". Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/20370.
Texto completo da fonteSotillo, Catherine Frances. "Phonological reduction and intelligibility in task-oriented dialogue". Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/21544.
Texto completo da fonteLivros sobre o assunto "Task oriented dialogue system"
Taboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Philadelphia: John Benjamins Pub. Co., 2004.
Encontre o texto completo da fonteTaboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Amsterdam: J. Benjamins, 2004.
Encontre o texto completo da fonteTaboada, María Teresa. Building coherence and cohesion: Task-oriented dialogue in English and Spanish. Amsterdam: John Benjamins, 2003.
Encontre o texto completo da fonteBernhaupt, Regina. Human-Centred Software Engineering: Third International Conference, HCSE 2010, Reykjavik, Iceland, October 14-15, 2010. Proceedings. Berlin, Heidelberg: IFIP International Federation of Information Processing, 2010.
Encontre o texto completo da fonteDadyan, Eduard. Modern programming technologies. The C#language. Volume 2. For advanced users. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1478383.
Texto completo da fonteLeech, Geoffrey. Pragmatics and Dialogue. Editado por Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0007.
Texto completo da fonteWatson, Craig. Task-Oriented Performance: The Top Management System. Ballinger Pub Co, 1988.
Encontre o texto completo da fonteKim, Joong Nam. An agent-based cockpit task management system: A task-oriented pilot-vehicle interface. 1994.
Encontre o texto completo da fonteKim, Joong Nam. An agent-based cockpit task management system: A task-oriented pilot-vehicle interface. 1994.
Encontre o texto completo da fonteLarson, Victoria M. Task-Oriented, Naturally Elicited Speech (TONE) database for the Force Requirements Expert System, Hawaii (FRESH). 1988.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Task oriented dialogue system"
Peng, Yan, Penghe Chen, Yu Lu, Qinggang Meng, Qi Xu e Shengquan Yu. "A Task-Oriented Dialogue System for Moral Education". In Lecture Notes in Computer Science, 392–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23207-8_72.
Texto completo da fonteMazumder, Sahisnu, e Bing Liu. "Continual Learning for Task-Oriented Dialogue Systems". In Synthesis Lectures on Human Language Technologies, 127–51. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-48189-5_6.
Texto completo da fonteVogel, Liane, e Lucie Flek. "Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems". In Text, Speech, and Dialogue, 476–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16270-1_39.
Texto completo da fonteBerg, Markus M., Antje Düsterhöft e Bernhard Thalheim. "Towards Interrogative Types in Task-Oriented Dialogue Systems". In Natural Language Processing and Information Systems, 302–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_38.
Texto completo da fonteFang, Ting, Tingting Qiao e Duanqing Xu. "HaGAN: Hierarchical Attentive Adversarial Learning for Task-Oriented Dialogue System". In Neural Information Processing, 98–109. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36708-4_9.
Texto completo da fonteSteindl, Sebastian, Ulrich Schäfer e Bernd Ludwig. "Generating Synthetic Dialogues from Prompts to Improve Task-Oriented Dialogue Systems". In KI 2023: Advances in Artificial Intelligence, 207–14. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42608-7_17.
Texto completo da fonteVeron, Mathilde, Sahar Ghannay, Anne-Laure Ligozat e Sophie Rosset. "Lifelong Learning and Task-Oriented Dialogue System: What Does It Mean?" In Lecture Notes in Electrical Engineering, 347–56. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9323-9_32.
Texto completo da fonteCarbonell, N., e J. M. Pierrel. "Task-Oriented Dialogue Processing in Human-Computer Voice Communication". In Recent Advances in Speech Understanding and Dialog Systems, 491–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83476-9_46.
Texto completo da fonteMarques, Tânia. "Towards a Taxonomy of Task-Oriented Domains of Dialogue". In PRIMA 2015: Principles and Practice of Multi-Agent Systems, 510–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25524-8_33.
Texto completo da fonteChauhan, Anamika, Aditya Malhotra, Anushka Singh, Jwalin Arora e Shubham Shukla. "Encoding Context in Task-Oriented Dialogue Systems Using Intent, Dialogue Acts, and Slots". In Innovations in Computer Science and Engineering, 287–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2043-3_34.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Task oriented dialogue system"
Blache, Philippe. "Dialogue management in task-oriented dialogue systems". In ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3139491.3139507.
Texto completo da fonteWei, Zhongyu, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong e Xiangying Dai. "Task-oriented Dialogue System for Automatic Diagnosis". In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2033.
Texto completo da fonteLi, Qian, e Xinmeng Li. "Memory-Augmented Dialogue State Tracker in Task-Oriented Dialogue System". In 2020 International Conference on Computer Information and Big Data Applications (CIBDA). IEEE, 2020. http://dx.doi.org/10.1109/cibda50819.2020.00100.
Texto completo da fonteHu, Xiangkun, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu e Zheng Zhang. "Dialogue Meaning Representation for Task-Oriented Dialogue Systems". In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.17.
Texto completo da fonteXing, Xulei, e Zelong Wang. "Probabilistic Dialogue Model Combined With VAE For Task-oriented Dialogue System". In 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT). IEEE, 2021. http://dx.doi.org/10.1109/cecit53797.2021.00154.
Texto completo da fonteMu, Yanhua, e YiXin Yin. "Task-Oriented Spoken Dialogue System for Humanoid Robot". In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631205.
Texto completo da fonteWang, Weizhi, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen e Weihua Luo. "Task-Oriented Dialogue System as Natural Language Generation". In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531920.
Texto completo da fonteSong, Meina, Zhongfu Chen, Peiqing Niu e E. Haihong. "Task-oriented Dialogue System Based on Reinforcement Learning". In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2019. http://dx.doi.org/10.1109/icsess47205.2019.9040789.
Texto completo da fonteSun, Shaochen, Youlang Ji, Hongying Zhao e Yang Yu. "Knowledge Graph Driven Dialogue Management for Task-oriented Dialogue". In 2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2020. http://dx.doi.org/10.1109/icicas51530.2020.00012.
Texto completo da fonteZhao, Xinyan, Bin He, Yasheng Wang, Yitong Li, Fei Mi, Yajiao Liu, Xin Jiang, Qun Liu e Huanhuan Chen. "UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues". In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dialdoc-1.2.
Texto completo da fonteRelatórios de organizações sobre o assunto "Task oriented dialogue system"
Traum, David R., e Elizabeth A. Hinkelman. Conversation Acts in Task-Oriented Spoken Dialogue. Fort Belvoir, VA: Defense Technical Information Center, junho de 1992. http://dx.doi.org/10.21236/ada256368.
Texto completo da fonteБакум, З. П., e В. О. Лапіна. Educational Dialogue in the Process of Foreign Language Training of Future Miners. Криворізький державний педагогічний університет, 2014. http://dx.doi.org/10.31812/0564/395.
Texto completo da fonteHeredia, Blanca. The Political Economy of Reform of the Administrative Systems of Public Sector Personnel in Latin America: An Analytical Framework. Inter-American Development Bank, novembro de 2002. http://dx.doi.org/10.18235/0012273.
Texto completo da fontePinchuk, O. P., e A. A. Prokopenko. Model of a computer-orient-ed methodological system for the development of digital competence of officers of the military administration of the Armed Forces of Ukraine in the system of qualification improvement. Національна академія Державної прикордонної служби України імені Б. Хмельницького, 2023. http://dx.doi.org/10.33407/lib.naes.736836.
Texto completo da fonteFreeman, Paul, Leslie A. Martin, Joanne Linnerooth-Bayer, Reinhard Mechler, Georg Pflug e Koko Warner. Disaster Risk Management: National Systems for the Comprehensive Management of Disaster Risk and Financial Strategies for Natural Disaster Reconstruction. Inter-American Development Bank, novembro de 2003. http://dx.doi.org/10.18235/0010539.
Texto completo da fonteShyshkina, Mariya P. The use of the cloud services to support the math teachers training. [б. в.], julho de 2020. http://dx.doi.org/10.31812/123456789/3897.
Texto completo da fonteBaader, Franz, e Barbara Morawska. Matching with respect to general concept inclusions in the Description Logic EL. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.205.
Texto completo da fonte