Relevant bibliographies by topics / Temporal knowledge representation

Academic literature on the topic 'Temporal knowledge representation'

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Published: 17 September 2022

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Journal articles on the topic "Temporal knowledge representation"

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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.

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Parisi, 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.

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We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.
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Della 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.

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Zhou, 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.

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Abstract Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address this problem, we propose a temporal knowledge graph representation learning method d-HyTE that incorporates entity descriptions. We learn structure-based representations of entities and relations and explore a deep convolutional neural network with attention to encode description-based representations of entities. The joint representation of two different representations of an entity is regarded as the final representation. We evaluate this method on link prediction and temporal scope prediction. Experimental results showed that our method d-HyTE outperformed the other baselines on many metrics.
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Camurri, 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.

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MORRIS, 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.

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Artificial intelligence research in temporal reasoning focuses on designing automated solutions to complex problems in computation involving time. TIME-97, the 4th International Workshop on Temporal Representation and Reasoning, held in Daytona Beach, Florida — like the three workshops that preceded it — had the objective of creating an international forum for the exchange of information among the many researchers and knowledge engineers who are developing and applying techniques in temporal reasoning.
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Wang, 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.

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Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.
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Rundensteiner, 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.

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Sethukkarasi, 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.

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Bernshtein, 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.

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Dissertations / Theses on the topic "Temporal knowledge representation"

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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.

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Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS.
Collaborative Unmanned Aircraft Systems (CUAS)
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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.

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Les systèmes auto-adaptatifs (SAS) optimisent leurs comportements ou configurations au moment de l'exécution en réponse à une modification de leur environnement ou de leurs comportements. Ces systèmes nécessitent donc une connaissance approfondie de la situation en cours qui permet de raisonnement en considérant les opérations d'adaptation. En utilisant la méthodologie de l'Ingénierie Dirigée par les Modèles (IDM), il est possible d'abstraire cette situation. Cependant, les informations concernant le système ne sont pas toujours connues avec une confiance absolue. De plus, dans de tels systèmes, la fréquence de surveillance peut différer du délai nécessaire pour que les mesures de reconfiguration aient des effets mesurables. Ces caractéristiques s'accompagnent d'un défi global pour les ingénieurs logiciels : comment représenter les connaissances incertaines tout en permettant de les interroger efficacement et de représenter les actions en cours afin d'améliorer les processus d'adaptation ? Pour relever ce défi, cette thèse défend la nécessité d'un framework de modélisation qui inclut, en plus de tous les éléments traditionnels, l'incertitude et le temps en tant que concepts de première classe. Par conséquent, un développeur sera en mesure d'extraire des informations relatives au processus d'adaptation, à l'environnement ainsi qu'au système lui-même. Dans cette optique, nous présentons deux contributions évaluées : un modèle de contexte temporel et un langage pour les données incertaines. Le modèle de contexte temporel permet d'abstraire les actions passées, en cours et futures avec leurs impacts et leur contexte. Le langage, appelé Ain'tea, intègre l'incertitude des données en tant que concept de première classe
Self-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
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Omar, Tariq Ali. "Une architecture mixte logicielle et matérielle pour le contrôle intelligent en temps réel." Grenoble INPG, 2006. http://www.theses.fr/2006INPG0089.

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Le Contrôle intelligente d'un système autonome dans un environnement dynamique et dangereux exige la capacité d'identifier les menaces d'échec et de planifier les réponses temps-réel qui peut assurer la sécurité et l'objectif du système autonome. Nous proposons une architecture pour le contrôle intelligent en temps-réel, appelée ORICA. Elle se compose d'un sous-système de raisonnement IA et d'un sous-système d'exécution temps-réel de réponse. Le sous-système de raisonnement modélise des caractéristiques temporelles et logiques du comportement environnemental et planifie les réponses du système. Le sous-système temps-réel, composé d'une partie logicielle et d'une partie matérielle, exécute ces réponses pour éviter l'échec du système autonome. Il donne une performance inégalée par rapport aux précédentes approches conventionnelles. Le comportement unique de l'intelligence reconfigurable est implanté dans la partie matérielle, avec un circuit logique reprogrammable (FPGA)
Autonomous 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)
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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.

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This 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.

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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.

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Made available in DSpace on 2014-12-17T14:56:08Z (GMT). No. of bitstreams: 1 AlynneCSQ_DISSERT.pdf: 5674522 bytes, checksum: 276b6f887cbd025afcc9fc319a3dbc2e (MD5) Previous issue date: 2012-09-24
Conselho 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
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陳育良. "A Temporal Knowledge Representation Model Using the Knowledge Visualization Technology." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/56469911446657497677.

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碩士
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.
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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.

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博士
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.
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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.

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碩士
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.
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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.

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碩士
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.
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"Representing and Reasoning about Goals and Policies of Agents." Doctoral diss., 2010. http://hdl.handle.net/2286/R.I.8749.

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abstract: Goal specification is an important aspect of designing autonomous agents. A goal does not only refer to the set of states for the agent to reach. A goal also defines restrictions on the paths the agent should follow. Temporal logics are widely used in goal specification. However, they lack the ability to represent goals in a non-deterministic domain, goals that change non-monotonically, and goals with preferences. This dissertation defines new goal specification languages by extending temporal logics to address these issues. First considered is the goal specification in non-deterministic domains, in which an agent following a policy leads to a set of paths. A logic is proposed to distinguish paths of the agent from all paths in the domain. In addition, to address the need of comparing policies for finding the best ones, a language capable of quantifying over policies is proposed. As policy structures of agents play an important role in goal specification, languages are also defined by considering different policy structures. Besides, after an agent is given an initial goal, the agent may change its expectations or the domain may change, thus goals that are previously specified may need to be further updated, revised, partially retracted, or even completely changed. Non-monotonic goal specification languages that can make these changes in an elaboration tolerant manner are needed. Two languages that rely on labeling sub-formulas and connecting multiple rules are developed to address non-monotonicity in goal specification. Also, agents may have preferential relations among sub-goals, and the preferential relations may change as agents achieve other sub-goals. By nesting a comparison operator with other temporal operators, a language with dynamic preferences is proposed. Various goals that cannot be expressed in other languages are expressed in the proposed languages. Finally, plans are given for some goals specified in the proposed languages.
Dissertation/Thesis
Ph.D. Computer Science 2010
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Books on the topic "Temporal knowledge representation"

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Bestougeff, Helene. Logical tools for temporal knowledge representation. New York: Ellis Horwood, 1992.

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Bestougeff, Hélène. Logical tools for temporal knowledge representation. New York: Ellis Horwood, 1992.

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Stock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.

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Barrera, Renato, and Khaled K. Al-Taha. Models in Temporal Knowledge Representation and Temporal DBMS: Report 90-8. Univ of California Natl Center for, 1990.

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Oliviero, Stock, ed. Spatial and temporal reasoning. Dordrecht: Kluwer Academic Publishers, 1997.

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Bestougeff, Helene, and Gerard Ligozat. Logical Tools for Temporal Knowledge Representation (Ellis Horwood Series in Artificial Intelligence). Ellis Horwood, Ltd., 1993.

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Bestougeff, Helene, and Gerard Ligozat. Logical Tools for Temporal Knowledge Representation (Ellis Horwood Series in Artificial Intelligence). Ellis Horwood, Ltd., 1993.

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Frank, 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.

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Inkson, 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.

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The boundaryless career type provides a model of career development that appears to have some advantages over traditional occupational or organizational models. In a changing environment, it encourages mobility, flexibility, the development of knowledge and networks, and the taking of responsibility for one's own career. The boundaryless career also resonates effectively with the temporary organization structures and “knowledge workers” becoming characteristic of the new century. It appears a particularly appropriate way of understanding careers in industries, such as film production and software development, that are based on temporary projects rather than permanent structures, but these industries may be merely extreme examples of a wider loosening and crossing of boundaries in the world of work. The organizational career is dead or dying, and boundaryless careers are representative not just of a creative elite of workers, but of the mainstream.
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Bolton, 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.

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This chapter traces competing theories of universal natures found in Locke’s Essay and Leibniz’s Nouveaux Essais. Locke maintains that kinds must be defined more or less as we see fit, because nature does not exhibit a reasonably precise or fully determinate division of things and there are no eternal archetypes. His theory of kinds is homocentric. It cedes no authority or priority to general truths or ideas over particular ones. By contrast, Leibniz argues that similarity relations are objective eternal essences of kinds. Their reality consists in being possible entities known by God. Concepts are formed by human minds in virtue of innate tendencies to construct sensible representations of essences. He maintains that knowledge of general principles is prior to knowledge of their particular instances. Leibniz considers dimensions (space, time) to be sort of universals with reality like that of essences. For Locke, ideas of space and time are constructed from particular ideas of spatial and temporal qualities.
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Book chapters on the topic "Temporal knowledge representation"

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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.

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Cyre, 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.

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Łupińska-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.

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Spiotta, 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.

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Hagège, 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.

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Herweg, 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.

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Ribarić, 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.

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Song, 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.

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Song, 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.

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Moulin, 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.

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Conference papers on the topic "Temporal knowledge representation"

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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.

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In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. Knowledge discovery based on the new representations will then be computationally efficient, and to certain extent be more effective due to the removal of noise and irrelevant information in the step of representation learning. In this paper, we will briefly review state-of-the-art techniques for extracting representation and discovering knowledge from streaming and temporal data, and demonstrate their performance at addressing several real application problems.
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Abdul 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.

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Kabakcioglu. "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.

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Kabakcioglu, 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.

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Hayes, 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.

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Abstract Solid models are static entities, often defined by boundary representation models as sets of enclosing surfaces. Constructive Solid Geometry and feature-based computer-aided design environments create procedural descriptions of 3D objects in forms of history or CSG trees. These representations are temporally fixed, i.e., they describe the state of an object at a point in time. This paper describes a method to represent and capture temporal evolution of solid models — what we call model process history. We define process history to be all states of a model — the search space of design process. This paper presents a representational formalism we call model process graphs (MPGs). We use MPGs to integrate a model’s description with a model of temporal changes that occur during the design process. We believe that MPG representations can have valuable application for many design and manufacturing problems. The paper describes our preliminary results to use MPGs to (1) create a record of design process; (2) store process-based design rationale; (3) represent in-process shapes for machined artifacts. We anticipate that similar structures will find application in other design and manufacturing problems where important process knowledge is embodied by temporal changes occurring in model evolution.
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Feng, 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.

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Ribaric, 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.

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Ma, 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.

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Li, 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.

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Aineto, 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.

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This paper introduces the Temporal Inference Problem (TIP), a general formulation for a family of inference problems that reason about the past, present or future state of some observed agent. A TIP builds on the models of an actor and of an observer. Observations of the actor are gathered at arbitrary times and a TIP encodes hypothesis on unobserved segments of the actor's trajectory. Regarding the last observation as the present time, a TIP enables to hypothesize about the past trajectory, future trajectory or current state of the actor. We use LTL as a language for expressing hypotheses and reduce a TIP to a planning problem which is solved with an off-the-shelf classical planner. The output of the TIP is the most likely hypothesis, the minimal cost trajectory under the assumption that the actor is rational. Our proposal is evaluated on a wide range of TIP instances defined over different planning domains.
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Reports on the topic "Temporal knowledge representation"

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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.

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Lutz, Carsten. Interval-based Temporal Reasoning with General TBoxes. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.109.

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Aus der Motivation: Description Logics (DLs) are a family of formalisms well-suited for the representation of and reasoning about knowledge. Whereas most Description Logics represent only static aspects of the application domain, recent research resulted in the exploration of various Description Logics that allow to, additionally, represent temporal information, see [4] for an overview. The approaches to integrate time differ in at least two important aspects: First, the basic temporal entity may be a time point or a time interval. Second, the temporal structure may be part of the semantics (yielding a multi-dimensional semantics) or it may be integrated as a so-called concrete domain. Examples for multi-dimensional point-based logics can be find in, e.g., [21;29], while multi-dimensional interval-based logics are used in, e.g., [23;2]. The concrete domain approach needs some more explanation. Concrete domains have been proposed by Baader and Hanschke as an extension of Description Logics that allows reasoning about 'concrete qualities' of the entities of the application domain such as sizes, length, or weights of real-worlds objects [5]. Description Logics with concrete domains do usually not use a fixed concrete domain; instead the concrete domain can be thought of as a parameter to the logic. As was first described in [16], if a 'temporal' concrete domain is employed, then concrete domains may be point-based, interval-based, or both.
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Horrocks, Ian, and Stephan Tobies. Optimisation of Terminological Reasoning. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.99.

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An extended abstract of this report was submitted to the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). When reasoning in description, modal or temporal logics it is often useful to consider axioms representing universal truths in the domain of discourse. Reasoning with respect to an arbitrary set of axioms is hard, even for relatively inexpressive logics, and it is essential to deal with such axioms in an efficient manner if implemented systems are to be effective in real applications. This is particularly relevant to Description Logics, where subsumption reasoning with respect to a terminology is a fundamental problem. Two optimisation techniques that have proved to be particularly effective in dealing with terminologies are lazy unfolding and absorption. In this paper we seek to improve our theoretical understanding of these important techniques. We define a formal framework that allows the techniques to be precisely described, establish conditions under which they can be safely applied, and prove that, provided these conditions are respected, subsumption testing algorithms will still function correctly. These results are used to show that the procedures used in the FaCT system are correct and, moreover, to show how effiency an be significantly improved, while still retaining the guarantee of correctness, by relaxing the safety conditions for absorption.
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