Semantic System Research Articles

A knowledge graph-based data harmonization framework for secondary data reuse

Background and objectiveThe adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data. MethodsThe framework includes an ontology-based common data model (i.e. SCDM), a data transformation pipeline and a semantic query system. Heterogeneous datasets, mapped to different terminologies, are integrated by using an ontology-based infrastructure rooted in a top-level ontology. A graph database is generated by using these mappings, and web-based semantic query system facilitates data exploration. ResultsSeveral datasets from different European institutions have been integrated by using the framework in the context of the European H2020 Precise4Q project. Through the query system, data scientists were able to explore data and use it for building machine learning models. ConclusionsThe flexible data representation using RDF, together with the formal semantic underpinning provided by the SCDM, have enabled the semantic integration, query and advanced exploitation of heterogeneous data in the context of the Precise4Q project.

Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data—namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.

DO-SLAM: research and application of semantic SLAM system towards dynamic environments based on object detection

DO-SLAM: research and application of semantic SLAM system towards dynamic environments based on object detection

The PURPLE mystery: Semantic meaning of three purple terms in French speakers from Algeria, France, and Switzerland

AbstractStudies on the color category PURPLE yielded inconsistent category boundaries, focal colors, and color‐emotion associations. In French, there are at least three color terms referring to the shades of purple, potentially weighing on these inconsistencies. Thus, we tested the semantic breadth and richness in semantic meaning of violet (basic term), lilas (non‐basic), and pourpre (non‐basic). We collected free associations in 274 French speakers from Algeria, France, and Switzerland, yielding 2079 responses, of which 436 were discrete and 275 were unique. Frequency analyses and semantic coding supported the basicness status of violet in French, within a hierarchically structured semantic system. Moreover, the meaning of the three terms was not synonymous. Violet had the most abstract meaning. Lilas had the narrowest meaning, mainly referring to Natural Entities. Pourpre seemed close to RED. We found no differences between the countries. Future studies should extend this approach to other languages and other color terms.

10 Semantic Memory as a Predictor of Future Memory Decline

Objective:To determine if the degree of split between phonemic verbal fluency and semantic verbal fluency at initial visit is predictive of decline in memory performance between initial evaluation and follow-up.Participants and Methods:Data from a retrospective multidisciplinary memory clinic database at Spectrum Health was utilized. We examined data from 90 participants who had both an initial and follow-up evaluation completed (initial age = 77.1±4.7 years, follow-up age = 78.4±4.7 years, education = 13.9±3.1 years, race = 91% White, 7% Black, & 2% Hispanic, sex = 61% female, time between evaluations = 15.2±9.9 months). Patients who returned for follow-up did not meet criteria for dementia at time one. Split between phonemic and semantic fluency, termed the semantic-phonological delta (SPD) was measured at the initial evaluation by subtracting the Controlled Oral Word Association Test (COWAT; FAS) T-score from the Animal Naming Test (ANT) T-score. Change in memory score was defined in two ways: 1) subtracting the follow-up evaluation Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) List Recognition Score (RBANS percentiles were converted to a scaled score) from the initial evaluation List Recognition Score (List Recognition Delta), and 2) computing the difference in the RBANS Delayed Memory Index Standard Score between the initial evaluation and the follow-up evaluation (RBANS Memory Delta).Results:Average semantic fluency T scores were (M = 40.3, SD = 12.3) and phonemic fluency T scores were (M = 42.7, SD = 10.3) at initial evaluation. Bivariate correlations were used to determine the relationship between the clinical variables. SPD was significantly correlated with List Recognition Delta, r(88) = .23, p = .026, with greater discrepancies in verbal fluency scores associated with higher level of decline in List Recognition at follow-up. By comparison, Semantic Fluency performance itself at initial evaluation was not significantly correlated with List Recognition Delta, r(88) = .17, p = .097. The correlation between SPD and the RBANS Memory Delta was also not significant, r(88) = .14, p = .166. At follow-up evaluation, 39% of the sample received a diagnosis of Alzheimer’s disease. Of those diagnosed with Alzheimer’s disease, 66% had a negative SPD split at time one, performing worse on semantic fluency compared to phonemic fluency.Conclusions:SPD is a better predictor of decline in RBANS List Recognition performance between evaluations than semantic fluency alone, with a larger negative SPD score (worse semantic fluency performance compared to phonemic fluency) at initial evaluation predicting decline in List Recognition performance at follow-up evaluation. SPD at initial evaluation was not significantly correlated with change in RBANS Delayed Memory Index score between evaluations. This may be because there are some patients who are similarly impaired in both semantic and phonemic verbal fluency at initial evaluation who later demonstrate progressive decline in memory retrieval due to hippocampal-sparing etiologies (e.g., vascular dementia). Overall, these findings are consistent with previous work suggesting that declines in the semantic memory system precede declines in episodic memory retention in conditions such as Alzheimer’s disease.

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

Erori de producere a cuvintelor în context afazic

This article is part of the paper „Patterns and errors of word production in aphasic contexts”. To begin with, generalities about aphasia and a cognitive neuropsychology model of spoken word production are presented, which addresses four stages involved in word retrieval: semantic system, phonological output lexicon, phonological set, and articulatory programming, and finally speech. Also, as a theoretical aspect, the three types of word utterance errors are presented: the imaginability, frequency, and length effects. The theoretical aspects are then exemplified in a case study. The aim of the research was to identify from a neurocognitive point of view in which domain the disorder is located, and which pattern of word production errors can be identified, in order to intervene specifically in language recovery, proposing an intervention plan. Among the conclusions, it can be mentioned that although aphasic symptomatology is well known, the approach from the proposed perspective created specific opportunities for recovery.

Online Multi-Contact Motion Replanning for Humanoid Robots with Semantic 3D Voxel Mapping: ExOctomap.

This study introduces a rapid motion-replanning technique driven by a semantic 3D voxel mapping system, essential for humanoid robots to autonomously navigate unknown territories through online environmental sensing. Addressing the challenges posed by the conventional approach based on polygon mesh or primitive extraction for mapping, we adopt semantic voxel mapping, utilizing our innovative Extended-Octomap (ExOctomap). This structure archives environmental normal vectors, outcomes of Euclidean Cluster Extraction, and principal component analysis within an Octree structure, facilitating an O(log N) efficiency in semantic accessibility from a position query x∈R3. This strategy reduces the 6D contact pose search to simple 3D grid sampling. Moreover, voxel representation enables the search of collision-free trajectories online. Through experimental validation based on simulations and real robotic experiments, we demonstrate that our framework can efficiently adapt multi-contact motions across diverse environments, achieving near real-time planning speeds that range from 13.8 ms to 115.7 ms per contact.

Spatiotemporal characteristics of the neural representation of event concepts

Events are a fundamentally important part of our understanding of the world. How lexical concepts denoting events are represented in the brain remains controversial. We conducted two experiments using event and object nouns matched on a range of psycholinguistic variables, including concreteness, to examine spatial and temporal characteristics of event concepts. Both experiments used magnitude and valence tasks on event and object nouns. The fMRI experiment revealed a distributed set of regions for events, including the angular gyrus, anterior temporal lobe, and posterior cingulate across tasks. In the EEG experiment, events and objects differed in amplitude within the 300–500 ms window. Together these results shed light into the spatiotemporal characteristics of event concept representation and show that event concepts are represented in the putative hubs of the semantic system. While these hubs are typically associated with object semantics, they also represent events, and have a likely role in temporal integration.

Generation of MRS Abstract Predicates from Paninian USR

Semantic Representations become useful resources for various multilingual NLP applications such as Machine Translation, Multilingual Generation, cross Lingual QA, to name a few. No Semantic Representation, to our knowledge, adopts vivakṣā (Speaker’s intention) as a guiding principle for the representation. This motivates us to develop a new Semantic Representation system – Universal Semantic Representation (USR) – following Indian Grammatical Tradition (IGT) and Paninian grammar. Since USR is designed to be language-independent, we have currently taken up the task of generating English, Hindi, Tamil and Bangla from the USR. For English generation, the USR is mapped to ERG meaning representation (Flickinger, D. 1999) which is couched in Minimal Recursion Semantics (MRS). We use an off-the-shelf ACE generator that uses ERG as a resource-grammar for generating English. While designing the transfer module from USR to ERG-based MRS, we came across various Abstract Predicates (APs) in MRS representation as described in ErgSemantics_Basic (Flickinger et al., 2014). These APs are used to represent the semantic contribution of grammatical constructions or more specialized lexical entries such as compounding or the comparative use of more and so on. This paper presents the strategy for postulating the APs from the information given in USR and then reports the implementation of the transfer module keeping the focus on the postulation of APs. We get around 95% accuracy in postulating APs from USR.

Lexico-Semantic Group of Verbs of Interpersonal Relations in Russian and Chinese: based on the Translation of F.M. Dostoevsky’s Novel “Crime and Punishment”

This study is devoted to the analysis of lexico-semantic group of verbs, which express attitude to someone in Russian and the ways of their translation into Chinese. A group of emotional and evaluative verbs included in the lexico-semantic field of interpersonal relations is analyzed. The choice of the study material is determined by the fact that this group of verbs is one of the most frequent in the use and widely represented in the novel “Crime and punishment” by F.M. Dostoyevsky, occurring 561 times. The significance of this research lies in the absence of a special systematic study of this lexico-semantic group on the material of literature in Russian and Chinese languages, as well as in the need to develop a comprehensive research methodology, methods of comparative and contextual analyses. The study reveals the semantic features of verbs in the Russian and Chinese languages. It is established that the lexico-semantic group under study consists of verbs that are perceived as categorical-lexical semes “relation” and can have both positive and negative semantic meaning. The semes ‘positive attitude’, ‘love’, ‘faith’, ‘respect’, ‘compassion’, ‘pity’ and ‘negative attitude’, ‘suffering’, ‘doubt’, ‘fear’ are subjected to study. These features determine the structure of the group in question in the lexical and semantic system of the Russian and Chinese languages, are expanding the understanding of the content and structure of the group of verbs. The result of the study is that the analysis of interlingual gaps reveals the presence of incomplete lexical correspondence to a foreign language word. The analyzed linguistic material made it possible to identify similarities and differences in the semantics of verbs when translating the text of the novel into Chinese.

Translation as Metamorphosis in the Animated Poetry-Film

ABSTRACT Adapting or translating poetry to a new medium destabilises the significance of each of the elements of a poem’s make-up. Hanna Andrews states that in translating poetry, the “idea or emotion itself may be translated, reframed or refashioned in a new medium, [but] the adapted version cannot replicate it precisely, because in poetry, it is created through the act of expression” (Andrews [2013]. “Recitation, Quotation, Interpretation: Adapting the Ouevre in Poet Biopics”. Adaptation 6 (3): 365–383. doi:10.1093/adaptation/apt019). The conundrum of translating poetry from one language into another is problematic because of the inherent differences between languages and the peculiar nature of language in poetry. The translation of poetry from a linguistic to a visual semantic system presents a vast number of challenges to the translator if conventional approaches to translation should be attempted. Kentaro Kawashima’s (“Metamorphosis as Origin—Koji Yamamura’s Short Animation Franz Kafka’s a Country Doctor”. Arts 8 (2): 54. doi:10.3390/arts8020054, 5) view of the creation of a film based on a literary text as an instance of metamorphosis rather than an act of translation offers a solution to the question of transmedial translation. Using praxis as research this paper will explore what such a translation entails by analysing the poem and its transmutation in the poem Amon L’isa by Carina van der Walt and the poetry-film Amon L’isa by Diek Grobler.

Economics of Semantic Communication System: An Auction Approach

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.

성장소설 표지디자인에 나타난 시각적 의미패턴 연구

As Korea grew after liberation, social and cultural changes occurred and were reflected in the content and cover design of coming-of-age novels. In a situation where Korea's bildungsroman is also changing and developing, we found that research on cover design for this was very insufficient, and attempted to study the characteristic semantic system of the cover design of Korean bildungsroman by era. The research method targets the cover designs of 166 coming-of-age novels published from the 1970s to 2023. First, the meaning of keywords and titles by era of the content of the coming-of-age novels is analyzed, and second, the meaning of the cover design of the coming-of-age novels is visualized. Analyze it by dividing it into images, colors, and typography, and check the flow and patterns of change by era. Third, we analyzed the relationship between keywords and visual elements by era in the content of coming-of-age novels. Looking at the analysis results, firstly, the composition of the parts of speech in the title by era appeared to be diversifying and becoming more complex, from 1 type of ‘noun’ structure in the 1970s to 17 types of parts of speech in the 2020s. Second, in terms of images, the oil painting technique of illustrations was used the most, and as the 2000s progressed, the frequency of using the entire cover as an image increased, and the use of stripes also gradually expanded. In terms of color, in the 1970s the hue distribution was medium brightness and low saturation, in the 1990s it was a hue distribution of wide brightness and saturation with warm colors and neutral colors, and in the 2020s the hue distribution was again centered on neutral colors and cool colors with medium brightness and low saturation. appear. In terms of typography, horizontal writing, center alignment, single line, serif, asymmetrical layout, and color changes were used to emphasize headings. Third, the concreteness and abstractness of titles and images were analyzed. In the 1970s, abstract expressions were very high, and concrete expressions were used more in the 2000s. In terms of visualization of the title, the average matching rate was 63%, and it was analyzed that the specificity of the title affects image visualization. In general, keyword visualization was relatively high in the 1970s, but the matching rate gradually decreased thereafter.

MC-YOLO-Based Lightweight Detection Method for Nighttime Vehicle Images in a Semantic Web-Based Video Surveillance System

Semantic web-based video surveillance systems can provide strong decision-making support for managers, and they have high requirements for real-time and precision of vehicle detection models in complex night scenes. To address this issue, a lightweight nighttime vehicle detection method (MC-YOLO) integrating MobileNetV2 and YOLOV3 is proposed. Firstly, in the preprocessing stage, image enhancement is performed on nighttime images to facilitate model feature extraction. Then, the lightweight network MobileNetV2 is used to extract feature by replacing the backbone network DarkNet53 in YOLOv3, thus accelerating the speed of target detection. Finally, after the convolution operation of the backbone network, a convolution block attention module is added to enhance the important feature information and suppress the secondary features, thereby improving the detection precision. The experimental results on the BDD100K dataset show that the proposed MC-YOLO model has a precision of up to 92.75%, which is superior to several other advanced comparative models.

An ontology-based framework for semantic geographic information systems development and understanding

Geographic information systems (GIS) are a widely used approach for geodata manipulation, analysis, and geoinformation visualization. Although the issue of semantic heterogeneity for GIS applications has been widely addressed, the development of flexible semantic GIS systems can still be improved, and GIS interfaces can be further unified to enhance system understanding among users of various GIS systems. This study proposes an ontology-based semantic GIS conceptual framework that can seamlessly combine ontology models, GIS elements and user actions for semantic GIS application development. In addition, a GIS components ontology that encompasses the five fundamental components of GIS is proposed to integrate GIS elements that can perceive GIS operations and processes semantically and to support customized GIS system designs. Moreover, a semantic WebGIS implementation framework is designed on the basis of the proposed conceptual framework to facilitate semantic WebGIS development. The implementation uses two common application scenarios regarding urban change analysis and user interaction. The former performs semantic retrieval for urban change analysis with system understanding, whereas the latter conducts online thematic mapping with semantic interpretation. The study contributes in terms of innovation in the design of a unified GIS components ontology and successful implementation of a semantic GIS system using the ontology-driven approach, thereby providing a convincing demonstration of the latter and showing the potential for semantic GIS application paradigms.

MVS‐SLAM: Enhanced multiview geometry for improved semantic RGBD SLAM in dynamic environment

AbstractSimultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent mobile robots to operate successfully in unknown environments. While many excellent SLAM systems have been developed in recent years, most assume that the environment is static, resulting in poor performance in dynamic environments. To address this limitation, we propose multiview stereo (MVS)‐SLAM (MVS‐SLAM), a real‐time semantic RGBD SLAM system with improved‐multiview geometry, built on the RGBD mode of ORB‐SLAM3. MVS‐SLAM tightly integrates semantic and geometric information to tackle the challenges posed by dynamic scenes. To meet the real‐time requirements, the semantic module leverages the latest and fastest object detection network, YOLOv7, to provide semantic prior knowledge for the geometric module. We introduce a novel geometric constraint method that capitalizes on depth images and semantic information to recover three‐dimensional (3D) feature points and initial camera pose. We use a 3D coordinate error threshold to identify dynamic points and remove them using the K‐means clustering algorithm. This approach effectively reduces the impact of dynamic points. We validate MVS‐SLAM using challenging dynamic sequences from the TUM data set, demonstrating that it significantly improves localization accuracy and system robustness in all types of dynamic environments.

Concepts require flexible grounding

Research on semantic memory has a problem. On the one hand, a robust body of evidence implicates sensorimotor regions in conceptual processing. On the other hand, a different body of evidence implicates a modality independent semantic system. The standard solution to this tension is to posit a hub-and-spoke system with modality independent hubs and modality specific spokes. In this paper, I argue in support of an alternative view of grounding which remains committed to neural reenactment but emphasizes the multimodal and multilevel nature of the semantic system. This view is built upon the recognition that abstraction is a design feature of concepts. Semantic memory employs hierarchically structured representations to capture different degrees of abstraction. Grounding does not work the way that many embodied approaches have assumed.

Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.

Although episodic memory and visual processing decline substantially with healthy aging, semantic knowledge is generally spared. There is evidence that older adults' spared semantic knowledge can support episodic memory. Here, we used functional magnetic resonance imaging (fMRI) combined with representational similarity analyses (RSAs) to examine how novel visual and preexisting semantic representations at encoding predict subjective memory vividness at retrieval. Eighteen young and seventeen older adults (female and male participants) encoded images of objects during fMRI scanning and recalled these images while rating the vividness of their memories. After scanning, participants discriminated between studied images and similar lures. RSA based on a deep convolutional neural network and normative concept feature data were used to link patterns of neural activity during encoding to visual and semantic representations. Relative to young adults, the specificity of activation patterns for visual features was reduced in older adults, consistent with dedifferentiation. However, the specificity of activation patterns for semantic features was enhanced in older adults, consistent with hyperdifferentiation. Despite dedifferentiation, visual representations in early visual cortex (EVC) predicted high memory vividness in both age groups. In contrast, semantic representations in lingual gyrus (LG) and fusiform gyrus (FG) were associated with high memory vividness only in the older adults. Intriguingly, data suggests that older adults with lower specificity of visual representations in combination with higher specificity of semantic representations tended to rate their memories as more vivid. Our findings suggest that memory vividness in aging relies more on semantic representations over anterior regions, potentially compensating for age-related dedifferentiation of visual information in posterior regions.SIGNIFICANCE STATEMENT Normal aging is associated with impaired memory for events while semantic knowledge might even improve. We investigated the effects of aging on the specificity of visual and semantic information in the brain when viewing common objects and how this information enables subsequent memory vividness for these objects. Using functional magnetic resonance imaging (fMRI) combined with modeling of the stimuli we found that visual information was represented with less specificity in older than young adults while still supporting memory vividness. In contrast semantic information supported memory vividness only in older adults and especially in those individuals that had the lowest specificity of visual information. These findings provide evidence for a spared semantic memory system increasingly recruited to compensate for degraded visual representations in older age.

Machine Learning Model for Joint Semantic and Channel Coding/Decoding in Wireless Systems

With a significant increase in the data and demand for large bandwidth, the wireless research community has identified the need to pay attention to transmitting the context rather than focusing on only the manner of transmission. Inspired by this, in the current article a semantic wireless system enabled by deep learning is proposed which aims to maximize the system capacity. As opposed to evaluating only the bit/symbol errors, the proposed technique is able to recover the meaning of sentences and is hence able to minimize the semantic errors. Furthermore, transfer learning is implemented to accelerate the process of re-training. Extensive simulations to validate the performance of the proposed technique demonstrate that it is able to (i) maintain enhanced robustness to channel fluctuations and (ii) achieve higher performance. Overall, the current study makes it evident that the proposed technique is a good candidate for implementation in semantic wireless systems.