Статті в журналах: "Temporal knowledge representation"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Chen, Ling, Xing Tang, Weiqi Chen, Yuntao Qian, Yansheng Li, and Yongjun Zhang. "DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical Relation." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–18. http://dx.doi.org/10.1145/3477051.

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Temporal knowledge graph (TKG) representation learning embeds relations and entities into a continuous low-dimensional vector space by incorporating temporal information. Latest studies mainly aim at learning entity representations by modeling entity interactions from the neighbor structure of the graph. However, the interactions of relations from the neighbor structure of the graph are neglected, which are also of significance for learning informative representations. In addition, there still lacks an effective historical relation encoder to model the multi-range temporal dependencies. In this article, we propose a d ual gr a ph c onvolution network based TKG representation learning method using h istorical rel a tions (DACHA). Specifically, we first construct the primal graph according to historical relations, as well as the edge graph by regarding historical relations as nodes. Then, we employ the dual graph convolution network to capture the interactions of both entities and historical relations from the neighbor structure of the graph. In addition, the temporal self-attentive historical relation encoder is proposed to explicitly model both local and global temporal dependencies. Extensive experiments on two event based TKG datasets demonstrate that DACHA achieves the state-of-the-art results.

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12

Liu, Pei Qi, and Yang Tian. "Study on Knowledge Representation of Temporal Conceptual Graph in the Context." Applied Mechanics and Materials 411-414 (September 2013): 272–76. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.272.

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In the semantic analysis and the pragmatic analysis, the knowledge representation of the context is a very important domain. For the representation of the context, traditional conceptual graph is very complex. Some problems of more nodes, conceptual graph multilayer nested, difficulties in connecting and matching of often appear. Especially when the statement includes the temporal, modal and negation, these defects are even more prominent. Aiming at the shortcomings, the knowledge representation of temporal conceptual graph is presented. First of all, the time interval and the temporal interval representation method of English tenses are defined. On this basis, it focuses on the simple matching rule and the structure of the temporal conceptual graph. Finally, the performance of traditional conceptual graph and temporal conceptual graph are compared through an essay. The results indicate that the temporal conceptual graph is propitious to the semantic analysis and the pragmatic analysis.

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13

Trudel, Andre. "A Temporal Knowledge Representation Approach Based on Elementary Calculus." Computational Intelligence 13, no. 4 (November 1997): 465–85. http://dx.doi.org/10.1111/0824-7935.00048.

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14

Ltifi, Hela, Emna Ben Mohamed, and Mounir ben Ayed. "Interactive visual knowledge discovery from data-based temporal decision support system." Information Visualization 15, no. 1 (February 8, 2015): 31–50. http://dx.doi.org/10.1177/1473871614567794.

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The article aims to present a generic interactive visual analytics solution that provides temporal decision support using knowledge discovery from data modules together with interactive visual representations. It bases its design decisions on classification of visual representation techniques according to the criteria of temporal data type, periodicity, and dimensionality. The design proposal is applied to an existing medical knowledge discovery from data–based decision support system aiming at assisting physicians in the fight against nosocomial infections in the intensive care units. Our solution is fully implemented and evaluated.

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15

MA, JIXIN, BRIAN KNIGHT, and EPHRAIM NISSAN. "Temporal representation of state transitions." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 2 (April 1999): 67–78. http://dx.doi.org/10.1017/s0890060499132025.

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This paper describes a knowledge-based temporal representation of state transitions for industrial real-time systems. To allow expression of uncertainty, we shall define fluents as disjuncts of positive/negative time-varying properties. A state of the world is represented as a collection of fluents, which is usually incomplete in the sense that neither the positive form nor the negative form of some properties can be implied from it. The world under consideration is assumed to persist in a given state until an action(s) takes place to effect a transition of it into another state, where actions may either be instantaneous or durative. High-level causal laws are characterized in terms of relationships between actions and the involved world states. An effect completion axiom is imposed on each causal law to guarantee that all the fluents that can be affected by the performance of the corresponding action are governed. This completion requirement is practical for most industrial real-time applications and in fact provides a simple and effective treatment to the so-called frame problem.

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16

Mavromatis, Costas, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, and George Karypis. "TempoQR: Temporal Question Reasoning over Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5825–33. http://dx.doi.org/10.1609/aaai.v36i5.20526.

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Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.

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17

Yao, Y. "A Petri net model for temporal knowledge representation and reasoning." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 9 (1994): 1374–82. http://dx.doi.org/10.1109/21.310513.

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18

Hornsby, Kathleen, and Max J. Egenhofer. "Identity-based change: a foundation for spatio-temporal knowledge representation." International Journal of Geographical Information Science 14, no. 3 (April 2000): 207–24. http://dx.doi.org/10.1080/136588100240813.

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19

Anzellotti, Stefano. "Anterior temporal lobe and the representation of knowledge about people." Proceedings of the National Academy of Sciences 114, no. 16 (April 4, 2017): 4042–44. http://dx.doi.org/10.1073/pnas.1703438114.

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20

Loganantharaj, Rasiah. "Representation and compilation of knowledge in point-based temporal system." International Journal of Intelligent Systems 6, no. 5 (August 1991): 549–67. http://dx.doi.org/10.1002/int.4550060506.

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21

Woei-Tzy Jong, Yuh-Shin Shiau, Yih-Jen Horng, Hsin-Horng Chen, and Shyi-Ming Chen. "Temporal knowledge representation and reasoning techniques using time Petri nets." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29, no. 4 (1999): 541–45. http://dx.doi.org/10.1109/3477.775271.

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22

Qian, Da-qun. "Representation and use of imprecise temporal knowledge in dynamic systems." Fuzzy Sets and Systems 50, no. 1 (August 1992): 59–77. http://dx.doi.org/10.1016/0165-0114(92)90204-h.

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23

Orgun, Mehmet A., Chuchang Liu, and Abhaya C. Nayak. "Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks." Mathematics in Computer Science 2, no. 1 (November 2008): 143–63. http://dx.doi.org/10.1007/s11786-008-0048-4.

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24

Hu, Danyang, Meng Wang, Feng Gao, Fangfang Xu, and Jinguang Gu. "Knowledge Representation and Reasoning for Complex Time Expression in Clinical Text." Data Intelligence 4, no. 3 (2022): 573–98. http://dx.doi.org/10.1162/dint_a_00152.

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Abstract Temporal information is pervasive and crucial in medical records and other clinical text, as it formulates the development process of medical conditions and is vital for clinical decision making. However, providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging. In order to capture complex temporal semantics in clinical text, we propose a novel Clinical Time Ontology (CTO) as an extension from OWL framework. More specifically, we identified eight time-related problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time, cyclic time, irregular time, negations and other complex aspects of clinical time. Then, we extended Allen's and TEO's temporal relations and defined the relation concept description between complex and simple time. Simultaneously, we provided a formulaic and graphical presentation of complex time and complex time relationships. We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets. Finally, experiment results demonstrate that CTO could faithfully represent and reason over 93% of the temporal expressions, and it can cover a wider range of time-related classes in clinical domain.

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25

Chittaro, Luca, and Angelo Montanari. "Trends in temporal representation and reasoning." Knowledge Engineering Review 11, no. 3 (September 1996): 281–88. http://dx.doi.org/10.1017/s026988890000792x.

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Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.

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26

Carlson, Thomas A., Ryan A. Simmons, Nikolaus Kriegeskorte, and L. Robert Slevc. "The Emergence of Semantic Meaning in the Ventral Temporal Pathway." Journal of Cognitive Neuroscience 26, no. 1 (January 2014): 120–31. http://dx.doi.org/10.1162/jocn_a_00458.

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In the ventral visual pathway, early visual areas encode light patterns on the retina in terms of image properties, for example, edges and color, whereas higher areas encode visual information in terms of objects and categories. At what point does semantic knowledge, as instantiated in human language, emerge? We examined this question by studying whether semantic similarity in language relates to the brain's organization of object representations in inferior temporal cortex (ITC), an area of the brain at the crux of several proposals describing how the brain might represent conceptual knowledge. Semantic relationships among words can be viewed as a geometrical structure with some pairs of words close in their meaning (e.g., man and boy) and other pairs more distant (e.g., man and tomato). ITC's representation of objects similarly can be viewed as a complex structure with some pairs of stimuli evoking similar patterns of activation (e.g., man and boy) and other pairs evoking very different patterns (e.g., man and tomato). In this study, we examined whether the geometry of visual object representations in ITC bears a correspondence to the geometry of semantic relationships between word labels used to describe the objects. We compared ITC's representation to semantic structure, evaluated by explicit ratings of semantic similarity and by five computational measures of semantic similarity. We show that the representational geometry of ITC—but not of earlier visual areas (V1)—is reflected both in explicit behavioral ratings of semantic similarity and also in measures of semantic similarity derived from word usage patterns in natural language. Our findings show that patterns of brain activity in ITC not only reflect the organization of visual information into objects but also represent objects in a format compatible with conceptual thought and language.

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27

Chen, J., and H. Wu. "LAND COVER CHANGE KNOWLEDGE REPRESENTATION USING TEMPORAL LOGIC AND OPERATION RELATIONS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-7 (July 17, 2012): 203–8. http://dx.doi.org/10.5194/isprsannals-i-7-203-2012.

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28

Ji, Heng, Taylor Cassidy, Qi Li, and Suzanne Tamang. "Tackling representation, annotation and classification challenges for temporal knowledge base population." Knowledge and Information Systems 41, no. 3 (August 23, 2013): 611–46. http://dx.doi.org/10.1007/s10115-013-0675-1.

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29

Darmoni, S. J., and J. Charlet. "Knowledge Representation and Management. From Ontology to Annotation." Yearbook of Medical Informatics 24, no. 01 (August 2015): 134–36. http://dx.doi.org/10.15265/iy-2015-038.

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Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014. Results: Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multi-lingual ontologies.Conclusion: Semantic models began to show their efficiency, coupled with annotation tools.

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30

van der Meer, Elke, Frank Krüger, and Antje Nuthmann. "The Influence of Temporal Order Information in General Event Knowledge on Language Comprehension." Zeitschrift für Psychologie / Journal of Psychology 213, no. 3 (July 2005): 142–51. http://dx.doi.org/10.1026/0044-3409.213.3.142.

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Abstract. The coding of chronological order of real-life events, that is, “time’s arrow” in general event knowledge and its access in language comprehension was investigated with two relatedness judgment tasks. The temporal orientation (chronological or reverse) and the stimulus onset asynchrony (SOA; 200/250 ms or 1,000 ms) between preinformation and target were manipulated. The first experiment examined highly familiar sequences of events (e.g., lighting-burning-extinguishing) with the same strength of temporal relatedness for preceding and succeeding events. The second experiment investigated individual events. The results show that time’s arrow is not restricted to sequences of events, but is also embedded in the mental representation of individual events. The preferred temporal orientation in favor of future time is not only coded by a higher association strength between mental event representations, but also by expectancy based selection processes. The results support Barsalou’s model of perceptual symbol systems ( 1999 ).

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31

Munir, Siraj, Syed Imran Jami, and Shaukat Wasi. "Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs." Open Computer Science 11, no. 1 (January 1, 2021): 294–304. http://dx.doi.org/10.1515/comp-2020-0209.

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Abstract In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.

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32

Zhu, Cunchao, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhang. "Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4732–40. http://dx.doi.org/10.1609/aaai.v35i5.16604.

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Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel time-aware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.

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33

Shyi-Ming Chen and Woei-Tzy Jong. "Comments on "A Petri net model for temporal knowledge representation and reasoning"." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 27, no. 1 (February 1997): 165–66. http://dx.doi.org/10.1109/3477.552200.

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34

Drap, P., O. Papini, E. Pruno, M. Nucciotti, and G. Vannini. "SURVEYING MEDIEVAL ARCHAEOLOGY: A NEW FORM FOR HARRIS PARADIGM LINKING PHOTOGRAMMETRY AND TEMPORAL RELATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (February 23, 2017): 267–74. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w3-267-2017.

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The paper presents some reflexions concerning an interdisciplinary project between Medieval Archaeologists from the University of Florence (Italy) and ICT researchers from CNRS LSIS of Marseille (France), aiming towards a connection between 3D spatial representation and archaeological knowledge. It is well known that Laser Scanner, Photogrammetry and Computer Vision are very attractive tools for archaeologists, although the integration of representation of space and representation of archaeological time has not yet found a methodological standard of reference. We try to develop an integrated system for archaeological 3D survey and all other types of archaeological data and knowledge through integrating observable (material) and non-graphic (interpretive) data. Survey plays a central role, since it is both a metric representation of the archaeological site and, to a wider extent, an interpretation of it (being also a common basis for communication between the 2 teams). More specifically 3D survey is crucial, allowing archaeologists to connect actual spatial assets to the stratigraphic formation processes (i.e. to the archaeological time) and to translate spatial observations into historical interpretation of the site. <br><br> We propose a common formalism for describing photogrammetrical survey and archaeological knowledge stemming from ontologies: Indeed, ontologies are fully used to model and store 3D data and archaeological knowledge. Xe equip this formalism with a qualitative representation of time. Stratigraphic analyses (both of excavated deposits and of upstanding structures) are closely related to E. C. Harris theory of “Stratigraphic Unit” (“US” from now on). Every US is connected to the others by geometric, topological and, eventually, temporal links, and are recorded by the 3D photogrammetric survey. However, the limitations of the Harris Matrix approach lead to use another representation formalism for stratigraphic relationships, namely Qualitative Constraints Networks (QCN) successfully used in the domain of knowledge representation and reasoning in artificial intelligence for representing temporal relations.

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35

Del Mondo, Géraldine, Peng Peng, Jérôme Gensel, Christophe Claramunt, and Feng Lu. "Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation." ISPRS International Journal of Geo-Information 10, no. 8 (August 12, 2021): 541. http://dx.doi.org/10.3390/ijgi10080541.

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This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.

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36

Zhao, Ling, Hanhan Deng, Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun, and Yun Chen. "Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding." Symmetry 12, no. 2 (February 1, 2020): 199. http://dx.doi.org/10.3390/sym12020199.

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

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37

Busacchi, Vinicio. "HISTORICAL FACTUALITY AND REPRESENTATION." SWS Journal of SOCIAL SCIENCES AND ART 1, no. 1 (July 23, 2019): 13–25. http://dx.doi.org/10.35603/ssa2019/issue1.02.

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Historical facts are not objects; rather, they are representational processes within other processes that also produced objects and left traces. These latter ones are themselves not historical facts either but are the same as historical facts in a given time and acquire meaning and significance with respect to that particular time. Therefore, the ‘historical-real’ is constitutively representational and constitutively temporal because it is a process. The question of what is a given truth in history then becomes the dilemma of creating a representative reconstruction of the process of (past) events that is close to the ‘real’ events as they are given in that specific time. Those ‘real’ events have been conceived, represented, lived, created, and narrated. The interweaving of the theory of history and the [cognitive] theory of representation is revealed as a central interlacing that could be proposed between the theory of history and the theory of narrative on the one hand and the theory of history and the theory of action on the other. From one perspective, history is about other people, other institutions, other representations and other visions of the world. It is about people who lived in different eras, who have created and inhabited different institutions, who spoke other languages, who embraced other conceptions and beliefs and so on. From another perspective, however, historians are not faced with a radical otherness. History describes people like us, but it is we who are the heirs of those cultures, those institutions, that wealth of knowledge, those skills, those beliefs and so on, and we are not without tools to recover, reproduce or re-present them.

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38

Chala, Oksana. "Construction of temporal rules for the representation of knowledge in information control systems." Advanced Information Systems 2, no. 3 (November 28, 2018): 54–59. http://dx.doi.org/10.20998/2522-9052.2018.3.09.

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39

Rybina, Galina, and Dmitry Demidov. "Automated acquisition, representation and processing of temporal knowledge in dynamic integrated expert systems." Procedia Computer Science 145 (2018): 448–52. http://dx.doi.org/10.1016/j.procs.2018.11.105.

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40

Lee, Anthony J. T., Han-Pang Chiu, and Ping Yu. "3D C-string: a new spatio-temporal knowledge representation for video database systems." Pattern Recognition 35, no. 11 (November 2002): 2521–37. http://dx.doi.org/10.1016/s0031-3203(01)00224-2.

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41

Lyu, Liting, Zhifeng Wang, Haihong Yun, Zexue Yang, and Ya Li. "Deep Knowledge Tracing Based on Spatial and Temporal Representation Learning for Learning Performance Prediction." Applied Sciences 12, no. 14 (July 17, 2022): 7188. http://dx.doi.org/10.3390/app12147188.

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Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students’ learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students’ learning history sequence, and then further extracts temporal features to extract deeper hidden information. Specifically, firstly, the DKT-STDRL model uses CNN to extract the spatial feature information of students’ exercise sequences. Then, the spatial features are connected with the original students’ exercise features as joint learning features. Then, the joint features are input into the BiLSTM part. Finally, the BiLSTM part extracts the temporal features from the joint learning features to obtain the prediction information of whether the students answer correctly at the next time step. Experiments on the public education datasets ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 prove that DKT-STDRL can achieve better prediction effects than DKT and CKT.

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42

Haddawy, Peter, and Larry Rendell. "Planning and decision theory." Knowledge Engineering Review 5, no. 1 (March 1990): 15–33. http://dx.doi.org/10.1017/s026988890000521x.

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Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.

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43

Ganapathy, Jayanthi, and Uma V. "Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process." International Journal of Intelligent Information Technologies 15, no. 2 (April 2019): 32–53. http://dx.doi.org/10.4018/ijiit.2019040103.

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Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.

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44

Delgoshaei, Parastoo, Mohammad Heidarinejad, and Mark A. Austin. "A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning." Sustainability 14, no. 10 (May 11, 2022): 5810. http://dx.doi.org/10.3390/su14105810.

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Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

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45

Adler, Melissa, Joseph T. Tennis, Stas̆a Milojević, Seth van Hooland, Corinne Rogers, and Jevin D. West. "The Temporal dimension in the study of knowledge bases: Approaches to understanding knowledge creation and representation over time." Proceedings of the American Society for Information Science and Technology 50, no. 1 (2013): 1–3. http://dx.doi.org/10.1002/meet.14505001018.

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46

Gebre, Haymanot Alalo, Jun Seong Choi, and Jong Hee Park. "A Knowledge Representation Scheme Formalizing Spatio-Temporal Aspects of Dynamic Situations in Virtual Environments." International Journal of Contents 11, no. 1 (March 28, 2015): 21–30. http://dx.doi.org/10.5392/ijoc.2015.11.1.021.

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47

Chala, Oksana. "MODEL OF GENERALIZED REPRESENTATION OF TEMPORAL KNOWLEDGE FOR TASKS OF SUPPORT OF ADMINISTRATIVE DECISIONS." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (3) (July 9, 2020): 14–18. http://dx.doi.org/10.20998/2079-0023.2020.01.03.

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48

Zarri, Gian Piero. "Representation of temporal knowledge in events: The formalism, and its potential for legal narratives." Information & Communications Technology Law 7, no. 3 (October 1998): 213–41. http://dx.doi.org/10.1080/13600834.1998.9965792.

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49

Chen, Jun, Hao Wu, Songnian Li, Anping Liao, Chaoying He, and Shu Peng. "Temporal logic and operation relations based knowledge representation for land cover change web services." ISPRS Journal of Photogrammetry and Remote Sensing 83 (September 2013): 140–50. http://dx.doi.org/10.1016/j.isprsjprs.2013.02.005.

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50

Edwards, Jan, Marios Fourakis, Mary E. Beckman, and Robert A. Fox. "Characterizing Knowledge Deficits in Phonological Disorders." Journal of Speech, Language, and Hearing Research 42, no. 1 (February 1999): 169–86. http://dx.doi.org/10.1044/jslhr.4201.169.

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To aid the development of finer-grained measures of phonological competence within a representation-based approach to phonology, two aspects of nonsymbolic phonological knowledge (knowledge of the acoustic/perceptual space and of the articulatory/production space) were examined in 6 preschool-age children with phonological disorders and 6 typically developing age peers. To evaluate perceptual knowledge, gating and noise-center tasks were used. Children with phonological disorders recognized significantly fewer words than age peers on both tasks. To evaluate production knowledge, spectral and temporal measures were obtained for CV sequences involving both lingual and labial stop consonants. Group differences on this task (such as larger transition slope values from lingual consonants to vowels for children with phonological disorders) were also observed. These differerences were interpreted as indicating that the children with phonological disorders were less able to maneuver jaw and tongue body separately or that they used "ballistic" (i.e., less controlled) gestures from lingual consonants to vowels than their age peers. These results suggest that phonological knowledge is multifaceted, and that seemingly categorical deficits at one level can be linked to less robust representations at other levels.

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