Готові списки джерел за темами / Graph-logical models

Добірка наукової літератури з теми "Graph-logical models"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Graph-logical models"

Liu, Qi, Dani Yogatama, and Phil Blunsom. "Relational Memory-Augmented Language Models." Transactions of the Association for Computational Linguistics 10 (2022): 555–72. http://dx.doi.org/10.1162/tacl_a_00476.

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Abstract We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model and a knowledge graph for more coherent and logical generation.

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Kovács, Tibor, Gábor Simon, and Gergely Mezei. "Benchmarking Graph Database Backends—What Works Well with Wikidata?" Acta Cybernetica 24, no. 1 (May 21, 2019): 43–60. http://dx.doi.org/10.14232/actacyb.24.1.2019.5.

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Knowledge bases often utilize graphs as logical model. RDF-based knowledge bases (KB) are prime examples, as RDF (Resource Description Framework) does use graph as logical model. Graph databases are an emerging breed of NoSQL-type databases, offering graph as the logical model. Although there are specialized databases, the so-called triple stores, for storing RDF data, graph databases can also be promising candidates for storing knowledge. In this paper, we benchmark different graph database implementations loaded with Wikidata, a real-life, large-scale knowledge base. Graph databases come in all shapes and sizes, offer different APIs and graph models. Hence we used a measurement system, that can abstract away the API differences. For the modeling aspect, we made measurements with different graph encodings previously suggested in the literature, in order to observe the impact of the encoding aspect on the overall performance.

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Shao, Bo, Yeyun Gong, Weizhen Qi, Guihong Cao, Jianshu Ji, and Xiaola Lin. "Graph-Based Transformer with Cross-Candidate Verification for Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8807–14. http://dx.doi.org/10.1609/aaai.v34i05.6408.

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In this paper, we present a graph-based Transformer for semantic parsing. We separate the semantic parsing task into two steps: 1) Use a sequence-to-sequence model to generate the logical form candidates. 2) Design a graph-based Transformer to rerank the candidates. To handle the structure of logical forms, we incorporate graph information to Transformer, and design a cross-candidate verification mechanism to consider all the candidates in the ranking process. Furthermore, we integrate BERT into our model and jointly train the graph-based Transformer and BERT. We conduct experiments on 3 semantic parsing benchmarks, ATIS, JOBS and Task Oriented semantic Parsing dataset (TOP). Experiments show that our graph-based reranking model achieves results comparable to state-of-the-art models on the ATIS and JOBS datasets. And on the TOP dataset, our model achieves a new state-of-the-art result.

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Ivannikov, A. D., and A. L. Stempkovskiy. "Iterative Methods for Solving Systems of Multi-Valued Logical Equations in the Simulation of Object Control Digital Systems." Mekhatronika, Avtomatizatsiya, Upravlenie 21, no. 9 (September 7, 2020): 511–20. http://dx.doi.org/10.17587/mau.21.511-520.

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The article is devoted to the analysis of methods for solving systems of multivalued logical equations by iteration methods. Iterative methods for solving such systems of equations are a mathematical description of the main process of functional-logical simulation, which is used at the stage of designing digital systems for objects control to verify the correctness of the design. Consideration of multi-valued values of logical signals at the outputs of blocks and elements of digital systems is explained by the fact that in some cases, to analyze the correctness of time relationships when simulating the hardware of digital systems, a several valued representation of logical signals is used, as well as that recently, logical elements are being developed that implement four or more valued logic. Based on the analysis of the structure of the system of logical equations used in digital hardware simulation, using graph and logical models, an analysis is made of the existence of solutions and their number. Iterative methods of a simple and generalized iteration are analyzed, a relationship is shown between the number of solutions of the system of equations and its graph representation, which reflects a given circuit of connecting elements of the hardware of a digital control system. For the generalized iteration method, options with a different structure of the iteration trace are considered, in particular, it is shown that, with a certain structure of the iteration trace, the generalized iteration turns into a simple iteration or Seidel iteration. It is shown that the generalized iteration most adequately describes the process of simulating the switching of logical signals in a simulated circuit of digital control systems hardware. The correspondence between various options of functional-logical simulation of digital systems and the used methods of iterative solution of systems of logical equations is shown.

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Gates, Alexander J., Rion Brattig Correia, Xuan Wang, and Luis M. Rocha. "The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling." Proceedings of the National Academy of Sciences 118, no. 12 (March 18, 2021): e2022598118. http://dx.doi.org/10.1073/pnas.2022598118.

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The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization—the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

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Singh, Pritpal, Gaurav Dhiman, and Amandeep Kaur. "A quantum approach for time series data based on graph and Schrödinger equations methods." Modern Physics Letters A 33, no. 35 (November 19, 2018): 1850208. http://dx.doi.org/10.1142/s0217732318502085.

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The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.

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Zhang, Jindou, and Jing Li. "Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts." Algorithms 12, no. 12 (December 10, 2019): 265. http://dx.doi.org/10.3390/a12120265.

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Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced Embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.

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Loganathan, MK, and OP Gandhi. "Reliability enhancement of manufacturing systems through functions." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 10 (November 14, 2015): 1850–68. http://dx.doi.org/10.1177/0954405415612324.

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A reliable system means being able to perform its intended functions. Therefore, ensuring performing of its required functions will help to enhance its reliability. For a manufacturing system (e.g. computer numerical control machines), there are a large number of functions, which complicate and make analysis difficult. In this article, a logical and systems approach of graph theory, which is effective to eliminate such difficulties, is employed. The graph theoretic models do consider the system structure explicitly and are applied to model functions at various hierarchical levels of a manufacturing system. These function digraph models are analysed using matrix approach to examine the cause and effect, which helps to evaluate importance of the function and hence provide direction for system reliability enhancement. A step-by-step methodology is presented, which is illustrated by an example of manufacturing system: computer numerical control drilling machine.

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Lv, Qingna, Yanyun Zhang, Yanyan Li, and Yang Yu. "Research on a Health Care Personnel Training Model Based on Multilayered Knowledge Mapping for the Integration of Nursing Courses and Examinations." Journal of Healthcare Engineering 2022 (February 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/3826413.

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While nursing courses provide a convenient and quick way to learn, they can also be overloaded with resources that can cause learners to become cognitively disoriented or have difficulty choosing nursing course. This paper proposes to fully explore learners’ interests in the case of sparse data by fusing knowledge graph technology and deep recommendation models and adopt knowledge graph to model nursing courses at the semantic level so as to correspond the set of nursing courses to the knowledge graph and solve the problem of lack of logical knowledge relationships. Due to the specificity of its positions, the nursing profession must accurately position the nursing professional curriculum standards in the process of determining the talent cultivation model based on the nursing professional positions and the admission requirements for nursing practice qualification. Through linear feature mining based on the knowledge graph, entities and relationships are used to intuitively display the interest paths of nursing professional learners and enhance the interpretability of recommendations.

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Kadyrov, Amanulla, and Amir Kadyrov. "Foundations of General Theory of Discrete Dynamic, Relay and Logical-Dynamic Systems Based on Physical Decomposition and Graph Models." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 10. Innovatcionnaia deiatel’nost’, no. 2 (May 2015): 80–89. http://dx.doi.org/10.15688/jvolsu10.2015.2.8.

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Дисертації з теми "Graph-logical models"

Морозов, Костянтин В’ячеславович. "Методи і засоби побудови моделей поведінки небазових відмовостійких багатопроцесорних систем". Doctoral thesis, Київ, 2021. https://ela.kpi.ua/handle/123456789/40485.

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Дисертація присвячена проблемі побудови графо-логічних моделей (GL-моделей) небазових відмовостійких багатопроцесорних систем (ВБС) з метою розрахунку їх параметрів надійності шляхом проведення статистичних експериментів із вищезгаданими моделями. Запропоновано метод перетворення GL-моделей за рахунок модифікації виразу будь-якої реберної функції так званої МВР-моделі. Метод базуються на зміні як однієї, так і обох частин виразу реберної функції та дозволяє модифікувати модель так, що на деяких векторах стану системи вона, на відміну від оригінальної, починає показувати роботозданий і/або нероботоздатний стан. Проаналізовано межі впливу для загального випадку модифікації як однієї, так і кількох реберних функцій на зміну поведінки моделі. Вперше запропоновано аналітичний апарат, що дозволяє визначати вектор стану t-діагностованої ВБС на основі результатів взаємного тестування процесорів, згідно із моделлю Препарати-Метца-Чена (ПМЧ-модель), для довільної топології зв’язків між ними. Вперше запропоновано метод побудови GL-моделей так званих зважених систем, в яких кожний компонент має деяку вагу, що характеризує його вклад в роботоздатність системи в цілому, а також узагальнення методу побудови GL-моделей, що базується на використанні в якості реберних функцій багатозначної логіки та дозволяє використовувати їх для побудови моделей систем, для яких, як і їх компонентів характерні більше двох станів роботоздатності. Запропоновано метод побудови GL-моделей ієрархічних систем шляхом композиції декількох МВР-моделей, а також моделей систем, що складаються з кількох підсистем та мають ковзний резерв.

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Figueiredo, Daniel Oliveira. "Logical foundations and computational tools for synthetic biology." Doctoral thesis, 2020. http://hdl.handle.net/10773/29829.

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The study and development of tools for computational systems is an area where we can easily find diverse works and, nowadays, it is one of the dominant topics when we think about the research on computer science. As consequence, the field of computation has access to a solid theoretical basis, as well as to a wide collection of algorithms and tools (such as model checkers). The focus of this thesis is to look at a biological system under a computational perspective, where cells and gens replace the role of transistors as the fundamental elements of a computational system. Indeed, the notion of computation is often compared to the functioning of a brain in an animal. Taking into account this point of view, the goal of this work is to revisit basic concepts present on the study of intracelular dynamics, which are fundamentally the same for all living organisms, under a computer science perpective. Thus, we intend to understand how we can apply concepts, algorithms and computational tools, which are used the field of Computer Science, to the mentioned biological systems. In particular, we start by describing some kinds of models used to model the intracellular dynamics of living organisms – Piecewise linear models and Boolean networks. Hence, we propose a new perspective over Piecewise linear model, considering these models as reconfigurable. This allows one to use computational tools like KeYmaera and dReach to reason about these models. Afterward, discretizing this kind of model but maintaining the notion of reconfigurability, we obtain the concept of reactive Boolean network, based on the switch graph formalism, and propose a logical language to express and formally check properties of these systems along with a notion of bisimulation. In what relates to Boolean networks, we provide a new point of view over the notion of “terminal”, by relating it to the notion of bisimulation, which is widely known in the area of Computater Science. Then, we focus in the asymptotic graph method and, after a fundamental study, we propose a generalized and intermediate method that is less efficient in a computational perspective but more suitable to the intended context. Finally, we consider a new kind of stochastic model which is obtaining embeding weights in edges of switch graphs. We also develop an extension of PRISM model checker – rPrism – to ease the study of this specific class of stochastic models.
O estudo e desenvolvimento de ferramentas para sistemas computacionais é uma área onde facilmente podemos encontrar vários trabalhos, sendo hoje em dia um dos tópicos dominantes na investigação em Ciências da Computação, permitindo, a esta área, o acesso a uma vasta base teórica, além de diversos algoritmos e ferramentas tais como model checkers. Esta tese centra-se na ideia de que um sistema biológico pode ser visto, em certa forma, como um sistema computacional, onde células e genes substituem o papel dos transístors. De facto, a noção de computação é, muitas vezes, associada ao funcionamento do cérebro de seres vivos. Tendo em conta este ponto de vista, o objetivo deste trabalho é revisitar alguns conceitos básicos no estudo de dinâmicas intracelulares, comuns a todos os seres vivos, de um ponto de vista computacional, de forma a averiguar como podemos aplicar a estes sistemas os conceitos, algoritmos e ferramentas computacionais usadas na área da Informática. Em particular, começamos por revisitar vários tipos de modelos usados para descrever a dinâmica intracelular de seres vivos – modelos lineares por partes e redes Booleanas. De seguida, propomos uma nova perspetiva sobre os modelos lineares por partes, considerando estes modelos como reconfiguráveis. Isto permite-nos o uso de ferramentas computacionais como o KeYmaera e dReach. Por outro lado, discretizando este modelos mas mantendo a noção de reconfigurabilidade, obtemos a noção de rede Booleana reactiva, baseada no formalismo de switch graphs, e propomos uma linguagem lógica para expressar e verificar propriedades destes sistemas bem como uma noção de bissimulação. No que diz respeito a modelos Booleanos, apresentamos uma nova visão sobre a noção de “terminal” (ou atrator) de forma a relacioná-lo com a noção de bisimulação, muito usada em computação. De seguida, focamos a atenção no método de obtenção do gráfico assintótico e, após um estudo profundo, propomos um método intermédio que, apesar de menos eficiente a nível computacional, se mostra mais adequado ao contexto. Finalmente, consideramos um novo tipo de modelo estocástico ao incorporar pesos na arestas dos switch graphs e desenvolvemos uma extensão do PRISM model checker – rPrism – para estudar esta classe específica de modelos.
Programa Doutoral em Matemática Aplicada

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Частини книг з теми "Graph-logical models"

Whelan, Ken, Oliver Ray, and Ross D. King. "Representation, Simulation, and Hypothesis Generation in Graph and Logical Models of Biological Networks." In Methods in Molecular Biology, 465–82. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-173-4_26.

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Locuratolo, Elvira Immacolata. "Quality Evaluation for Evolving Conceptual Database Design." In Encyclopedia of Information Science and Technology, Fourth Edition, 2020–30. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch175.

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The article is concerned with the proposal of a new approach of conceptual database design, called evolving conceptual database design, which exploits the structure for the preservation of database classes/concepts within the design. In order to discuss the opportunity to take into consideration this approach, the structure is constructed starting from a database conceptual graph. The leaves of the structure are mapped to a logical/object database graph. Horizontal steps of constructive logical database design extend the model. The computational costs required to design the structure for the preservation of database classes/concepts, as well as the qualitative/conceptual costs of the logical models resulting from the constructive design, are discussed.

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Locuratolo, Elvira Immacolata. "Quality Evaluation for Evolving Conceptual Database Design." In Advances in Computer and Electrical Engineering, 632–44. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7598-6.ch046.

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The chapter is concerned with the proposal of a new approach of conceptual database design, called evolving conceptual database design, which exploits the structure for the preservation of database classes/concepts within the design. In order to discuss the opportunity to take into consideration this approach, the structure is constructed starting from a database conceptual graph. The leaves of the structure are mapped to a logical/object database graph. Horizontal steps of constructive logical database design extend the model. The computational costs required to design the structure for the preservation of database classes/concepts, as well as the qualitative/conceptual costs of the logical models resulting from the constructive design, are discussed.

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Zaitsev, Dmitry A. "Composition of Functional Petri Nets." In Formal Methods in Manufacturing Systems, 404–64. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4034-4.ch017.

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Functional Petri nets and subnets are introduced and studied for the purpose of speed-up of Petri nets analysis with algebraic methods. The authors show that any functional subnet may be generated by a composition of minimal functional subnets. They propose two ways to decompose a Petri net: via logical equations solution and with an ad-hoc algorithm, whose complexity is polynomial. Then properties of functional subnets are studied. The authors show that linear invariants of a Petri net may be computed from invariants of its functional subnets; similar results also hold for the fundamental equation of Petri nets. A technique for Petri nets analysis using composition of functional subnets is also introduced and studied. The authors show that composition-based calculation of invariants and solutions of fundamental equation provides a significant speed-up of computations. For an additional speed-up, they propose a sequential composition of functional subnets. Sequential composition is formalised in the terms of graph theory and was named the optimal collapse of a weighted graph. At last, the authors apply the introduced technique to the analysis of Petri net models of such well-known networking protocols as ECMA, TCP, BGP.

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Hawrylak, Peter J., Chris Hartney, Mauricio Papa, and John Hale. "Using Hybrid Attack Graphs to Model and Analyze Attacks against the Critical Information Infrastructure." In Critical Information Infrastructure Protection and Resilience in the ICT Sector, 173–97. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2964-6.ch009.

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The Smart Grid will incorporate computer networking technologies into the electrical generation, transmission, and distribution sectors. Thus, there will be an underlying Critical Information Infrastructure (CII) based on these network connections. This CII is vulnerable to traditional cyber or computer based attacks typically geared toward disabling devices or networks. However, the Smart Grid is also vulnerable to physical attacks where sensors are tricked into reporting false conditions that cause the control system to react in an inappropriate manner. Cyber-physical attacks blending both cyber and physical attack components are also a possibility. Techniques to model cyber-attacks exist, and this chapter presents a modeling methodology, termed hybrid attack graphs, to model cyber-physical attacks. The hybrid attack graph formalism can be applied to develop best practice guidelines and security patches for the Smart Grid. This formalism can also be applied to other cyber-physical domains as well to help bridge the gap between the physical, logical, and network domains.

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Hawrylak, Peter J., Chris Hartney, Mauricio Papa, and John Hale. "Using Hybrid Attack Graphs to Model and Analyze Attacks against the Critical Information Infrastructure." In Cyber Behavior, 2098–124. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5942-1.ch110.

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Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "Big Data Warehouse Automatic Design Methodology." In Big Data, 454–92. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch023.

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Traditional data warehouse design methodologies are based on two opposite approaches. The one is data oriented and aims to realize the data warehouse mainly through a reengineering process of the well-structured data sources solely, while minimizing the involvement of end users. The other is requirement oriented and aims to realize the data warehouse only on the basis of business goals expressed by end users, with no regard to the information obtainable from data sources. Since these approaches are not able to address the problems that arise when dealing with big data, the necessity to adopt hybrid methodologies, which allow the definition of multidimensional schemas by considering user requirements and reconciling them against non-structured data sources, has emerged. As a counterpart, hybrid methodologies may require a more complex design process. For this reason, the current research is devoted to introducing automatisms in order to reduce the design efforts and to support the designer in the big data warehouse creation. In this chapter, the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model. In order to automate the whole design process, the methodology has been implemented using logical programming.

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Тези доповідей конференцій з теми "Graph-logical models"

Li, Ronghan, Lifang Wang, Shengli Wang, and Zejun Jiang. "Asynchronous Multi-grained Graph Network For Interpretable Multi-hop Reading Comprehension." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/531.

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Multi-hop machine reading comprehension (MRC) task aims to enable models to answer the compound question according to the bridging information. Existing methods that use graph neural networks to represent multiple granularities such as entities and sentences in documents update all nodes synchronously, ignoring the fact that multi-hop reasoning has a certain logical order across granular levels. In this paper, we introduce an Asynchronous Multi-grained Graph Network (AMGN) for multi-hop MRC. First, we construct a multigrained graph containing entity and sentence nodes. Particularly, we use independent parameters to represent relationship groups defined according to the level of granularity. Second, an asynchronous update mechanism based on multi-grained relationships is proposed to mimic human multi-hop reading logic. Besides, we present a question reformulation mechanism to update the latent representation of the compound question with updated graph nodes. We evaluate the proposed model on the HotpotQA dataset and achieve top competitive performance in distractor setting compared with other published models. Further analysis shows that the asynchronous update mechanism can effectively form interpretable reasoning chains at different granularity levels.

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Geh, Renato, Denis Mauá, and Alessandro Antonucci. "Learning Probabilistic Sentential Decision Diagrams by Sampling." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11968.

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Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit’s structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit’s graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains.

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van Bremen, Timothy, and Ondřej Kuželka. "Lifted Inference with Tree Axioms." 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.2021/57.

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We consider the problem of weighted first-order model counting (WFOMC): given a first-order sentence ϕ and domain size n ∈ ℕ, determine the weighted sum of models of ϕ over the domain {1, ..., n}. Past work has shown that any sentence using at most two logical variables admits an algorithm for WFOMC that runs in time polynomial in the given domain size (Van den Broeck 2011; Van den Broeck, Meert, and Darwiche 2014). In this paper, we extend this result to any two-variable sentence ϕ with the addition of a tree axiom, stating that some distinguished binary relation in ϕ forms a tree in the graph-theoretic sense.

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Khristodulo, Olga, Vladimir Gvozdev, Oxana Bezhaeva, and Marat Shamsutdinov. "Assessment of the characteristics of the municipal solid waste management system based on the apparatus of the theory of reliability." In International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce277281cca9.86270786.

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Currently, the problem of organizing a municipal solid waste management system (MSW) has become more acute in Russian regions and all over the world. Various environmental and economic problems, as well as problems related to public health negatively affect many aspects of the development of modern society. Therefore, the careful organization of the management of the MSW management system is of great social importance and needs constant improvement. In this paper, we look at the MSW management system from the point of various scientific approaches, namely, structural analysis methods, geoinformation technology methods, reliability theory for complex technical systems, evergetics, graph theory and fuzzy logic methods, including the apparatus of linguistic variables. The purpose of the work is to show the possibility of application and systematic combination of the above scientific methods that have proven themselves well in their fields to a new area of the MSW management systems. However, just application of these approaches is impossible without their further adaptation and systematization from the point of view of existing approaches to the management of similar systems. As part of this study, elements of the MSW management system were identified and a topological model of the system was developed. Structural-logical models of reliability at various levels of abstraction were also built.

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Hochin, Teruhisa, and Hiroki Nomiya. "A Logical and Graphical Operation of a Graph-Based Data Model." In 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science. IEEE, 2009. http://dx.doi.org/10.1109/icis.2009.130.

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de Sousa, Victor Martins, and Luis Mariano del Val Cura. "Logical Design of Graph Databases from an Entity-Relationship Conceptual Model." In iiWAS2018: 20th International Conference on Information Integration and Web-based Applications & Services. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3282373.3282375.

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Wang, Yashen, and Huanhuan Zhang. "A Novel Logical Query Representation Learning Model on Knowledge Graph for Interpretable Knowledge Reasoning." In 2020 3rd International Conference on Unmanned Systems (ICUS). IEEE, 2020. http://dx.doi.org/10.1109/icus50048.2020.9274950.

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Thomas Mason, Robert. "NoSQL Databases and Data Modeling for a Document-oriented NoSQL Database." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2245.

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NoSQL databases are an important component of Big Data for storing and retrieving large volumes of data. Traditional Relational Database Management Systems (RDBMS) use the ACID theorem for data consistency, whereas NoSQL Databases use a non-transactional approach called BASE. RDBMS scale vertically and NoSQL Databases can scale both horizontally (sharding) and vertically. Four types of NoSQL databases are Document-oriented, Key-Value Pairs, Column-oriented and Graph. Data modeling for Document-oriented databases is similar to data modeling for traditional RDBMS during the conceptual and logical modeling phases. However, for a physical data model, entities can be combined (denormalized) by using embedding. What was once called a foreign key in a traditional RDBMS is now called a reference in a Document-oriented NoSQL database.

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Zhao, Yue, Chunling Dong, and Qin Zhang. "Pilot Study of Diagnosis on CPR1000 With Dynamic Uncertain Causality Graph." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60181.

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To develop a computerized intelligent system for CPR1000 with functions of fault diagnosis (FD) for safe operation of nuclear power plant (NPP) counts a great deal. Traditional procedures, earlier systems and classic uncertain artificial intelligence methodologies cannot apply well to some severe conditions like Three Mile Island (TMI) accident. To remedy these defectives, in this paper, the methodology of Dynamic Uncertain Causality Graph (DUCG) is brought in for diagnosis of NPP. Causal graphs of DUCG is used to symbolize logical relationships and inserted weighting factor can balance the representation of multi-valued causal relationships and avoid the cases of exponential scale-up of conditional probability table (CPT). With graph-based inference scheme, precise and compact results of FD can be computed and laid out in graphic forms. A DUCG model centering seven faults of secondary circuit in CPR1000 is constructed based on systematic analysis of Unit-I of Ningde NPP of China General Nuclear Power Group (CGNPG). Verification experiment is carried out for fault steam generator (SG) loss feed-water with sensor measurements from corresponding simulation machine NS-FSS manufactured by China Nuclear Power Simulation Technology Co, Ltd (CNPSC). Superiorities of DUCG in FD emerge by contrast with other techniques, which lay theoretical foundation for the promotion of development of CPR1000 intelligent system.

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Zhou, Zhenxu, Chunling Dong, and Qin Zhang. "Dynamic Fault Tree Analysis Based on Dynamic Uncertain Causality Graph." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81636.

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Dynamic Fault Tree (DFT) has drawn attention from comprehensive industrial systems in recent years. Many analytical approaches are developed to analyze DFT, such as Markov Chain based method, Inclusion-Exclusion Rule based method, and Sum-of-Disjoint-Product theory based method. Novel methods such as Bayesian Network and Petri Net are also used to solve DFT. However, Basic events are usually assumed unrepairable and are restricted to specific probabilistic distributions. And some methods may suffer from combination explosion. This paper applies Dynamic Uncertain Causality Graph (DUCG) to analyze DFT to overcome the aforementioned issues. DUCG is a newly proposed Probabilistic Graphic Model for large complex industrial systems which allows for dynamics, uncertainties and logic cycles. The DUCG based methodology can be summarized as event mapping, logical mapping, and numerical mapping. This paper studies how to map the PAND, FDEP, SEQ AND SPARE sequential logic gates into equivalent representations in DUCG. With the DUCG representation mode, one can analyze DFT with algorithms in DUCG. Future work will be done on benchmark tests and on software development.

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