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Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets. [ABSTRACT FROM AUTHOR]

We show that intuitionistic propositional logic is Carnap categorical: the only interpretation of the connectives consistent with the intuitionistic consequence relation is the standard interpretation. This holds with respect to the most well-known semantics relative to which intuitionistic logic is sound and complete; among them Kripke semantics, Beth semantics, Dragalin semantics, topological semantics, and algebraic semantics. These facts turn out to be consequences of an observation about interpretations in Heyting algebras. [ABSTRACT FROM AUTHOR]

Residuated basic logic (RBL) is the logic of residuated basic algebras, which constitutes a conservative extension of basic propositional logic (BPL). The basic implication is a residual of a non-associative binary operator in RBL. The conservativity is shown by relational semantics. A Gentzen-style sequent calculus GRBL , which is an extension of the distributive full non-associative Lambek calculus, is established for residuated basic logic. The calculus GRBL admits the mix-elimination, subformula, and disjunction properties. Moreover, the class of all residuated basic algebras has the finite embeddability property. The consequence relation of GRBL is decidable. [ABSTRACT FROM AUTHOR]

We consider relational semantics (R-models) for the Lambek calculus extended with intersection and explicit constants for zero and unit. For its variant without constants and a restriction which disallows empty antecedents, Andr'eka and Mikul'as (1994) prove strong completeness. We show that it fails without this restriction, but, on the other hand, prove weak completeness for non-standard interpretation of constants. For the standard interpretation, even weak completeness fails. The weak completeness result extends to an infinitary setting, for so-called iterative divisions (Kleene star under division). We also prove strong completeness results for product-free fragments. [ABSTRACT FROM AUTHOR]

The definition of institution formalizes the intuitive notion of logic in a category-based setting. Similarly, the concept of stratified institution provides an abstract approach to Kripke semantics. This includes hybrid logics, a type of modal logics expressive enough to allow references to the nodes/states/worlds of the models regarded as relational structures, or multi-graphs. Applications of hybrid logics involve many areas of research, such as computational linguistics, transition systems, knowledge representation, artificial intelligence, biomedical informatics, semantic networks, and ontologies. The present contribution sets a unified foundation for developing formal verification methodologies to reason about Kripke structures by defining proof calculi for a multitude of hybrid logics in the framework of stratified institutions. To prove completeness, the article introduces a forcing technique for stratified institutions with nominal and frame extraction and studies a forcing property based on syntactic consistency. The proof calculus is shown to be complete and the significance of the general results is exhibited on a couple of benchmark examples of hybrid logical systems. [ABSTRACT FROM AUTHOR]

This paper shows that the Hilbert system H(C + J)-, given by del Cerro and Herzig (1996), is semantically incomplete. This system is proposed as a proof theory for a combination of classical and intuitionistic propositional logic, which is called C + J. The Kripke semantics for this combination is obtained by adding the semantic clause of classical implication directly to the Kripke semantics for intuitionistic propositional logic. The Hilbert system H(C + J)- does not contain the classical modus ponens, although it contains the intuitionistic modus ponens as a rule, which makes this system semantically incomplete. This paper gives an argument that the system H(C + J)- is semantically incomplete because of the absence of the classical modus ponens. Our way to prove this is based on the logic of paradox, which is a paraconsistent logic proposed by Priest (1979). [ABSTRACT FROM AUTHOR]

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]

This article aims at providing some extension of the modal square of opposition in the light of Ockham's account of modal operators. Moreover, we set forth some significant remarks on the de re–de dicto distinction and on the modal operator of contingency by means of a set-theoretic algebra called numbering semantics. This generalization starting from Ockham's account of modalities will allow us to take into consideration whether Ockham's account holds water or not, and in which case it should be changed. [ABSTRACT FROM AUTHOR]

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach. [ABSTRACT FROM AUTHOR]

The paraconsistent and paracomplete 4-valued logic PŁ4 is originally interpreted with a two-valued Belnap-Dunn semantics. In the present paper, PŁ4 is endowed with both a ternary Routley-Meyer semantics and a binary Routley semantics together with their respective restriction to the 2 set-up case. [ABSTRACT FROM AUTHOR]

SQL is the world's most popular declarative language, forming the basis of the multi-billion-dollar database industry. Although SQL has been standardized, the full standard is based on ambiguous natural language rather than formal specification. Commercial SQL implementations interpret the standard in different ways, so that, given the same input data, the same query can yield different results depending on the SQL system it is run on. Even for a particular system, mechanically checked formalization of all widely-used features of SQL remains an open problem. The lack of a well-understood formal semantics makes it very difficult to validate the soundness of database implementations. Although formal semantics for fragments of SQL were designed in the past, they usually did not support set and bag operations, lateral joins, nested subqueries, and, crucially, null values. Null values complicate SQL's semantics in profound ways analogous to null pointers or side-effects in other programming languages. Since certain SQL queries are equivalent in the absence of null values, but produce different results when applied to tables containing incomplete data, semantics which ignore null values are able to prove query equivalences that are unsound in realistic databases. A formal semantics of SQL supporting all the aforementioned features was only proposed recently. In this paper, we report about our mechanization of SQL semantics covering set/bag operations, lateral joins, nested subqueries, and nulls, written in the Coq proof assistant, and describe the validation of key metatheoretic properties. Additionally, we are able to use the same framework to formalize the semantics of a flat relational calculus (with null values), and show a certified translation of its normal forms into SQL. [ABSTRACT FROM AUTHOR]

Relation classification is an important fundamental task in information extraction, and convolutional neural networks have been commonly applied to relation classification with good results. In recent years, due to the proposed pre-training model BERT, the use of which as a feature extraction architecture has become more and more popular, convolutional neural networks have gradually withdrawn from the stage of NLP, and the relation classification/extraction model based on pre-training BERT has achieved state-of-the-art results. However, none of these methods consider how to accurately capture the semantic features of the relationships between entities to reduce the number of noisy words in a sentence that are not helpful for relation classification. Moreover, these methods do not have a systematic prediction structure to fully utilize the extracted features for the relational classification task. To address these problems, a SpanBert-based relation classification model is proposed in this paper. Compared with existing Bert-based architectures, the model is able to understand the semantic information of the relationships between entities more accurately, and it can fully utilize the extracted features to represent the degree of dependency of a pair of entities with each type of relationship. In this paper, we design a feature fusion method called "SRS" (Strengthen Relational Semantics) and an attention-based prediction structure. Compared with existing methods, the feature fusion method proposed in this paper can reduce the noise interference of irrelevant words when extracting relational semantics, and the prediction structure proposed in this paper can make full use of semantic features for relational classification. We achieved advanced results on the SemEval-2010 Task 8 and the KBP37 relational dataset. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Psicología (1669-2438) is the property of Pontificia Universidad Catolica Argentina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Implicational partial Galois logics and some of their semilinear extensions, such as semilinear extensions satisfying abstract Galois and dual Galois connection properties, have been introduced together with their relational semantics. However, similar extensions satisfying residuated, dual residuated connection properties have not. This paper fills the gaps by introducing those semilinear extensions and their relational semantics. To this end, the class of implicational (dual) residuated-connected prelinear gaggle logics is defined and it is verified that these logics are semilinear. In particular, associated with the contribution of this work, we note the following two: One is that implications can be introduced by residuated connection in semilinear logics. This shows that the residuated, dual residuated connection properties are important and so need to be investigated in semilinear logics. The other is that set-theoretic relational semantics can be provided for semilinear logics. Semilinear logics have been dealt with extensively in algebraic context, whereas they have not yet been performed in the set-theoretic one. [ABSTRACT FROM AUTHOR]

The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, and XLNET, to complete Medical Knowledge Graphs has become a popular research topic. The existing work focuses mainly on relationship completion and has rarely solved entities and related triples. In this paper, a framework to predict RDF triples for Medical Knowledge Graphs based on word embeddings (named PTMKG-WE) is proposed, for the specific use for the completion of entities and triples. The framework first formalizes existing samples for a given relationship from the Medical Knowledge Graph as prior knowledge. Second, it trains word embeddings from big medical data according to prior knowledge through Word2vec. Third, it can acquire candidate triples from word embeddings based on analogies from existing samples. In this framework, the paper proposes two strategies to improve the relation features. One is used to refine the relational semantics by clustering existing triple samples. Another is used to accurately embed the expression of the relationship through means of existing samples. These two strategies can be used separately (called PTMKG-WE-C and PTMKG-WE-M, respectively), and can also be superimposed (called PTMKG-WE-C-M) in the framework. Finally, in the current study, PubMed data and the National Drug File-Reference Terminology (NDF-RT) were collected, and a series of experiments was conducted. The experimental results show that the framework proposed in this paper and the two improvement strategies can be used to predict new triples for Medical Knowledge Graphs, when medical data are sufficiently abundant and the Knowledge Graph has appropriate prior knowledge. The two strategies designed to improve the relation features have a significant effect on the lifting precision, and the superposition effect becomes more obvious. Another conclusion is that, under the same parameter setting, the semantic precision of word embedding can be improved by extending the breadth and depth of data, and the precision of the prediction framework in this paper can be further improved in most cases. Thus, collecting and training big medical data is a viable method to learn more useful knowledge. [ABSTRACT FROM AUTHOR]

Six interesting variants of the logics BN4 and E4--which can be considered as the 4-valued logics of the relevant conditional and (relevant) entailment, respectively--were previously developed in the literature. All these systems are related to the family of relevant logics and contain Routley and Meyer's basic logic B, which is well-known to be specifically associated with the ternary relational semantics. The aim of this paper is to develop reduced general Routley-Meyer semantics for them. Strong soundness and completeness theorems are proved for each one of the logics. [ABSTRACT FROM AUTHOR]

We investigate the relationship between M. De's empirical negation in Kripke and Beth Semantics. It turns out empirical negation, as well as co-negation, corresponds to different logics under different semantics. We then establish the relationship between logics related to these negations under unified syntax and semantics based on R. Sylvan's CCω.

The present paper aims to examine the use of the persuasive rules in texts and images to determine the role of these visual modes in recalling or adding what they mention. Such a study helps set clearly which visual mode is much more convincing, the image or the text. Scholars generally agreed that the visual mode is more persuasive. However, such a statement is general as it does not state exactly which type of visual mode is more convincing. Accordingly, the research questions highlighted here are: What are the persuasive strategies invested in any of these two visual modes, texts and images? And, what is the role these visual modes play to enhance the sense of persuasion? To achieve the objectives, the researchers will adopt the cognitive linguistic theory of frame semantics by Fillmore (1995). Since the study is qualitative, the study's data will be limited to four randomly selected American English advertisements posted on Facebook. Findings have revealed that the number of the evoked frame units is unlimited and subjective, reflecting, as a result, a person's imagination power, his needs, and desires. The study has concluded that the visual, written text is more persuasive than the pictures and photos. The text reflects a bricolage of various persuasive strategies that help pull the attention. Besides, the images provided played a vital role, though secondary to the texts, supporting the textual information and flavor it realistically. [ABSTRACT FROM AUTHOR]

Copyright of Acta Linguistica Lithuanica is the property of Institute of the Lithuanian Language and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This paper introduces the logics of super-strict implications, where a super-strict implication is a strengthening of C.I. Lewis' strict implication that avoids not only the paradoxes of material implication but also those of strict implication. The semantics of super-strict implications is obtained by strengthening the (normal) relational semantics for strict implication. We consider all logics of super-strict implications that are based on relational frames for modal logics in the modal cube. it is shown that all logics of super-strict implications are connexive logics in that they validate Aristotle's Theses and (weak) Boethius's Theses. A proof-theoretic characterisation of logics of super-strict implications is given by means of G3-style labelled calculi, and it is proved that the structural rules of inference are admissible in these calculi. It is also shown that validity in the S5-based logic of super-strict implications is equivalent to validity in G. Priest's negation-as-cancellation-based logic. Hence, we also give a cut-free calculus for Priest's logic. [ABSTRACT FROM AUTHOR]

In this paper we study a new operator of strong modality ..., related to the non-contingency operator Δ. We then provide soundness and completeness theorems for the minimal logic of the ...-operator. [ABSTRACT FROM AUTHOR]

Convolution is a ubiquitous operation in mathematics and computing. The Kripke semantics for substructural and interval logics motivates its study for quantale-valued functions relative to ternary relations. The resulting notion of relational convolution leads to generalised binary and unary modal operators for qualitative and quantitative models, and to more conventional variants, when ternary relations arise from identities over partial semigroups. Convolution-based semantics for fragments of categorial, linear and incidence (segment or interval) logics are provided as qualitative applications. Quantitative examples include algebras of durations and mean values in the duration calculus. [ABSTRACT FROM AUTHOR]

We investigate the relationship between M. De's empirical negation in Kripke and Beth Semantics. It turns out empirical negation, as well as co-negation, corresponds to different logics under different semantics. We then establish the relationship between logics related to these negations under unified syntax and semantics based on R. Sylvan's CCw. [ABSTRACT FROM AUTHOR]

Given the frequency of human error, it seems rational to believe that some of our own rational beliefs are false. This is the axiom of epistemic modesty. Unfortunately, using standard propositional quantification, and the usual relational semantics, this axiom is semantically inconsistent with a common logic for rational belief, namely KD45. Here we explore two alternative semantics for KD45 and the axiom of epistemic modesty. The first uses the usual relational semantics and bisimulation quantifiers. The second uses a topological semantics and standard propositional quantification. We show the two different semantics validate many of the same formulas, though we do not know whether they validate exactly the same formulas. Along the way we address various philosophical concerns. [ABSTRACT FROM AUTHOR]

Sports discourse is in focus in linguistics due to the influential power of linguistic means, especially the MetaSelf metaphor. The metaphor is of anthropomorphic character since it assimilates all phenomena to a person, his/her morphology, and behaviour. The article aims to establish metaphorical models of MetaSelf in the English-language sports discourse. 300 metaphors were selected from the US and UK periodicals, and publications on open online sources of the sports press from 2001 to 2019. Analysis of the metaphors on a syntactic level and a semantic level was based on frame semantics and the semantic-cognitive method as tools of an integrative approach. The analysis allowed constructing productive metaphorical models of MetaSelf: the physiological metaphor, social metaphor, and morbial metaphor. Within each model, there were established relevant semantic frames and slots that underlay the majority of the MetaSelf metaphors, with examples included. The findings demonstrated prevalence of the physiological metaphor. In the English-language sports discourse, the MetaSelf metaphor was presented in simple (33.8%) and simple sentences in a compound (25.6%) on a syntactic level, collocations (23.4%) and separate words (17.2%) - on a semantic level. A distribution of various parts of speech illustrated a complex structure of the MetaSelf metaphor in the English-language sports discourse. The MetaSelf metaphor serves as an important linguistic means of pragmatically influencing the sports discourse, in which sport is likened to a person as a living organism. [ABSTRACT FROM AUTHOR]

Copyright of Ricercare is the property of Universidad EAFIT and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to comparatively investigate the semantic frames of motion verbs in Persian and English within the framework of the frame semantics theory (Fillmore 1977; 1982; 1985). As far as motion verbs are concerned, Manner is considered as one of the motion components expressed by either the verb or any element other than the verb. In English -- a satellite-framed language (Talmy 2000b) -- Manner is shown by motion verbs, whereas in Persian it is typically indicated by non-verbal elements, although there are also some verbs via which Manner is encoded. Within this study, thirty English verbs of manner were selected from among the ones Levin (1993) has introduced and then the verbs were translated into Persian and looked up through the Persian Corpus of Bijankhan to achieve their contexts of use. Next, FrameNet was asked for the semantic frame each verb evoked. Thereafter, comparing the semantic frames in the two languages, it was revealed that not every verb of manner does exist as a Lexical Unit in FrameNet. Likewise, not for every verb was a specified semantic frame either. Moreover, the frames for some other verbs have been defined in such a way that they cannot semantically distinguish those verbs from each other, whereas such distinctions are prominent in both manner verbs and the frames they evoke especially in Persian. [ABSTRACT FROM AUTHOR]

We define Kripke semantics for propositional intuitionistic logic with Suszko's identity (ISCI). We propose sequent calculus for ISCI along with cut-elimination theorem. We sketch a constructive interpretation of Suszko's propositional iden- tity connective. [ABSTRACT FROM AUTHOR]

In this paper, we present a logic MMLnS4 n which is a combination of multilattice logic and modal logic nS4. MMLnS4 n is an extension of Kamide and Shramko's modal multilattice logic which is a multilattice analogue of S4. We present a cut-free hypersequent calculus for MMLnS4 n in the spirit of Restall's one for nS4 and develop a Kripke semantics for MMLnS4 n, following Kamide and Shramko's approach. Moreover, we prove theorems for embed- ding MMLnS4 n into nS4 and vice versa. As a result, we obtain completeness, cut elimination, decidability, and interpolation theorems for MMLnS4 n. Besides, we show the duality principle for MMLnS4 n. Additionally, we introduce a modification of Kamide and Shramko's sequent calculus for their multilat- tice version of S4 which (in contrast to Kamide and Shramko's original one) proves the interdefinability of necessity and possibility operators. Last, but not least, we present Hilbert-style calculi for all the logics in question as well as for a larger class of modal multilattice logics. [ABSTRACT FROM AUTHOR]

Opportunism is an intentional behavior that takes advantage of knowledge asymmetry and results in promoting agents' own value and demoting others' value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents' opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. Agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents' state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism. [ABSTRACT FROM AUTHOR]

ONNILLI-formulas were introduced in [2] and were shown to be the set of formulas that are preserved under monotonic images of descriptive or Kripke frames. As a result, ONNILLI is a syntactically defined set of formulas that axiomatize all stable logics. In this paper, among other things, by proving the uniform interpolation property for ONNILLI we show that ONNILLI is exactly the set of formulas that are preserved in monotonic bijections of descriptive or (finite) Kripke models. This resolves an open problem in [2]. [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]

This study aims at discussing the encyclopedic nature of semantic frames and their role in lexicographic practice. In theoretical terms, we try to understand the vision of Charles Fillmore (1982, 1992) with the proposition of Frames Semantics, as well as to seek in Cognitive Linguistics its principles. In applied terms, we focused on the interface between Frames Semantic and Lexicography and presented, in the sequence, two case studies, both highlighting the encyclopedic vision of the frames and their relevance to the lexicographic practice. We emphasize, finally, the occupation of a specific niche of research, as we return only to the semantic frames. It is noteworthy that other topics of Cognitive Semantics also have their relevance for lexicography. [ABSTRACT FROM AUTHOR]

The present study aims to analyze selected internet memes as examples of social communication from the perspective of Cognitive Linguistics, and to examine more closely the relation between their visual and verbal aspects. Internet memes contain a wide range of constructions (necessary for rebuilding the semantic framework and extracting selected content), which are fragmentary but at the same time sufficient to induce a whole framework of meanings by using their salient features. The multimodal context is considered within the frameworks of conceptual metaphor theory (Lakoff & Johnson, 1980), construal (Langacker, 1987, 2008), frame semantics (Fillmore, 1988), conceptual blending theory (Fauconnier & Turner, 2002) and Discourse Viewpoint Space (Dancygier & Vandelanotte, 2016). The paper will present an analysis of the structure of internet memes, which are rich concepts that spread rapidly and widely, and also invite recipients to actively participate in the construction of the message, thus becoming contributors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of English Studies is the property of Universidad de la Rioja, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Studia z Filologii Polskiej i Slowianskiej is the property of Polish Academy of Sciences, Institute of Slavic Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Modern general system theory proposed a holistic integrative approach based on input-state-output dynamics as opposed to the traditional reductionist detail based approach. Information complexity and uncertainty required a fuzzy system theory, based on fuzzy sets and fuzzy logic. While successful in dealing with analysis, synthesis and control of technical engineering systems, general system theory and fuzzy system theory could not fully deal with humanistic and human-like intelligent systems which combine technical engineering components with human or human-like components characterized by their cognitive, emotional/motivational and behavioral/action levels of operation. Such humanistic systems are essential in artificial intelligence, cognitive and behavioral science applications, organization management and social systems, man-machine systems or human factor systems, behavioral knowledge based economics and finance applications. We are introducing here a “postmodern fuzzy system theory” for controlled state dynamics and output fuzzy systems and fuzzy rule based systems using our earlier postmodern fuzzy set theory and a Kabbalah possible worlds model of modal logic and semantics type. In order to create a postmodern fuzzy system theory, we “deconstruct” a fuzzy system in order to incorporate in it the cognitive, emotional and behavioral actions and expressions levels characteristic for humanistic systems. Kabbalah offers a structural, fractal and hierarchic model for integrating cognition, emotions and behavior. We obtain a canonic deconstruction for a fuzzy system into its cognitive, emotional and behavioral fuzzy subsystems.

Orthologic (OL) is non-classical logic and has been studied as a part of quantum logic. OL is based on an ortholattice and is also called minimal quantum logic. Sequent calculus is used as a tool for proof in logic and has been examined for several decades. Although there are many studies on sequent calculus for OL, these sequent calculi have some problems. In particular, they do not include implication connective and they are mostly incompatible with the cut-elimination theorem. In this paper, we introduce new labeled sequent calculus called LGOI, and show that this sequent calculus solve the above problems. It is already known that OL is decidable. We prove that decidability is preserved when the implication connective is added to OL. [ABSTRACT FROM AUTHOR]

Abstract Some temporal properties of reactive systems, such as actions with duration and temporal aggregations, which are inherently interval-based, can not be properly expressed by the standard, point-based temporal logics LTL, CTL and CTL⁎, as they give a state-by-state account of system evolution. Conversely, interval temporal logics—which feature intervals, instead of points, as their primitive entities—naturally express them. We study the model checking (MC) problem for Halpern and Shoham's modal logic of time intervals (HS), interpreted on Kripke structures, under the homogeneity assumption. HS is the best known interval-based temporal logic, which has one modality for each of the 13 ordering relations between pairs of intervals (Allen's relations), apart from equality. We focus on MC for some HS fragments featuring modalities for (a subset of) Allen's relations meet, met-by, started-by, and finished-by, showing that it is in P NP. Additionally, we provide some complexity lower bounds to the problem. [ABSTRACT FROM AUTHOR]

This study realizes belief/reliability change of a judge in a legal judgment by dynamic epistemic logic (DEL). A key feature of DEL is that possibilities in an agent’s belief can be represented by a Kripke model. This study addresses two difficulties in applying DEL to a legal case. First, since there are several methods for constructing a Kripke model, our question is how we can construct the model from a legal case. Second, since this study employs several dynamic operators, our question is how we can decide which operators are to be applied for belief/reliability change of a judge. In order to solve these difficulties, we have implemented a computer system which provides two functions. First, the system can generate a Kripke model from a legal case. Second, the system provides an inconsistency solving algorithm which can automatically perform several operations in order to reduce the effort needed to decide which operators are to be applied. By our implementation, the above questions can be adequately solved. With our analysis method, six legal cases are analyzed to demonstrate our implementation. [ABSTRACT FROM AUTHOR]

Models of complex systems are widely used in the physical and social sciences, and the concept of layering, typically building upon graph-theoretic structure, is a common feature. We describe an intuitionistic substructural logic called ILGL that gives an account of layering. The logic is a bunched system, combining the usual intuitionistic connectives, together with a non-commutative, non-associative conjunction (used to capture layering) and its associated implications. We give soundness and completeness theorems for a labelled tableaux system with respect to a Kripke semantics on graphs. We then give an equivalent relational semantics, itself proven equivalent to an algebraic semantics via a representation theorem. We utilise this result in two ways. First, we prove decidability of the logic by showing the finite embeddability property holds for the algebraic semantics. Second, we prove a Stone-type duality theorem for the logic. By introducing the notions of ILGL hyperdoctrine and indexed layered frame we are able to extend this result to a predicate version of the logic and prove soundness and completeness theorems for an extension of the layered graph semantics. We indicate the utility of predicate ILGL with a resource-labelled bigraph model. [ABSTRACT FROM AUTHOR]

In a series of papers Alvin Plantinga argued for a serious actu- alist modal semantics based on the notions of a possible world, understood as a maximal possible state of affairs, and of individual essence (haecceity). Plantinga's actualism is known as haecceitism. In spite of the fact that haec- ceitism has been thought by Plantinga to require a Kripke-style semantics, the aim of this paper is to show that it is compatible with constant do- mains semantics and the simplest quantified modal logic. I will argue that not only does this approach have all the advantages of a greater simplicity in combining quantification and modalities, but also it better conforms to the actualist program. [ABSTRACT FROM AUTHOR]

The fragment of the language of modal logic that consists of all implications A → B, where A and B are built from variables, the constant ⊤ (truth), and the connectives ∧ and ◊1,◊2,...,◊m. For the polymodal logic S5m (the logic of m equivalence relations) and the logic K4.3 (the logic of irreflexive linear orders), an axiomatization of such fragments is found and their algorithmic decidability in polynomial time is proved. [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]