Semantic Framework Research Articles

Systemic Functional Linguistic Based on Semantic Web

This research aims to combine a functional and systematic linguistic approach with Semantic Web technology. The main aim of this research is to develop a framework that can facilitate systematized analysis and language modeling in the context of the semantic web. The research method used is a qualitative approach with focused data analysis. The data collected will involve text and web content relevant to the Semantic Web framework. The analysis was carried out using the principles and tools of systematic functional linguistic analysis to identify language structure, communicative function, and meaning in web content. The results of this research are expected to provide new insights into the understanding of how language can be organized and analyzed regularly in the context of the semantic web. Apart from that, this research is also expected to contribute to the development of Semantic Web technology by using a functional linguistic approach. It is hoped that the conclusions of this research will provide a deeper understanding of the role of language in the context of the semantic web and provide a basis for the development of more sophisticated and effective Semantic Web-based applications and systems.

Principle and Application for Rumination Computing Algorithms

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model

AbstractMangrove wetlands are important ecosystems in tropical and subtropical coastal areas, providing wind and wave attenuation and embankment protection functions. However, mangrove wetlands worldwide are facing severe loss and degradation. Accurate identification of mangrove wetland extent is crucial for their protection, but traditional methods struggle to meet the requirements of large‐scale, high‐precision identification. Furthermore, field data collection and annotation in wetlands in complex intertidal zones pose challenges, and the lack of high‐quality training samples limits the application of deep learning methods in this field. This paper proposes a novel semantic segmentation framework for mangrove wetlands called mangrove wetland remote sensing image segmentation network based on segment anything model (MW‐SAM) to address these issues. MW‐SAM is based on the pre‐trained SAM and achieves cross‐domain adaptation through parameter‐efficient fine‐tuning techniques. It introduces wetland‐specific prompts, auxiliary branches, semi‐supervised training, and iterative optimization strategies to improve accuracy and tackle the problem of sample scarcity. Moreover, on the mangrove wetland dataset constructed in this paper, MW‐SAM significantly outperforms SAM and other traditional methods. MW‐SAM provides a new technology for monitoring and protecting mangrove wetlands, and the constructed dataset will be made publicly available, which is expected to promote the development of mangrove wetland conservation efforts.

SABTR: semantic analysis-based tourism recommendation

Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data and the wide variance in user preferences. To address this challenge, we propose a Semantic Analysis-Based Tourism Recommendation framework, abbreviated as SABTR (Semantic Analysis-Based Tourism Recommendation). The framework comprises two stages: Firstly, Latent Dirichlet Allocation (LDA) models are utilized to deeply mine data between users and attractions, constructing two core matrices: the user similarity matrix and the attraction similarity matrix. Secondly, based on the user similarity matrix, similarity calculation methods are applied to predict ratings for tourism spots that users have not yet evaluated. Simultaneously, within the attraction similarity matrix, probability distributions for each attraction across various thematic interests are calculated. When the system identifies a user’s interest in specific types of attractions, SABTR can select a series of related attractions from associated interest tags. Then, these candidate attractions are ranked according to both known and predicted user ratings, ultimately forming personalized attraction packages recommended to users. Extensive experiments have demonstrated that compared to existing tourism recommendation solutions, our method significantly improves the quality of attraction recommendations and enhances user satisfaction.

Sentido y valor de los mercados costeros de lugar en Chile

This article analyses the structural dynamics of social life at five local coastal markets (LCMs) on the Chilean coast (Calbuco, Puerto Montt, Valdivia, Valparaiso and Tongoy). In a semantic narrative framework, we use ethnographic methodology to demonstrate the opacity of local markets—particularly LCMs—in terms of economic and community life. Opacity is a social condition. However, the background reveals profound significance that bears witness to and partially portrays the complexity of markets as places of business, livelihood, life projects and social mobility, where individuality is necessarily intertwined with the community. Our conclusions show that these LCMs go beyond the orthodox definitions that reduce the economy (and the markets themselves) to cost-benefit-motivated interactions between individual agents. Instead, markets go against the grain of this instrumental reductionism and offer remarkable potential for re-thinking, imagining, constructing and co-constructing areas (coastal areas in our analysis) based on collective experience.

Model of Comparative Analysis of the Structure and Semantics of Causal Constructions in Multi-Structural Languages

This article explores the application of constructive grammar, a linguistic approach that integrates cognitive, syntactic, and semantic frameworks to analyse causative constructions in English and Russian languages. Constructive grammar interprets not only the form and content of linguistic components but also the structure itself, which influences and imposes constraints on these components. The study considers the methodological evolution from traditional syntactic theories to cognitive models, particularly the influence of Ch. Fillmore’s work. By focusing on causative constructions, specifically analytical causatives like "The police made him confess," the research draws upon the theories of V.P. Nedyalkov, G.G. Silnitsky, and A.A. Kholodovich, among others, to provide a comparative analysis of causative structures in different languages. Cognitive and generative grammar perspectives, including R. King's spatial metaphor and L. Talmy’s force dynamics, are integrated to establish a comprehensive framework for understanding the universal and language-specific properties of causative expressions. The findings highlight the importance of the cognitive interpretation of syntactic structures, the role of energy transfer between agents and patients, and the diverse semantic types of causative situations. The study concludes by suggesting future research directions in the syntactic classification and formal expression of causative components.

Multiteam Semantics for Interventionist Counterfactuals: Probabilities and Causation

AbstractIn (Barbero and Sandu 2020 Journal of Philosophical Logic, 50, 471-521), we showed that languages encompassing interventionist counterfactuals and causal notions based on them (as e.g. in Pearl’s and Woodward’s manipulationist approaches to causation) as well as information-theoretic notions (such as learning and dependence) can be interpreted in a semantic framework which combines the traditions of structural equation modeling and of team semantics. We now present a further extension of this framework (causal multiteams) which allows us to talk about probabilistic causal statements. We analyze the expressivity resources of two causal-probabilistic languages, one finitary and one infinitary. We show that many causal-probabilistic notions from the literature on causal inference can already be expressed in the finitary language, and we prove a normal form theorem that throws a new light on Pearl’s “ladder of causation”. In addition, we provide an exact semantic characterization of the infinitary language, which shows that this language captures precisely those causal-probabilistic statements that do not commit us to any specific interpretation of probability; and we prove that no usual, countable language is apt for this task.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

O político da linguagem em Special, de Lizzo: uma análise semântico-enunciativa

This descriptive-interpretative study analyzes the functioning of the political aspect of language in the translation of the composition Special, interpreted by Lizzo. It is based on the framework of the Historical Semantics of Enunciation (Guimarães, 2002, 2006, 2007b, 2018). Through the description of the enunciative space and the argumentative orientation, it was found that the political aspect is manifested throughout the enunciation, by the establishment of discursive memories related to racism, fatphobia, media, social exclusion, self-acceptance and Lizzo’s own history/path. These statements intertwine, retracing and re-signifying, transversely, the possibilities of constructed meanings. The political meaning is evidenced by the struggle and constant effort of the Speaker to define her space in the world, as well as the space of the minoritized groups (fat people, black people, and women) that she represents through self-affirmations, which challenge the established hierarchy and other exclusionary representations.

Samba: Semantic segmentation of remotely sensed images with state space model

High-resolution remotely sensed images pose challenges to traditional semantic segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). CNN-based methods struggle to handle high-resolution images due to their limited receptive field, while ViT-based methods, despite having a global receptive field, face challenges when processing long sequences. Inspired by the Mamba network, which is based on a state space model (SSM) to efficiently capture global semantic information, we propose a semantic segmentation framework for high-resolution remotely sensed imagery, named Samba. Samba utilizes an encoder-decoder architecture, with multiple Samba blocks serving as the encoder to efficiently extract multi-level semantic information, and UperNet functioning as the decoder. We evaluate Samba on the LoveDA, ISPRS Vaihingen, and ISPRS Potsdam datasets using the mIoU and mF1 metrics, and compare it with top-performing CNN-based and ViT-based methods. The results demonstrate that Samba achieves unparalleled performance on commonly used remotely sensed datasets for semantic segmentation. Samba is the first to demonstrate the effectiveness of SSM in segmenting remotely sensed imagery, setting a new performance benchmark for Mamba-based techniques in this domain of semantic segmentation. The source code and baseline implementations are available at https://github.com/zhuqinfeng1999/Samba.

Exploring multi-granularity contextual semantics for fully inductive knowledge graph completion

Fully inductive knowledge graph completion (KGC) aims to predict triplets involving both unseen entities and relations. Recent several approaches transform paths between entities into descriptions and modeling semantic correlations between paths using pre-trained language models (PLMs), have emerged as a promising solution for fully inductive reasoning. However, these methods often adopt a simplistic concatenation strategy for path-to-sentence transformation, which impedes PLMs’ ability to capture subtle nuances in context, resulting in sub-optimal path context embeddings. Furthermore, they ignore the high-order semantics underlying the complete context, which can provide richer information for inductive reasoning. To address these issues, we propose a Multi-Granularity Contextual Semantic (MGCS) modeling framework, utilizing a Path Modeling Network (PMN) and a Subgraph Modeling Network (SMN) to extract two granularity levels of contextual semantics from single paths and complete subgraphs, for fully inductive KGC. The PMN extracts paths between head and tail entities and employs reasoning patterns from similar cases to filter out unreliable paths. Then two innovative path conversion strategies are designed to significantly enhance the pre-trained language model’s understanding of specific path contexts. The SMN employs a neighbor interactive graph neural network to extract high-order semantics from the complete subgraph context with a concept-enhanced relation encoding, and optimizes it through a contrastive learning method. Finally, the confidence of the triples is evaluated from the perspective of global complete context by comparing the semantics between the subgraphs surrounding the target triplet and the subgraphs surrounding similar cases. Experimental results on benchmark datasets demonstrate the effectiveness of MGCS.

Evolving Representations of Childhood: A Diachronic Analysis of the Lexeme Child in British Newspapers from the 18th To 21st Centuries

This article investigates the semantic evolution of the lexeme CHILD in British newspaper discourse from the 18th to the 21st centuries, employing a diachronic approach and cognitive semantics framework. The study utilizes lexical analysis and frame modelling methodologies to trace changes in the lexical meaning of the lexeme CHILD during the mentioned period, drawing on dictionaries and newspaper archives. Findings reveal a dynamic semantic structure characterized by the emergence of new meanings and the persistence of core components like 'person' and 'juvenile'. The analysis identifies shifts influenced by societal, cultural, and ideological factors, reflecting changes in societal perceptions and linguistic representations over time. This research contributes to understanding how a language mirrors and shapes societal attitudes towards childhood across centuries, emphasizing the role of newspaper discourse in framing cultural norms and perceptions.

Efficient and accurate semi-supervised semantic segmentation for industrial surface defects

Deep learning-based defect detection methods have gained widespread application in industrial quality inspection. However, limitations such as insufficient sample sizes, low data utilization, and issues with accuracy and speed persist. This paper proposes a semi-supervised semantic segmentation framework that addresses these challenges through perturbation invariance at both the image and feature space. The framework employs diverse perturbation cross-pseudo-supervision to reduce dependency on extensive labeled datasets. Our lightweight method incorporates edge pixel-level semantic information and shallow feature fusion to enhance real-time performance and improve the accuracy of defect edge detection and small target segmentation in industrial inspection. Experimental results demonstrate that the proposed method outperforms the current state-of-the-art (SOTA) semi-supervised semantic segmentation methods across various industrial scenarios. Specifically, our method achieves a mean Intersection over Union (mIoU) 3.11% higher than the SOTA method on our dataset and 4.39% higher on the public KolektorSDD dataset. Additionally, our semantic segmentation network matches the speed of the fastest network, U-net, while achieving a mIoU 2.99% higher than DeepLabv3Plus.

Prawitz's completeness conjecture: A reassessment

AbstractIn 1973, Dag Prawitz conjectured that the calculus of intuitionistic logic is complete with respect to his notion of validity of arguments. On the background of the recent disproof of this conjecture by Piecha, de Campos Sanz and Schroeder‐Heister, we discuss possible strategies of saving Prawitz's intentions. We argue that Prawitz's original semantics, which is based on the principal frame of all atomic systems, should be replaced with a general semantics, which also takes into account restricted frames of atomic systems. We discard the option of not considering extensions of atomic systems, but acknowledge the need to incorporate definitional atomic bases in the semantic framework. It turns out that ideas and results by Westerståhl on the Carnap categoricity of intuitionistic logic can be applied to Prawitz semantics. This implies that Prawitz semantics has a status of its own as a genuine, though incomplete, semantics of intuitionstic logic. An interesting side result is the fact that every formula satisfiable in general semantics is satisfiable in an axioms‐only frame (a frame whose atomic systems do not contain proper rules). We draw a parallel between this seemingly paradoxical result and Skolem's paradox in first‐order model theory.

Construction of supply chain management information system based on networked web service composition technology

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods.

Category-sensitive semi-supervised semantic segmentation framework for land-use/land-cover mapping with optical remote sensing images

High-quality land-use/land-cover mapping with optical remote sensing images yet presents significant work. Even though fully convolutional semantic segmentation models have recently contributed to popular solutions, the lack of annotation data may lead to severe degradations in their inference performance. Besides, the category confusion in high-resolution representations will further exacerbate the adverse effects. In this paper, we propose a category-sensitive semi-supervised semantic segmentation framework to address these weaknesses by employing massive unlabeled data. With the perturbations from adopted hybrid data augmentation structures, we first focus on the output space and execute regularization constraints to learn category-specific discriminative features. It is formulated with a consistency self-training procedure where a dynamic class-balanced threshold selection scheme is proposed to provide high-confident pseudo supervisions for each category. In addition, we introduce pixel-wise contrastive learning on the common embedding space from both labeled and unlabeled data domains to further facilitate the semantic dependencies among category features, in which the reliable labels are leveraged as guidance for pixel sample selection. We verify the proposed framework on two benchmark land-use/land-cover datasets, and the experimental results demonstrate its competitive performance to other state-of-the-art semi-supervised methods.

Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation

With the proliferation of large-scale 3D point cloud datasets, the high cost of per-point annotation has spurred the development of weakly supervised semantic segmentation methods. Current popular research mainly focuses on single-scale classification, which fails to address the significant feature scale differences between background and objects in large scenes. Therefore, we propose MCCR (Multi-scale Classification and Contrastive Regularization), an end-to-end semantic segmentation framework for large-scale 3D scenes under weak supervision. MCCR first aggregates features and applies random downsampling to the input data. Then, it captures the local features of a random point based on multi-layer features and the input coordinates. These features are then fed into the network to obtain the initial and final prediction results, and MCCR iteratively trains the model using strategies such as contrastive learning. Notably, MCCR combines multi-scale classification with contrastive regularization to fully exploit multi-scale features and weakly labeled information. We investigate both point-level and local contrastive regularization to leverage point cloud augmentor and local semantic information and introduce a Decoupling Layer to guide the loss optimization in different spaces. Results on three popular large-scale datasets, S3DIS, SemanticKITTI and SensatUrban, demonstrate that our model achieves state-of-the-art (SOTA) performance on large-scale outdoor datasets with only 0.1% labeled points for supervision, while maintaining strong performance on indoor datasets.

Decoupling foreground and background with Siamese ViT networks for weakly-supervised semantic segmentation

Due to the coarse granularity of information extraction in image-level annotation-based weakly supervised semantic segmentation algorithms, there exists a significant gap between the generated pseudo-labels and the real pixel-level labels. In this paper, we propose the DeFB-SV framework, which consists of a dual-branch Siamese network structure. This framework separates the foreground and background of images by generating unified resolution and mixed resolution class activation maps, which are then fused to obtain pseudo-labels. The mixed-resolution class activation maps are produced by a new mixed-resolution patch partition method, where we introduce a semantically heuristic patch scorer to divide the image into patches of different sizes based on semantics. Additionally, a novel multi-confidence region division mechanism is proposed to enable the adaptive extraction of the effective parts of pseudo-labels, further enhancing the accuracy of weakly supervised semantic segmentation algorithms. The proposed semantic segmentation framework, DeFB-SV, is evaluated on the PASCAL VOC 2012 and MS COCO 2014 datasets, demonstrating comparable segmentation performance with state-of-the-art methods.

Analysis of Dataset Limitations in Semantic Knowledge-Driven Multi-Variant Machine Translation

In this study, we explore the implications of dataset limitations in semantic knowledge-driven machine translation (MT) for intelligent virtual assistants (IVA). Our approach diverges from traditional single-best translation techniques, utilizing a multi-variant MT method that generates multiple valid translations per input sentence through a constrained beam search. This method extends beyond the typical constraints of specific verb ontologies, embedding within a broader semantic knowledge framework. We evaluate the performance of multi-variant MT models in translating training sets for Natural Language Understanding (NLU) models. These models are applied to semantically diverse datasets, including a detailed evaluation using the standard MultiATIS++ dataset. The results from this evaluation indicate that while multi-variant MT method is promising, its impact on improving intent classification (IC) accuracy is limited when applied to conventional datasets such as MultiATIS++. However, our findings underscore that the effectiveness of multi-variant translation is closely associated with the diversity and suitability of the datasets utilized. Finally, we provide an in-depth analysis focused on generating variant-aware NLU datasets. This analysis aims to offer guidance on enhancing NLU models through semantically rich and variant-sensitive datasets, maximizing the advantages of multi-variant MT.

A Cognitive Semantic Account of the Preposition “‘bra” in Modern Standard Arabic with Reference to the English “Through”

Prepositions are crucial grammatical devices for indicating location, movement, time, and other meanings. A case in point is the Arabic preposition “‘abra,” which expresses different spatial, temporal, and abstract meanings. Adopting a cognitive semantic perspective, this study investigates ‘abra, its diverse spatial meanings, and metaphorical extensions. It also uses cognitive constructs such as image schemas, prototypes, and conceptual metaphor to demonstrate how various senses of ‘abra can be described based on perceptual properties and cognitive characteristics. In addition, this study compares the meanings of ‘abra with those of its English equivalent (through). This research uses authentic examples from Modern Standard Arabic available through the Sketch Engine website and illustrations created by the researcher. The analytical description involves the basic schema, the prototypical meaning, and the metaphorical extensions. The findings reveal that ‘abra and “through” share a basic image schema and prototypical sense with slight differences resulting from varying conceptualizations. The metaphorical extensions are also similar. The distinctions between the two mostly pertain to the semantic scope in addition to the fact that ‘abra also covers the meaning of the preposition “across.” Generally, this analysis asserts that prepositional meanings are best described using a cognitive semantic framework. The results may be useful in the fields of lexicon, second learning and teaching, as well as translation.