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Статті в журналах з теми "Temporal knowledge representation"
Galton, Antony. "Spatial and temporal knowledge representation." Earth Science Informatics 2, no. 3 (May 12, 2009): 169–87. http://dx.doi.org/10.1007/s12145-009-0027-6.
Повний текст джерелаParisi, Francesco, and John Grant. "Knowledge Representation in Probabilistic Spatio-Temporal Knowledge Bases." Journal of Artificial Intelligence Research 55 (March 28, 2016): 743–98. http://dx.doi.org/10.1613/jair.4883.
Повний текст джерелаDella Penna, Giuseppe, and Sergio Orefice. "Qualitative representation of spatio-temporal knowledge." Journal of Visual Languages & Computing 49 (December 2018): 1–16. http://dx.doi.org/10.1016/j.jvlc.2018.10.002.
Повний текст джерелаZhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.
Повний текст джерелаCamurri, Antonio. "Temporal logic issues in music knowledge representation." Microprocessing and Microprogramming 27, no. 1-5 (August 1989): 541–46. http://dx.doi.org/10.1016/0165-6074(89)90107-5.
Повний текст джерелаMORRIS, ROBERT, and LINA KHATIB. "Temporal Representation and Reasoning." Knowledge Engineering Review 12, no. 4 (December 1997): 411–12. http://dx.doi.org/10.1017/s0269888997003081.
Повний текст джерелаWang, Shu, Xueying Zhang, Peng Ye, Mi Du, Yanxu Lu, and Haonan Xue. "Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation." ISPRS International Journal of Geo-Information 8, no. 4 (April 8, 2019): 184. http://dx.doi.org/10.3390/ijgi8040184.
Повний текст джерелаRundensteiner, Elke A., Lois W. Hawkes, and Wyllis Bandler. "Set-valued temporal knowledge representation for fuzzy temporal retrieval in ICAI." International Journal of Approximate Reasoning 2, no. 2 (April 1988): 107. http://dx.doi.org/10.1016/0888-613x(88)90093-x.
Повний текст джерелаSethukkarasi, R., S. Ganapathy, P. Yogesh, and A. Kannan. "An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns." Journal of Intelligent & Fuzzy Systems 26, no. 3 (2014): 1167–78. http://dx.doi.org/10.3233/ifs-130803.
Повний текст джерелаBernshtein, L. S., S. M. Kovalev, and A. V. Muravskii. "Models of representation of fuzzy temporal knowledge in databases of temporal series." Journal of Computer and Systems Sciences International 48, no. 4 (August 2009): 625–36. http://dx.doi.org/10.1134/s1064230709040169.
Повний текст джерелаДисертації з теми "Temporal knowledge representation"
Lazarovski, Daniel. "Extending the Stream Reasoning in DyKnow with Spatial Reasoning in RCC-8." Thesis, Linköpings universitet, KPLAB - Laboratoriet för kunskapsbearbetning, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75885.
Повний текст джерелаCollaborative Unmanned Aircraft Systems (CUAS)
Mouline, Ludovic. "Towards a modelling framework with temporal and uncertain data for adaptive systems." Thesis, Rennes 1, 2019. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/32c7a604-bdf6-491e-ba8f-1a9f2a1c0b8b.
Повний текст джерелаSelf-Adaptive Systems (SAS) optimise their behaviours or configurations at runtime in response to a modification of their environments or their behaviours. These systems therefore need a deep understanding of the ongoing situation which enables reasoning tasks for adaptation operations. Using the model-driven engineering (MDE) methodology, one can abstract this situation. However, information concerning the system is not always known with absolute confidence. Moreover, in such systems, the monitoring frequency may differ from the delay for reconfiguration actions to have measurable effects. These characteristics come with a global challenge for software engineers: how to represent uncertain knowledge that can be efficiently queried and to represent ongoing actions in order to improve adaptation processes? To tackle this challenge, this thesis defends the need for a unified modelling framework which includes, besides all traditional elements, temporal and uncertainty as first-class concepts. Therefore, a developer will be able to abstract information related to the adaptation process, the environment as well as the system itself. Towards this vision, we present two evaluated contributions: a temporal context model and a language for uncertain data. The temporal context model allows abstracting past, ongoing and future actions with their impacts and context. The language, named Ain’tea, integrates data uncertainty as a first-class citizen
Omar, Tariq Ali. "Une architecture mixte logicielle et matérielle pour le contrôle intelligent en temps réel." Grenoble INPG, 2006. http://www.theses.fr/2006INPG0089.
Повний текст джерелаAutonomous intelligent control system for a dynamic and dangerous environment necessitates the capacity to identify the failure threats and to plan the real-time responses that ensure safety and goal achievement by the autonomous system. We propose a real-time intelligent control architecture called ORICA. It consists of an AI reasoning subsystem and a real-time response execution subsystem. The AI reasoning subsystem models the temporal and logical characteristics of the environment and plans the system responses. The real-time subsystem, which is composed of a software section and a hardware section, executes these responses to avoid failure of the autonomous system. Its performance behavior is unparalleled by the previous classical approaches (pure hardware or pure software). The software section uses behavior switching according to the frequency of external events and a unique reconfigurable intelligence behavior has been implemented in hardware section, using a reprogrammable chip (FPGA)
Gredebäck, Gustaf. "Infants’ Knowledge of Occluded Objects: Evidence of Early Spatiotemporal Representations." Doctoral thesis, Uppsala University, Department of Psychology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4058.
Повний текст джерелаThis thesis demonstrates that infants represent temporarily non-visible, or occluded, objects. From 4 months of age, infants could accurately predict the reappearance of a moving object after 660 ms of non visibility; indicating accurate spatiotemporal representations. At this age predictions were dominated by associations between specific events and outcomes (associative rules). Between 6 and 8 months of age predictions became dominated by extrapolations (Study III). From 6 months infants could represent occluded objects for up to 4 seconds. The number of successful predictions decreased, however, if the information contained in the occlusion event diminished (time of accretion and deletion). As infants grew older (up to 12 months) they produced more accurate predictions. (Study II). The similarities between adult and infant performances were numerous (Study I). These conclusion are based on one cross sectional (Study I) and two longitudinal studies (Study II & III) in which an object, a ‘happy face’, moved on circular (Study I, II, & III) and other complex trajectories (Study III). One portion of each trajectory was covered by a screen that blocked the object from sight. In each study participants gaze were recorded with an infrared eye tracking system (ASL 504) and a magnetic head tracker (Flock of Birds). This data was combined with data from the stimulus and stored for of line analysis.
Queiroz, Alynne Concei??o Saraiva de. "Extra??o e Representa??o de Conhecimento de S?ries Temporais de Demanda de Energia El?trica Usando TSKR." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15454.
Повний текст джерелаConselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
The opening of the Brazilian market of electricity and competitiveness between companies in the energy sector make the search for useful information and tools that will assist in decision making activities, increase by the concessionaires. An important source of knowledge for these utilities is the time series of energy demand. The identification of behavior patterns and description of events become important for the planning execution, seeking improvements in service quality and financial benefits. This dissertation presents a methodology based on mining and representation tools of time series, in order to extract knowledge that relate series of electricity demand in various substations connected of a electric utility. The method exploits the relationship of duration, coincidence and partial order of events in multi-dimensionals time series. To represent the knowledge is used the language proposed by M?rchen (2005) called Time Series Knowledge Representation (TSKR). We conducted a case study using time series of energy demand of 8 substations interconnected by a ring system, which feeds the metropolitan area of Goi?nia-GO, provided by CELG (Companhia Energ?tica de Goi?s), responsible for the service of power distribution in the state of Goi?s (Brazil). Using the proposed methodology were extracted three levels of knowledge that describe the behavior of the system studied, representing clearly the system dynamics, becoming a tool to assist planning activities
A abertura do mercado brasileiro de energia el?trica e a competitividade entre as empresas do setor energ?tico fazem com que a busca por informa??es ?teis e ferramentas que venham a auxiliar na tomada de decis?es, aumente por parte das concession?rias. Uma importante fonte de conhecimento para essas concession?rias s?o as s?ries temporais de consumo de energia. A identifica??o de padr?es de comportamento e a descri??o de eventos se tornam necess?rias para a execu??o de atividades de planejamento, buscando melhorias na qualidade de atendimento e vantagens financeiras. A presente disserta??o apresenta uma metodologia baseada em ferramentas de minera??o e representa??o de s?ries temporais, com o objetivo de extrair conhecimento que relacionam s?ries de demanda de energia el?trica de diversas subesta??es interligadas de uma concession?ria. O m?todo utilizado explora rela??es de dura??o, coincid?ncia e ordem parcial de eventos em s?ries temporais multidimensionais. Para a representa??o do conhecimento ser? utilizada a linguagem proposta por M?rchen (2005) chamada Time Series Knowledge Representation (TSKR). Foi realizado um estudo de caso usando s?ries temporais de demanda de energia de 8 subesta??es interligadas por um sistema em anel, que alimenta a regi?o metropolitana de Goi?nia-GO, cedidas pela CELG (Companhia Energ?tica de Goi?s), permission?ria do servi?o de distribui??o de energia no estado de Goi?s (Brasil). Utilizando a metodologia proposta foram extra?dos tr?s n?veis de conhecimento que descrevem o comportamento do sistema estudado, representando a din?mica do sistema de forma clara, constituindo-se em uma ferramenta para auxiliar em atividades de planejamento
陳育良. "A Temporal Knowledge Representation Model Using the Knowledge Visualization Technology." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/56469911446657497677.
Повний текст джерела國立清華大學
工業工程與工程管理學系
95
In order to help the knowledge receivers to efficiently and accurately understand the useful knowledge and information from a large number of digital documents, knowledge representation mechanism has become an important issue that the content providers should concern. In the traditional knowledge sharing environment, the abstract temporal knowledge such as temporal information or temporal relations of events is hard to be fully recognized by knowledge receivers in a short time based on the text-oriented knowledge representation scheme. Therefore, a visual representation scheme for the text-based temporal knowledge will support knowledge receivers to efficiently recognize this type of knowledge. The core idea of this research is to extract and tag the temporal terms from text-oriented documents via temporal knowledge analysis procedure. As a result, the temporal relations of events can be derived from the temporal information and the corresponding visualized display of temporal knowledge can also be established according to the temporal information and events. This research develops a methodology for temporal knowledge visualization so that computer systems can automatically convert the text-oriented temporal knowledge into visualized display. A three-phase methodology (including full-text tagging, temporal relations analysis and temporal knowledge visualization) for temporal information extraction, temporal relations analysis and temporal concepts visualization is developed. In addition to the proposed methodology, a Web-based prototype system is developed to evaluate the feasibility of the proposed model and technique. As a whole, this research provides an effective visualization methodology for the knowledge users to improve efficiency for temporal knowledge recognition. The methodology can be further applied in many enterprise activities such as e-training or knowledge management to enhance reuse of temporal knowledge.
Chang, Yu-Chen, and 張祐城. "Point-Interval Algebra for Spatio-Temporal Knowledge Representation and Reasoning." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/92927429336365457533.
Повний текст джерела淡江大學
資訊工程學系
88
Representation and Reasoning about temporal/spatial information play an important role in current computer science. Herein, we construct an Interval Transitive Group for maintaining qualitative and quantitative temporal knowledge. This point-interval algebra is also extended for spatial constraint reasoning. We develop an O(n)-time algorithm for propagation temporal constraint between two time events. For solving point/interval algebra networks, we develop an O(n2)-time algorithm for finding all pairs of feasible relations, where n is the number of points or intervals. The Qualitative-Quantitative temporal knowledge was integrated herein. Also, we discuss some of the properties of interval relations and developed a fast computation mechanism to representation the interval relation distance. A set of algorithms is proposed to manage spatio-temporal knowledge. The contributions of these algorithms and the point-interval algebra system can be used to process the spatio-temporal knowledge query, to generate the schedule and layout of multimedia presentations, to handle synchronization specifications, to composite the temporal semantics among distributed multimedia objects, and to provide a pattern matching mechanism for content based multimedia information retrieval.
Chiu, Han-Pang, and 邱漢邦. "3D C-String: A New Spatio-temporal Knowledge Representation for Video Database Systems." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/76749926736281833530.
Повний текст джерела國立臺灣大學
資訊管理研究所
89
The knowledge structure, called 2D C+-string, to represent the spatial relations between the objects in an image was proposed by P.W. Huang et al. It allows us to represent spatial knowledge in images. The knowledge structure, called 3D string, to represent the spatial and temporal relations between the objects in a video was proposed by A.L.P. Chen et al. In the 3D string representation, an object is represented by its central point and starting frame number. So, they cannot deal with the overlapping relation in spatial and temporal dimensions and with the information of motions and size changes. In this thesis, we propose a new spatio-temporal knowledge representation called 3D C-string. 3D C-string can overcome the weakness of 3D string. The knowledge structure of 3D C-string, based on the concepts of 2D C+-string, uses the projections of video objects to represent spatial and temporal relations between the objects in a video. Moreover, 3D C-string can keep track of the motions and size changes of the objects in a video. This approach can provide us an easy and efficient way to retrieve, visualize and manipulate video objects in video database systems. The string generation and video reconstruction algorithms for the 3D C-string representation of video objects are also developed. By introducing the concept of the template objects and nearest former objects. The string generated by the string generation algorithm is unique and the symbolic video reconstructed from a given 3D C-string is unique, too. In comparison with the spatial relation inference and similarity retrieval in image database systems, the counterparts in video database systems are a fuzzier concept. Therefore, we extend the idea behind the relation inference and similarity retrieval of images in 2D C+-string to 3D C-string. We also define the similarity measures and propose a similarity retrieval algorithm. Finally, some experiments are performed to show the efficiency and effectiveness of the proposed algorithm.
Huang, Ching-Hsin, and 黃敬欣. "3D Z+-string：A spatio-temporal knowledge representation for the object successive appears and disappears." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/32357181464240795508.
Повний текст джерела靜宜大學
資訊管理學系研究所
98
The major objective of this research is to propose a new spatio-temporal knowledge representation called 3D Z+-string which extends the work of 3D Z-string. As a result of 3D Z-string, it cannot deal with the condition that an object appears and then disappears for more than one time. To solve this problem, we set the disappearance of the object as that object’ size is zero. And, we bring up the “&” to be a new operator. Therefore, it can record that an object appears and then disappears for more than one time. At the same time, we offer two search methods to find the specific frame. The first method, it can find out the spatial relationship between two objects in the Nth frame. The second method, it can find out the spatial relationship between objects that in which frame. These two methods can be offered to software developers. They can extend these methods for users to more convenient and powerful video search functions.
"Representing and Reasoning about Goals and Policies of Agents." Doctoral diss., 2010. http://hdl.handle.net/2286/R.I.8749.
Повний текст джерелаDissertation/Thesis
Ph.D. Computer Science 2010
Книги з теми "Temporal knowledge representation"
Bestougeff, Helene. Logical tools for temporal knowledge representation. New York: Ellis Horwood, 1992.
Знайти повний текст джерелаBestougeff, Hélène. Logical tools for temporal knowledge representation. New York: Ellis Horwood, 1992.
Знайти повний текст джерелаStock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.
Знайти повний текст джерелаBarrera, Renato, and Khaled K. Al-Taha. Models in Temporal Knowledge Representation and Temporal DBMS: Report 90-8. Univ of California Natl Center for, 1990.
Знайти повний текст джерелаOliviero, Stock, ed. Spatial and temporal reasoning. Dordrecht: Kluwer Academic Publishers, 1997.
Знайти повний текст джерелаBestougeff, Helene, and Gerard Ligozat. Logical Tools for Temporal Knowledge Representation (Ellis Horwood Series in Artificial Intelligence). Ellis Horwood, Ltd., 1993.
Знайти повний текст джерелаBestougeff, Helene, and Gerard Ligozat. Logical Tools for Temporal Knowledge Representation (Ellis Horwood Series in Artificial Intelligence). Ellis Horwood, Ltd., 1993.
Знайти повний текст джерелаFrank, Schilder, Katz Graham, and Pustejovsky J, eds. Annotating, extracting and reasoning about time and events: International seminar, Dagstuhl Castle, Germany, April 10-15, 2005 : revised papers. Berlin: Springer, 2007.
Знайти повний текст джерелаInkson, Kerr. The Boundaryless Career. Edited by Susan Cartwright and Cary L. Cooper. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199234738.003.0023.
Повний текст джерелаBolton, Martha Brandt. Locke’s Essay and Leibniz’s Nouveaux Essais. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190608040.003.0010.
Повний текст джерелаЧастини книг з теми "Temporal knowledge representation"
Tang, Na, Yong Tang, Lingkun Wu, and Hui Ma. "Temporal Knowledge Representation and Reasoning." In Temporal Information Processing Technology and Its Application, 311–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14959-7_16.
Повний текст джерелаCyre, Walling. "Acquiring temporal knowledge from schedules." In Conceptual Graphs for Knowledge Representation, 328–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56979-0_18.
Повний текст джерелаŁupińska-Dubicka, Anna, and Marek J. Druzdzel. "Modeling Dynamic Processes with Memory by Higher Order Temporal Models." In Foundations of Biomedical Knowledge Representation, 219–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28007-3_14.
Повний текст джерелаSpiotta, Matteo, Paolo Terenziani, and Daniele Theseider Dupré. "Answer Set Programming for Temporal Conformance Analysis of Clinical Guidelines Execution." In Knowledge Representation for Health Care, 65–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26585-8_5.
Повний текст джерелаHagège, Caroline, Pierre Marchal, Quentin Gicquel, Stefan Darmoni, Suzanne Pereira, and Marie-Hélène Metzger. "Linguistic and Temporal Processing for Discovering Hospital Acquired Infection from Patient Records." In Knowledge Representation for Health-Care, 70–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18050-7_6.
Повний текст джерелаHerweg, Michael. "Aspectual requirements of temporal connectives: Evidence for a two-level approach to semantics." In Lexical Semantics and Knowledge Representation, 185–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55801-2_35.
Повний текст джерелаRibarić, Slobodan. "Temporal Knowledge Representation and Reasoning Model for Temporally Rich Domains." In Lecture Notes in Computer Science, 430–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_57.
Повний текст джерелаSong, Xiuting, Luyi Bai, Rongke Liu, and Han Zhang. "Temporal Knowledge Graph Entity Alignment via Representation Learning." In Database Systems for Advanced Applications, 391–406. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_30.
Повний текст джерелаSong, Xiuting, Luyi Bai, Rongke Liu, and Han Zhang. "Temporal Knowledge Graph Entity Alignment via Representation Learning." In Database Systems for Advanced Applications, 391–406. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_30.
Повний текст джерелаMoulin, Bernard. "The representation of linguistic information in an approach used for modelling temporal knowledge in discourses." In Conceptual Graphs for Knowledge Representation, 182–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56979-0_10.
Повний текст джерелаТези доповідей конференцій з теми "Temporal knowledge representation"
Zhang, Xiangliang. "Mining Streaming and Temporal Data: from Representation to Knowledge." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/821.
Повний текст джерелаAbdul Manaf, Nor, and Mohammad Beikzadeh. "Representation and Reasoning of Fuzzy Temporal Knowledge." In 2006 IEEE Conference on Cybernetics and Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/iccis.2006.252252.
Повний текст джерелаKabakcioglu. "Temporal Knowledge Representation / Reasoning / Learning For Medicine." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.594620.
Повний текст джерелаKabakcioglu, A. Mete. "Temporal knowledge representation / reasoning / learning for medicine." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761278.
Повний текст джерелаHayes, Erik E., and William C. Regli. "Integrating Design Process Knowledge With CAD Models." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/cie-21247.
Повний текст джерелаFeng, Weixin, Yuanjiang Wang, Lihua Ma, Ye Yuan, and Chi Zhang. "Temporal Knowledge Consistency for Unsupervised Visual Representation Learning." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01001.
Повний текст джерелаRibaric, S., B. Dalbelo Basic, and N. Pavesic. "A model for fuzzy temporal knowledge representation and reasoning." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793237.
Повний текст джерелаMa, Jixin, Brian Knight, Miltos Petridis, and Xiao Bai. "A Graphical Representation for Uncertain and Incomplete Temporal Knowledge." In 2010 Second Global Congress on Intelligent Systems (GCIS). IEEE, 2010. http://dx.doi.org/10.1109/gcis.2010.219.
Повний текст джерелаLi, Zixuan, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. "Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3462963.
Повний текст джерелаAineto, Diego, Sergio Jimenez, and Eva Onaindia. "Generalized Temporal Inference via Planning." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/3.
Повний текст джерелаЗвіти організацій з теми "Temporal knowledge representation"
Bell, Colin E. Temporal Knowledge Representation and Reasoning for Project Planning. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada196075.
Повний текст джерелаLutz, Carsten. Interval-based Temporal Reasoning with General TBoxes. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.109.
Повний текст джерелаHorrocks, Ian, and Stephan Tobies. Optimisation of Terminological Reasoning. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.99.
Повний текст джерела