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The concept of modernizing outdated systems in critical infrastructure through digital transformation has been a widely discussed topic nowadays. Following the transition of energy systems, the attention has now shifted towards digitalizing the water distribution systems. These systems are large-scale but outdated systems that frequently encounter various issues and upgrading them would enable easier to identify issues and provide smoother, more efficient service. However, this process requires cautious planning and guidance to ensure that the generated data is reliable, and the system remains operational during the transition. Hence, the primary objective of this paper is to propose a formal model based on ternary relational semantics that can guide the digital transformation of water distribution networks. The proposed model provides a flexible transformation process while making the system generate reliable data. Additionally, this paper demonstrates the application of the proposed model by developing a proof of concept based on a real-world scenario. [ABSTRACT FROM AUTHOR]

Dependency trees reflect rich structural information, which can effectively guide models to understand text semantics and are widely used for relation extraction. However, existing dependency-based models suffer from noise in dependency trees, which distorts the modeling of contextual information, particularly when introducing dependency types, further exacerbating the propagation and accumulation of errors. In addition, rule-based pruning strategies used to eliminate noise in dependency trees may lose crucial information for capturing relational patterns, whereas unconstrained learning-based pruning strategies may introduce more noisy edges. These two aspects of dependency trees may cause models to produce semantic confusion, which is detrimental to relation extraction. In this study, we propose a Contextual Dependency-Aware Graph Convolutional Network (C-DAGCN) that works on complete dependency trees and perceives the importance of word dependencies from multiple angles without considering the noise in dependency trees. This study includes three parts. First, the sequence model captures semantic information for the input sentence and its words. We considered the appropriate outputs as word and sentence-level representations based on model characteristics. Second, the structure model enhances the description of the sentence structure and its guidance. We consider that position knowledge implies associations between words; therefore, we propose the Current Word Global Relative Position Knowledge (CW-GRPK) to globally enhance text structure modeling and reduce the models' overreliance on single noisy structure guidance. A dependency-aware module weighs heuristic syntactic knowledge and non-heuristic CW-GRPK to capture fine-grained word-level interactions that effectively distinguish words related to relational semantics. Subsequently, a dependency-guided module, based on the results of the dependency-aware layer, encodes a knowledge-enhanced word graph without losing global linguistic patterns. Third, we constructed high-level linguistic representations based on the results of the sequence and structure model in C-DAGCN as a relational representation and mapped it to the relational decision space for relational prediction. The experimental results demonstrate that our method outperforms nine strong baseline models on two benchmark datasets. Compared with the best baseline Attentive Graph Convolutional Networks (A-GCN) model (Tian et al., 2021), our proposed C-DAGCN model with 90.15 macro-average F1 and 79.52 micro-average F1 achieves 0.28 macro-average F1 and 0.47 micro-average F1 improvement on the SemEval2010-Task8 and ACE2005 datasets, respectively. 1 1 Our code is available at https://github.com/dymyyc/RE%5fC-DAGCN. The released code when run produces results which are the same as those reported in this paper. • A kind of position knowledge globally enhances text structure modeling. • A dependency-aware module captures fine-grained word-level interactions. • Our model constructs a high-level linguistic representation for relation extraction. • Our model outperforms nine strong baselines on the two benchmark datasets. [ABSTRACT FROM AUTHOR]

Abstract Brouwer's views on the foundations of mathematics have inspired the study of intuitionistic logic, including the study of the intuitionistic propositional calculus and its extensions. The theory of these systems has become an independent branch of logic with connections to lattice theory, topology, modal logic, and other areas. This paper aims to present a modern account of semantics for intuitionistic propositional systems. The guiding idea is that of a hierarchy of semantics, organized by increasing generality: from the least general Kripke semantics on through Beth semantics, topological semantics, Dragalin semantics, and finally to the most general algebraic semantics. While the Kripke, topological, and algebraic semantics have been extensively studied, the Beth and Dragalin semantics have received less attention. We bring Beth and Dragalin semantics to the fore, relating them to the concept of a nucleus from pointfree topology, which provides a unifying perspective on the semantic hierarchy. [ABSTRACT FROM AUTHOR]

The relational triple extraction is a fundamental and essential information extraction task. The existing approaches of relation triple extraction achieve considerable performance but still suffer from 1) treating the relation between entities as a meaningless label while ignoring the relational semantic information of the relation itself and 2) ignoring the interdependence and inseparability of three elements of the triple. To address these problems, this paper proposes a R elational P rompt approach, based on which constructs a S ingle-module S ingle-step relational triple extraction model (RPSS). In particular, the proposed relational prompt approach consist of a relational hard-prompt and a relational soft-prompt, while provide take into account different level of relational semantic information, covering both the token-level and the feature-level relational prompt information. Then, we jointly encode entities and relational prompts to obtain a unified global representation. We mine deep correlations between different embeddings through attention mechanism and then construct a triple interaction matrix. Then, all triples could be directly extracted from a single module in a single step. Experiments demonstrate the effectiveness of the relational prompt approach, as well as relational semantics and triple integrity are essential for relation extraction. Experimental results on two benchmark datasets demonstrate our model outperforms current state-of-the-art models. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have received considerable attention in recent years due to their unique ability to model both topologies and semantics in the graphs. In this paper, we explore the use of GNNs in learning low-dimensional node representations in Heterogeneous Information Networks (HINs), which retain rich semantic information across multiple types of nodes and relations. Existing methods for HINs generally focus on modeling heterogeneity at either the node level or the relation level, but not both. As a result, these methods often fall short of optimal performance. To address this issue, we propose a novel Semantic- and Relation-aware Heterogeneous Graph neural Network, dubbed SR-HGN, which jointly incorporates rich semantics preserved on nodes and relations. Our approach involves projecting the HINs into a low-dimensional vector space through two steps: node-level aggregation and type-level aggregation. The node-level aggregation employs an attention mechanism to create relation vectors by aggregating messages from neighborhoods connected via the same type of relation. The type-level aggregation leverages relation vectors to aggregate node representations. In particular, we introduce semantic-aware attention and relation-aware attention in the type-level aggregation to model the contributions of relation vectors, in order to simultaneously gain knowledge from node semantics and relational information. Unlike other approaches that rely on pre-defined meta-paths, our model can be readily applied to most real-world applications without requiring any domain knowledge. To validate the effectiveness of our proposed approach, we conducted extensive experiments on three public datasets. Experimental results demonstrate that the SR-HGN significantly outperforms state-of-the-art baselines on node classification and node clustering tasks. [ABSTRACT FROM AUTHOR]

Relational triple extraction is aimed at detecting entity pairs with relations from sentences, which is a key technology for large-scale knowledge graph construction. Recent studies focus on the overlapping triple problem, where multiple relational triples may have overlaps in a sentence. However, these methods disregard the bidirectionality of triple extraction, which may lead to extracting invalid triples. In addition, many relational triples are labeled in datasets of the triple extraction task, implying domain knowledge information of these datasets, but current methods rarely consider it. In this paper, we present a bidirectional relation-guided attention network with semantics and knowledge (BRASK) for relational triple extraction. BRASK is a bidirectional extraction framework that is based on multitask learning and contains forward and backward triple extraction tasks. Forward triple extraction and backward triple extraction are parallel and complementary, which can obtain predicted results with high confidence. We utilize semantic relations and knowledge relations as guidance in forward triple extraction and backward triple extraction, respectively, thus integrating general and domain knowledge into our model. In addition, we adopt an attention mechanism to learn fine-grained sentence representations for different relations. BRASK can solve the triple overlap problem and capture bidirectional dependencies between subjects and objects. Experimental results show that BRASK achieves new state-of-the-art results in two public datasets, which demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

We define a Kripke semantics for Intuitionistic Higher-Order Logic with constraints formulated within Church's Theory of Types via the addition of a new constraint base type. We then define an executable fragment, hoHH ( C ) , of the logic: a higher-order logic programming language with typed λ -abstraction, implication and universal quantification in goals and constraints, and give a modified model theory for this fragment. Both formal systems are shown sound and complete for their respective semantics. We also solve the impredicativity problem in λ Prolog semantics, namely how to give a definition of truth without appealing to induction on subformula structure. In the last section we give a simple semantics-based conservative extension proof that the language hoHH ( C ) satisfies a uniformity property along the lines of [39] . [ABSTRACT FROM AUTHOR]

We present a sound and complete graph calculus for modalities. This calculus is a general framework for expressing modal formulas and frame properties, with a rich repertoire of relations, and reasoning about them in a uniform manner. The calculus employs graphical interpretations of logical operators and builds graphical objects that represent conditions on Kripke structures. [ABSTRACT FROM AUTHOR]

The modal characterization theorem by J. van Benthem characterizes classical modal logic as the bisimulation invariant fragment of first-order logic. In this paper, we prove a similar characterization theorem for intuitionistic modal logic. For this purpose we introduce the notion of modal asimulation as an analogue of bisimulations. The paper treats four different fragments of first-order logic induced by their respective versions of Kripke-style semantics for modal intuitionistic logic. It is shown further that this characterization can be easily carried over to arbitrary first-order definable subclasses of classical first-order models. [ABSTRACT FROM AUTHOR]

Justification logics are modal-like logics that provide a framework for reasoning about justifications. This paper introduces labeled sequent calculi for justification logics, as well as for combined modal-justification logics. Using a method due to Sara Negri, we internalize the Kripke-style semantics of justification and modal-justification logics, known as Fitting models, within the syntax of the sequent calculus to produce labeled sequent calculi. We show that all rules of these systems are invertible and the structural rules (weakening and contraction) and the cut rule are admissible. Soundness and completeness are established as well. The analyticity for some of our labeled sequent calculi are shown by proving that they enjoy the subformula, sublabel and subterm properties. We also present an analytic labeled sequent calculus for S4LPN based on Artemov–Fitting models. [ABSTRACT FROM AUTHOR]

In this work, we combine features from Justification Logics and Logics of Plausibility-Based Beliefs to build a logic for Multi-Agent Systems where each agent can explicitly state his justification for believing in a given sentence. Our logic is a normal modal logic based on the standard Kripke semantics, where we provide a semantic definition for the evidence terms and define the notion of plausible evidence for an agent, based on plausibility relations in the model. As we deal with beliefs, justifications can be faulty and unreliable. In our logic, agents can disagree not only over whether a sentence is true or false, but also on whether some evidence is a valid justification for a sentence or not. After defining our logic and its semantics, we provide a strongly complete axiomatic system for it, show that it has the finite model property, analyze the complexity of its Model-Checking Problem and show that its Satisfiability Problem has the same complexity as the one from basic modal logics. Thus, this logic seems to be a good first step for the development of a dynamic logic that can model the processes of argumentation and debate in Multi-Agent Systems. [ABSTRACT FROM AUTHOR]

By means of a countermodel we show that the homotopy equivalence of the fibers of a Kan fibration over a connected base cannot be proved constructively. [ABSTRACT FROM AUTHOR]

In this paper we show that the satisfaction of timed CTL (TCTL) formulas under the natural continuous semantics for both discrete-time and dense-time timed Kripke structures can be reduced to a model-checking problem in the pointwise semantics for a large class of timed Kripke structures, which includes many discrete-event systems. We then present a TCTL model checking algorithm for the pointwise case. An important consequence of our results is that they together describe a sound and complete TCTL model checking procedure for time-robust real-time rewrite theories also for dense time domains. We have implemented such a TCTL model checker for Real-Time Maude. Our model checker provides for free a sound and complete TCTL model checker for subsets of modeling languages, such as Ptolemy II and (Synchronous) AADL, which have Real-Time Maude analysis integrated into their tool environments. [ABSTRACT FROM AUTHOR]

We propose methods to enable the relational model to meet scalability and functionality needs of a large-scale social networking services (SNS) system. NewSQL has emerged recently indicating that shared-nothing parallel relational DBMSs can be used to guarantee the ACID properties of transactions while keeping the high scalability of NoSQL. Leading commercial SNS systems, however, rely on a graph – not relational – data model with key–value storage and, for certain operations, suffer overhead of unnecessarily accessing multiple system nodes. Exploiting higher semantics with the relational data model could be the remedy. The solution we offer aims to perform a transaction as a set of independent local transactions whenever possible based on the conceptual semantics of the SNS database schema. First, it hierarchically clusters entities that are sitting on a path of frequently navigated one-to-many relationships, thereby avoiding inter-node joins. Second, when a multi-node delete transaction is performed over many-to-many relationships, it defers deletion of related references until they are accessed later, thereby amortizing the cost of multi-node updates. These solutions have been implemented in Odysseus/SNS — an SNS system using a shared nothing parallel DBMS. Performance evaluation using synthetic workload that reflects the real SNS workload demonstrates significant improvement in processing time. We also note that our work is the first to present the entity-relationship schema and its relational representation of the SNS database. [ABSTRACT FROM AUTHOR]