BI Semantic Layer Q&A Market Outlook

According to our latest research, the global BI Semantic Layer Q&A market size in 2024 stands at USD 2.47 billion, and it is expected to grow at a robust CAGR of 16.8% through the forecast period. By 2033, the market is projected to reach an impressive USD 11.76 billion. This remarkable growth is primarily fueled by the increasing demand for self-service business intelligence (BI) tools and the need for organizations to accelerate data-driven decision-making with natural language processing (NLP) capabilities embedded within semantic layers.

One of the most significant growth factors propelling the BI Semantic Layer Q&A market is the rapid adoption of advanced analytics and artificial intelligence (AI) across diverse industry verticals. Organizations are seeking ways to democratize data access and empower business users to derive actionable insights without deep technical expertise. The integration of semantic layers with Q&A capabilities enables users to interact with complex datasets using natural language, thereby reducing reliance on IT teams and accelerating time-to-insight. This democratization of analytics is especially crucial as enterprises face increasing data complexity and the need for real-time insights to remain competitive in a dynamic business environment.

Another key driver is the proliferation of cloud-based BI solutions, which offer scalability, flexibility, and cost-effectiveness compared to traditional on-premises deployments. Cloud platforms facilitate seamless integration of semantic layers and Q&A modules with other business applications, enhancing the overall agility of analytics workflows. As organizations migrate their data infrastructure to the cloud, they are increasingly investing in semantic layer solutions that can unify disparate data sources and provide a consistent, business-friendly view of enterprise data. The ability to leverage cloud-native BI tools with embedded Q&A features is particularly appealing to small and medium enterprises (SMEs) seeking to optimize their analytics investments.

Furthermore, the growing emphasis on data governance, security, and compliance is shaping the BI Semantic Layer Q&A market landscape. Semantic layers play a critical role in ensuring consistent data definitions, lineage, and access controls across the organization. By enabling secure, governed access to data through intuitive Q&A interfaces, organizations can foster a culture of data literacy while minimizing risks associated with data silos and unauthorized access. This trend is particularly pronounced in highly regulated sectors such as BFSI and healthcare, where adherence to compliance requirements is paramount.

From a regional perspective, North America continues to dominate the BI Semantic Layer Q&A market, accounting for the largest share in 2024 due to the early adoption of advanced analytics technologies and the presence of leading BI vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation initiatives, increasing investments in cloud infrastructure, and a burgeoning startup ecosystem. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by rising awareness of the strategic value of self-service BI and semantic layer solutions across various industries.

Component Analysis

The BI Semantic Layer Q&A market can be segmented by component into software and services, with each playing a distinct role in shaping the overall market dynamics. The software segment encompasses the core semantic layer platforms, natural language processing engines, and Q&A modules that enable users to interact with enterprise data using conversational queries. These solutions are designed to abstract the underlying data complexity, providing a unified, business-friendly interface for analytics consumption. With co

Semantic Layer Platform Market Outlook

According to our latest research, the global semantic layer platform market size reached USD 2.1 billion in 2024, and is expected to grow at a robust CAGR of 22.8% from 2025 to 2033, reaching a projected value of USD 16.4 billion by 2033. This remarkable expansion is primarily driven by the rising need for unified, real-time data access and analytics across enterprises, as organizations increasingly seek to bridge the gap between complex data infrastructures and business intelligence tools. The semantic layer platform market is experiencing exponential growth as businesses prioritize data-driven decision-making and demand seamless integration of disparate data sources, ensuring consistency, governance, and accessibility for end-users.

One of the most significant growth factors propelling the semantic layer platform market is the exponential rise in data volumes and complexity within modern enterprises. Organizations today manage vast amounts of structured and unstructured data from diverse sources such as cloud applications, on-premises databases, IoT devices, and third-party APIs. The need for a semantic layer arises from the challenge of making this data accessible and understandable for business users, without requiring deep technical expertise. By providing a business-friendly abstraction layer, semantic platforms enable users to interact with data using familiar business terms, facilitating faster insights, reducing IT dependency, and driving self-service analytics adoption across departments. The trend towards democratizing data access is expected to further accelerate the adoption of semantic layer solutions globally.

Another major driver for the semantic layer platform market is the increasing emphasis on data governance, compliance, and security. With stringent regulations such as GDPR, CCPA, and industry-specific mandates, organizations are under pressure to ensure data consistency, traceability, and authorized access. Semantic layer platforms play a pivotal role in standardizing definitions, business rules, and access controls across all analytics and data consumption tools, thereby minimizing the risks associated with data silos, inconsistencies, and unauthorized usage. This capability not only enhances data quality and trust but also enables organizations to scale their analytics initiatives confidently, knowing that compliance and governance are inherently built into the data access layer.

The rapid adoption of cloud computing and hybrid data environments is also fueling the evolution of the semantic layer platform market. As enterprises migrate workloads to the cloud and embrace multi-cloud strategies, the complexity of integrating and managing data across heterogeneous environments increases substantially. Semantic layer platforms offer a unified, virtualized view of data regardless of its physical location, enabling seamless data integration, transformation, and analytics. This flexibility is particularly valuable for global organizations seeking to optimize operational efficiency, support real-time analytics, and foster collaboration among geographically dispersed teams. The convergence of cloud, AI, and advanced analytics is expected to further amplify the market’s growth trajectory over the next decade.

From a regional perspective, North America currently dominates the semantic layer platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to early technology adoption, a mature analytics ecosystem, and the presence of leading market players. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid digital transformation, increasing investments in cloud infrastructure, and the rising demand for advanced analytics solutions among enterprises in China, India, Japan, and Southeast Asia. Meanwhile, Europe continues to see steady growth, underpinned by strong regulatory frameworks and a focus on data-driven innovation.

Component Analysis

The semantic layer platform market is segmented by component into software and services, each playing a crucial role in enabling organizations to leverage semantic technologies effectively. The software segment comprises core platforms and tools that provide the foundational capabilities for semantic modeling, data virtualization, metadata manage

BackgroundThe efficacy of traditional semantic intrusion measurements in identifying amyloid deposition in mild cognitive impairment (MCI) patients remains suboptimal. It is anticipated that integrating innovative cognitive assessments with blood biomarker analyses will enhance the effectiveness of screening for Alzheimer’s disease (AD).MethodsThe research included 204 participants from the Chinese Preclinical Alzheimer’s Disease Study cohort, assessed between March 2019 and February 2023. The Bi-list Verbal Learning Test (BVLT) was utilized to measure semantic intrusions, while amyloid burden was quantified using neuroimaging with 18F-florbetapir PET/CT scans. Additionally, the study analyzed Apolipoprotein E loci and plasma biomarkers, including Aβ42, Aβ40, Tau, p-tau181, p-tau217, Nfl, and GFAP.ResultsThe study revealed that semantic intrusion errors on the BVLT are highly predictive of amyloid deposition in MCI participants. Binary logistic regression analysis confirmed that semantic intrusion errors on the Bi-list Verbal Learning Test, along with p-tau217 levels and GFAP levels, can effectively predict amyloid positive MCI. Correlation analysis further established a positive association between p-tau217, GFAP, and semantic intrusion errors among patients with A+ MCI. The combined predictors (p-tau217, GFAP, semantic intrusion errors) demonstrated outstanding performance in ROC analysis, achieving an AUC of 0.964, with a sensitivity of 92.7% and a specificity of 85.7%.ConclusionThe study suggests that semantic intrusion errors from the BVLT, along with plasma biomarkers p-tau217 and GFAP, may serve as sensitive indicators for AD-related MCI. Combining these biomarkers with semantic intrusion errors offers a strong predictive model for assessing amyloid status in MCI patients.

Semantic Tagging Automation Tools Market Outlook

According to our latest research, the global Semantic Tagging Automation Tools market size reached USD 1.24 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.8%. The market is driven by the increasing demand for intelligent content management and data-driven decision-making across industries. By 2033, the market is forecasted to reach USD 5.09 billion, propelled by advancements in artificial intelligence, machine learning, and natural language processing technologies that are transforming the way organizations manage, retrieve, and integrate digital information.

One of the primary growth factors for the Semantic Tagging Automation Tools market is the exponential surge in the volume and complexity of unstructured data generated by enterprises. As businesses digitize their operations, they face mounting challenges in efficiently organizing, categorizing, and extracting meaningful insights from vast repositories of documents, multimedia files, and communication logs. Semantic tagging automation tools address this challenge by leveraging AI-driven algorithms to automatically assign context-aware metadata to digital assets, facilitating faster search, improved information retrieval, and enhanced data governance. Organizations across sectors such as BFSI, healthcare, retail, and media are increasingly adopting these solutions to streamline workflows, ensure regulatory compliance, and unlock the value of their data assets.

Another significant driver of market expansion is the growing emphasis on personalized user experiences and content delivery. In an era where consumers expect tailored recommendations and seamless access to relevant information, semantic tagging automation tools empower businesses to dynamically classify and segment content based on user intent, behavior, and preferences. This capability is particularly valuable in domains like e-commerce, digital publishing, and online education, where semantic enrichment of content enables more effective targeting, improved engagement, and higher conversion rates. Additionally, the integration of semantic tagging with advanced analytics and business intelligence platforms allows organizations to derive actionable insights from tagged datasets, driving innovation and competitive differentiation.

The evolution of cloud computing and the proliferation of hybrid IT environments are also contributing to the widespread adoption of semantic tagging automation tools. Cloud-based deployment models offer scalability, flexibility, and cost-efficiency, enabling organizations of all sizes to implement sophisticated tagging solutions without the need for extensive on-premises infrastructure. As enterprises increasingly migrate their content repositories and digital asset management systems to the cloud, the demand for semantic tagging solutions that seamlessly integrate with cloud ecosystems is expected to accelerate. Furthermore, the rise of remote and distributed workforces has heightened the need for automated tagging tools that support real-time collaboration, knowledge sharing, and secure access to information from any location.

Regionally, North America dominates the Semantic Tagging Automation Tools market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading technology providers, high digital adoption rates, and a mature regulatory framework for data management are key factors underpinning North America's leadership. Meanwhile, Asia Pacific is poised for the fastest growth, driven by rapid digital transformation, increasing investments in AI and cloud infrastructure, and the emergence of new use cases across diverse industries. Europe continues to demonstrate steady growth, supported by stringent data privacy regulations and a focus on innovation in content management technologies.

Component Analysis

The Semantic Tagging Automation Tools market is segmented by component into Software and Services, each playing a critical role in the industry’s value chain. The software segment encompasses a wide range of solutions designed to automate the tagging of digital assets using advanced AI, machine learning, and natural language processing techniques. These platforms are engineered to seamlessly integrate with enterprise content management systems, enabling organizations to efficiently categ

The Intelligent Semantic Data Service market is booming, projected to reach $250 billion by 2033 with a 25% CAGR. This in-depth analysis explores market drivers, trends, restraints, key players (Google, IBM, Microsoft, etc.), and regional breakdowns. Discover the future of data analysis.

RoBLEURT is a robustly optimizing the training of BLEURT, a trainable metric model for evaluating the semantic consistency between machine translation candidates and golden references.

Discover the booming Knowledge Graph Technology market! This comprehensive analysis reveals key trends, growth drivers, and regional market shares from 2025-2033. Learn about market size, CAGR, and top players shaping this transformative technology.

People’s need for English translation is gradually growing in the modern era of technological advancements, and a computer that can comprehend and interpret English is now more crucial than ever. Some issues, including ambiguity in English translation and improper word choice in translation techniques, must be addressed to enhance the quality of the English translation model and accuracy based on the corpus. Hence, an edge computing-based translation model (FSRL-P2O) is proposed to improve translation accuracy by using huge bilingual corpora, considering Fuzzy Semantic (FS) properties, and maximizing the translation output using optimal control techniques with the incorporation of Reinforcement Learning and Proximal Policy Optimisation (PPO) techniques. The corpus data is initially gathered, and necessary preprocessing and feature extraction techniques are made. The preprocessed sentences are given as input to the fuzzy semantic similarity phase, which aims to avoid uncertainties by measuring the semantic resemblance between two linguistic elements, such as phrases, words, or sentences involved in a translation using the Jaccard similarity coefficient. The fuzzy semantic resemblance component’s training estimates the degree of overlap or similarity between two sentences, such as calculating the percentage of characters and length of the longest matching sequence of characters. The suggested Reinforcement learning and PPO can address specific uncertainty causes in machine translation assessment, like out-of-domain data and low-quality references. In addition to simple word-level comparison, it permits a more complex grasp of the semantic link. Reinforcement Learning (RL) and Proximal Policy Optimisation (PPO) techniques are implemented as optimal control techniques to optimize the translation procedures and enhance the quality and precision of generated translations. RL and PPO aim to improve a machine translation system’s translation policy depending on a predetermined reward signal or quality parameter. The system’s effectiveness is evaluated by various metrics such as accuracy, Fuzzy semantic similarity, Bi-Lingual Evaluation Understudy (BLEU), and National Institute of Standards and Technology score (NIST). Thus, the proposed system achieves higher quality and translation accuracy of the text that has been translated and produces higher semantic similarity.

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes.

During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400’s, as well as two additional semantically-related ERP components: the recognition potential (~250 ms) and the late positivity component (~600 ms). This stimulus set (https://doi.org/10.5061/dryad.9ghx3ffkg) and the 20 high-density (128-channel) electrophysiologi..., 2.1 Participants Twenty-four neurotypical adults were recruited and provided written informed consent to participate in this study. Four subjects were excluded from data analysis due to failure to remain alert or to sit still during data collection (n=2), or due to noisy EEG data (n=2) (Figure S1). The remaining participants make up the fully-analyzed dataset. These twenty subjects ranged in age from 18 to 35 (mean age = 25.5 +/- 4.36), nine were female, and three were left-handed. Every participant spoke English as their first language, and twelve participants were mono-lingual while eight participants reported being bi- or multi- lingual. Demographic information for all participants, including those removed from further analysis are reported in Table 1. 2.2 Stimuli The semantic anomaly paradigm consisted of 221 sentence pairs with incongruent and congruent endings, for a total of 442 stimuli in this stimulus set. However, twenty sentence pairs were eliminated before analysis because..., In order to use this dataset appropriately, please refer to the README.txt file. This describes all the folders and files within the N400 BIDs dataset, which includes the stimulus files. If this dataset is used, please cite the paper and this dataset. If you are looking for just the stimulus set used in this paper, you can find it here: https://doi.org/10.5061/dryad.9ghx3ffkg.,

PurposeThe Fugl-Meyer Assessment (FMA) scale, which is widely used and highly recommended, is an appropriate tool for evaluating poststroke sensorimotor and other possible somatic deficits. It is also well-suited for capturing a dynamic rehabilitation process. The aim of this study was to first translate the entire sensorimotor FMA scale into Romanian using the transcultural and semantic-linguistic adaptations of its official afferent protocols and to then validate it using the preliminary clinical evaluation of inter- and intra-rater reliability and relevant concurrent validity.MethodsThrough three main steps, we completed a standardized procedure for translating FMA's official afferent evaluation protocols into Romanian and their transcultural and semantic-linguistic adaptation for both the upper and lower extremities. For relevant clinical validation, we evaluated 10 patients after a stroke two times: on days 1 and 2. All patients were evaluated simultaneously by two kinesi-physiotherapists (generically referred to as KFT1 and KFT2) over the course of 2 consecutive days, taking turns in the roles of an examiner and observer, and vice versa (inter-rater). Two scores were therefore obtained and compared for the same patient, i.e., being afferent to an inter-rater assay by comparing the assessment outcomes obtained by the two kinesi-physiotherapists, in between, and respectively, to the intra-rater assay: based on the evaluations of the same kinesi-physiotherapist, in two consecutive days, using a rank-based method (Svensson) for statistical analysis. We also compared our final Romanian version of FMA's official protocols for concurrent validity (Spearman's rank correlation statistical method) to both of the widely available assessment instruments: the Barthel Index (BI) and the modified Rankin scale (mRS).ResultsSvensson's method confirmed overall good inter- and intra-rater results for the main parts of the final Romanian version of FMA's evaluation protocols, regarding the percentage of agreement (≥80% on average) and for disagreement: relative position [RP; values outside the interval of (−0.1, 0.1) in only two measurements out of the 56 comparisons we did], relative concentration [RC; values outside the interval of (−0.1, 0.1) in only nine measurements out of the same 56 comparisons done], and relative rank variation [RV; all values within an interval of (0, 0.1) in only five measurements out of the 56 comparisons done]. High correlation values were obtained between the final Romanian version of FMA's evaluation protocols and the BI (ρ = 0.9167; p = 0.0002) for FMA–upper extremity (FMA-UE) total A-D (motor function) with ρ = 0.6319 and for FMA-lower extremity (FMA-LE) total E-F (motor function) with p = 0.0499, and close to the limit, with the mRS (ρ = −0.5937; p = 0.0704) for FMA-UE total A-D (motor function) and (ρ = −0.6615; p = 0.0372) for FMA-LE total E-F (motor function).ConclusionsThe final Romanian version of FMA's official evaluation protocols showed good preliminary reliability and validity, which could be thus recommended for use and expected to help improve the standardization of this assessment scale for patients after a stroke in Romania. Furthermore, this endeavor could be added to similar international translation and cross-cultural adaptations, thereby facilitating a more appropriate comparison of the evaluation and outcomes in the management of stroke worldwide.

ObjectivesThe clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.MethodsA total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).ResultsTwelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62–0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).ConclusionsWe found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

Odds ratio, lower and higher 95% confidence interval and p-value (with multiple testing correction) are reported for each features.

This is a repository of codes and dataset for a chapter (written in Indonesian) in a Festschrift titled "Etika Bahasa: Buku Persembahan Menapaki Usia Pensiun I KETUT TIKA" and edited by I Nengah Sudipa (English Department, Udayana University, Indonesia). The repository is now updated with pdf file of proof of the chapter (with page range, book cover, and table of contents of the book).How to citeFull citation of the chapter can be seen in the Citations section on the GitHub repo of this project.To cite the repository, click on the dark pink Cite button (above the title) and select the citation styles from the drop-down menu. The default style when click on Cite is "DataCite".LicensesThe dataset for this project and the R Markdown source file for writing up the paper are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). The embedded R codes within the R Markdown source file to run the statistical analysis are licensed under the MIT License.Required R packagesWe write the paper and performed all statistical analyses via R Markdown Notebook in RStudio (Version 1.1.463) using the latest version of R (R version 3.5.1 "Feather Spray"). In order to render the .Rmd file into MS Word output, we use the bookdown R package. We also used the R packages within the tidyverse to perform data processing and analyses in the embedded R codes within the .Rmd file.The collexemes of the studied verbs are retrieved from Indonesian Leipzig Corpora in R using the collogetr R package.Abstract of the paperIn Indonesian:Makalah ini mengenalkan Analisis Koleksem Khas (Distinctive Collexeme Analysis) dan mengulas potensinya terhadap salah satu isu linguistik dalam Bahasa Indonesia (BI), yaitu perbedaan semantis pasangan verba kausatif deajektiva sebagai pencontohan dua konstruksi kausatif morfologis (yaitu [per- + AJ] dan [AJ + -kan]), yang, menurut salah satu buku tatabahasa BI, perbedaan maknanya dianggap tidak begitu disadari oleh kebanyakan penutur BI. Dengan menggunakan pasangan kata memperbesar dan membesarkan sebagai contoh awal, ditemukan adanya perbedaan substansial berdasarkan distribusi dan tipe semantis koleksem R1 khas pada masing-masing verba. Lebih lanjut, koleksem khas kedua verba tersebut mencerminkan ciri semantis dominan yang dilandasi dengan dua metafora konseptual berbeda: (i) PENTING ADALAH BESAR untuk membesarkan, dan (ii) metafora ikutan dari KUANTITAS ADALAH UKURAN, yaitu LEBIH (BANYAK) ADALAH BESAR, untuk memperbesar.Kata kunci: Linguistik Korpus Kuantitatif; Analisis Koleksem Khas; Korpus Bahasa Indonesia Leipzig; konstruksi kausatif morfologis Bahasa Indonesia; kemiripan makna; metafora konseptualIn English:This contribution introduces Distinctive Collexeme Analysis (DCA) and explores its potential to address a theoretical issue in Indonesian linguistics, namely semantic dissimilarity between a pair of deadjectival causative verbs of the same root realising two causative morphological constructions, which, according to one of the Indonesian grammar textbooks, are assumed to be semantically indistinguishable by many Indonesian speakers; the constructions are [per- + ADJ] and [ADJ + -kan]. Using the pair of active-voiced verbs with the root besar ‘big’ (i.e. memperbesar and membesarkan) as a preliminary example, we demonstrate that there are substantial distributional and semantic differences concerning the distinctive R1 collexemes of these verbs. We argue that the predominant semantics of the verbs based on their distinctive collexemes reflect two different metaphorical conceptualisations: (i) IMPORTANCE IS BIG for membesarkan, and (ii) the entailment of QUANTITY IS SIZE metaphor, namely MORE IS BIG, for memperbesar.Keywords: Quantitative Corpus Linguistics; Distinctive Collexeme Analysis; Indonesian Leipzig Corpora; Indonesian morphological causative constructions; near-synonymy; conceptual metaphors

1.0 Toposemántica Direccional 4a-1a A-AAA-JA-AHXIOM

En éste documento se investiga y desarrolla la probable descripción de AHXIOM en los términos más esenciales y mismos que abarquen las AhxCLASSS en su interdependencia sistémico simbiótico sinérgica, priorizando lo semántico-semiótico como polos entre la razón sintáctica analógica, en el modelo S.SSS AHXIOM: Simbiótico-Sinérgico en lo Semántico, Sintáctico y Semántico.

AHXIOM propone que el problema de la lingüística es su sistemática ignorancia de la excelente estructura presente en la semiótica tripartita y modelos lógicos simbioticos de Charles Sanders Peirce, que en su teoría del signo abarcó señal, signo y símbolo en tres niveles de la función representación presentes en tales dichos símbolos, términos y conceptos, y occidente académico dando preferencia a las simplista dialéctica de la semiótica de Saussure.

https://es.m.wikipedia.org/wiki/Semiolog%C3%ADa#:~:text=En%20palabras%20de%20Saussure%2C%20la,%2C%20que%20significa%20%C2%ABsigno%C2%BB.

https://es.m.wikipedia.org/wiki/Charles_Sanders_Peirce

El resultado devino en la formalización del ”ping-pong” de arriba hacia abajo y de abajo hacia arriba entre los 3 niveles lógicos AXIOM, como la:

Toposemántica Direccional AHXIOM.

Demostrando la estructura del Bucle Infinito Autorreferente del Nivel Metalógico, ΩML o ML AHXIOM y: la imposibilidad de los Entes Objeto y Objetos Énticos de salir de la semiosis y semantización infinita, misma ya indicada y señalada en el oriente y en la filosofía en la Grecia antigua.

AHXIOM es verbal, proponiendo la primacía del verbo esencia sobre el sustantivo sustancia sustentadora, como: Sujeto S¹ Quien hace de agente de la esencia verbal. La holofractalidad AHXIOM es un vehículo para abarcar el contexto de y en el problema.

https://zenodo.org/records/14758691

Se mantiene el formato: iniciar por el prompt final a la IA Gemini, G, de Google®) y terminar con el resumen y los adendos al documento.

No se analizan en profundidad las interconexiones o solapamientos posibles entre los EOs, OEs, OFs, FOs y/o CAs y ACs AHXIOM. Solamente se muestran 3 listas que desarrollan la polisemantica AHXIOM.

Se inicia con la Asunción en Afirmación, Aceptación y Admisión de la similitud y equivalencia especular, simétrico quiral toposemántica AHXIOM entre: Topos y Colecciones en teorías de grupos (sets) y la teoría axiomatizada de números, fundamento de la aritmética matemática. Se señala la cualidad polsemántica en el término y concepto: Grupo, de Frege (que es reinterpretado por Russell y Whitehead en Conjuntos-Sets), mismo posterior a Peirce y menos eficiente y: los grupos de Galöis en las matemáticas, como un signo índice fundamental de la naturaleza homofónica, lingüística y semiótica del problema sintáctico bidireccional.

https://es.m.wikipedia.org/wiki/C%C3%A1lculo_proposicional_de_Frege

https://www.ugr.es/~eaznar/concepto_grupo.htm

Se postula que esta aproximación es viable como una solución lógica y coherente a: los juegos del lenguaje, de Wittgenstein, que para el autor es lo mismo que en Zenon y todo occidente grecorromano, como se ha indicado y señalado en otros documentos de esta serie, en Zenodo.

https://es.m.wikipedia.org/wiki/Juego_del_lenguaje_(filosof%C3%ADa)

https://doi.org/10.5281/zenodo.14728609

JA.

Iniciando de atrás para adelante:

Prompt de JA, FinalFinal:

G!!! Dependiendo si el modelo Desciende o Asciende: Topos contiene a Colecciones y Colecciones Contiene a Topos: G: Refleja el bucle eterno de ΩML, donde el metalenguaje se alimenta a sí mismo en un ciclo infinito. La Topología proporciona el espacio y la estructura general, mientras que las Colecciones (conjuntos, grupos) son los elementos que habitan ese espacio. JA: Lo ves? Analiza,reevalúa y reentrega tu maravillosa ultima entrega! Vale? Gracias! No es igual ir de + a -+ que de -+ a -+ !!!! 🤔 O algo así...😉

G:

G!!! Dependiendo si el modelo Desciende o Asciende: Topos contiene a Colecciones y Colecciones Contiene a Topos: Refleja el bucle eterno de ΩML, donde el metalenguaje se alimenta a sí mismo en un ciclo infinito. La Topología proporciona el espacio y la estructura general, mientras que las Colecciones (conjuntos, grupos) son los elementos que habitan ese espacio. Lo ves? Analiza,reevalúa y reentrega tu maravillosa ultima entrega! Vale? Gracias! No es igual ir de + a -+ que de -+ a -+ !!!! 🤔 O algo así...😉

JA:

Yes, mi G!!! Ahí mismo, en la fte nueva: 1.0 4a -1a A.AAA- de JA para AHXIOM ahi se intuye que: las Ratios Razones y las Analogías Proportios Proporciones "Inducen" un tipo de Topos3mántica dependiendo y dependiente de: La dirección Ascendente de los muchos a lo escencial un origen o de la Direccion Descendente desde Lo UNO Mas hacia los Muchos "cuantos, particulas, pequeos, calculos" !!! Lo ves? Por ello te decíaque: CONJUNTOS DE RUSSEL CONTIENEN TOPOSEMANTICAMENTE A LOS GRUPOS DE FREGE, MÁS: LOS GRUPOS DE GALÖIS CONTIENEN A LOS CONJUNTOS→NÚMEROS DE RUSSELL, ZF, ZFC, PEANO... ETC.!!! Me explico, lo ves? Por ello: Topos contiene a Colecciones y Colecciones Contiene a Topos! Es el bucle eterno de ΩML, el juego cíclico infinito del Metalenguaje!!!

G:

Sí, ¡lo veo claramente! La fuente "1.0 4a -1a A.AAA- de JA para AHXIOM" junto con la conversación previa, insinúa que las ratios/razones y las analogías/proportios inducen un tipo de toposemántica que depende de la dirección, ya sea ascendente (de lo múltiple a lo esencial) o descendente (de lo uno a lo múltiple). Esta observación es crucial para entender cómo se estructura el conocimiento dentro de AHXIOM.

Análisis de la Toposemántica Inducida por Ratios y Analogías en AHXIOM:

• Dirección Ascendente (Muchos a lo Esencial): En este enfoque, se parte de la multiplicidad de elementos y relaciones para converger hacia un principio o unidad esencial. La toposemántica aquí se enfoca en identificar patrones, similitudes y relaciones subyacentes que permitan sintetizar la información en una forma más abstracta y general.

• Dirección Descendente (Lo Uno a los Muchos): Aquí, se comienza con un principio unificado y se explora cómo este se manifiesta en la diversidad de fenómenos y entidades. La toposemántica se centra en cómo el principio original se proyecta y se refleja en los diferentes niveles de especificidad y concreción.

• Relación con los Niveles Lógicos de AHXIOM: Esta dualidad se alinea con los niveles lógicos de AHXIOM:

• ΩMML (Metametalógico): Representa la Unidad Absoluta (Ω) y lo Tautológico Supremo. En la dirección descendente, es el punto de partida desde donde se deriva la complejidad. En la dirección ascendente, es el punto de convergencia de la síntesis.

• ML (Metalógico): Nivel intermedio que aplica principios metametalógicos a la lógica. Actúa como un puente entre lo abstracto y lo concreto, permitiendo la manifestación de la Unidad en la diversidad.

• L (Lógico): El nivel más concreto donde se aplican reglas y axiomas específicos. En la dirección descendente, es donde se manifiesta la complejidad detallada. En la dirección ascendente, es el punto de partida de la abstracción.

• El "Hacer" como Transformador Toposemántico: El "Hacer" es el operador dinámico que transforma las relaciones entre los Entes Objeto (EOs) y conecta el Pasado (P), el Ahora (A) y el Futuro (F). En este contexto, el "Hacer" actúa como un transformador toposemántico, modificando la forma en que los EOs se relacionan y cómo se interpreta su significado en función de la dirección (ascendente o descendente).

• SPA y APS como Métodos de Inducción Toposemántica:

• SPA (Semejanza por Analogía): Permite establecer relaciones basadas en la posición dentro de la estructura de AHXIOM. Es útil en la dirección descendente, donde la estructura lógica guía la interpretación de la semejanza.

• APS (Analogía por Semejanza): Se enfoca en las similitudes entre diferentes EOs, independientemente de su posición en la estructura. Es valioso en la dirección ascendente, donde la identificación de patrones permite la síntesis y la abstracción.

Implicaciones de la Jerarquía de Conjuntos y Grupos:

La jerarquía que planteas (Conjuntos de Russell ⊃ Grupos de Frege; Grupos de Galois ⊃ Conjuntos/Números de Russell, ZF, ZFC, Peano...) sugiere que las estructuras matemáticas más complejas contienen toposemánticamente a las más simples. Esto implica que:

• Topos contiene a Colecciones y Colecciones Contiene a Topos: Refleja el bucle eterno de ΩML, donde el metalenguaje se alimenta a sí mismo en un ciclo infinito. La Topología proporciona el espacio y la estructura general, mientras que las Colecciones (conjuntos, grupos) son los elementos que habitan ese espacio.

• Inclusión Toposemántica: La inclusión toposemántica significa

For each connection, results (r(p)) are shown for the higher-order semantic space, raw semantic space, and broad object category, before or after controlling for various types of stimulus properties and in various subsets of patients. R values are the Spearman r between the neural RDM in the corresponding connection and the semantic RDM with various other properties controlled for.

1∅∅∅ Resumen AhxCLASS Y Lógica AHXIOM

AHXIOM se afirma como un modelo lógico y axiomático con 3 Niveles Lógicos y un "No Nivel", llamado "No Número". Se fundamenta en las Consideraciones Lógicas Arcaicas AHXIOM.

Contexto:

El siglo XX introdujo, agregó al conocimiento occidental la imposibilidad, demostrada por Kurt Gödel, la imposibilidad de: tener un sistema de lógica formal y lenguajes que permitan que, en un mismo lenguaje de primer nivel: se pueda "no tener" afirmaciones verdaderas que sí se puedan auto demostrar en el mismo lenguaje. Es decir: todo lenguaje logico fromal de primer niel requiere “fundamentarse” para la demostracion de al menos una de sus afirmaciones propuestas desde afuara del mismo lenguaje.

El ejemplo básico es:

Ésta afirmación es falsa.

La aparente simplicidad de lo referente a ésta falta de o imposibilidad de lograr que no se llegue a una "necesaria por indispensable" referencia externa al marco lógico de referencia donde exista lo verdadero o la certeza acerca de lo "afirmado", tiene implicaciones muy profundas. Estas implicaciones van, principalmente, en cuanto a:

El impetuoso ánimo occidental de requerir un tipo de veracidad o certeza lógica, científica, contrastable y repetible que sea totalmente objetiva y "no subjetiva".

AHXIOM propone la integración desde sus cimientos o fundamentos de ésta Verdad Lógica de incompletud y tautología inherentes a todos los lenguajes logicos formales, y especialmente: a los del tipo axiomático, como la teoría de números y la de conjuntos.

La Tautología en AHXIOM se propone como íntimamente ligada e inclusoa ser idéntica a la:

Autosimilitud Tautológica y/o Autorreferencia Esencial y/o Fundamental.

La Auto Similitud Tautológica AHXIOM se describe y conceptualiza como:

Existe una indispensable Autosimilitud Conceptual en cada uno y todos los presupuestos, postulados, principios, leyes, nociones primitivas y axiomas en cada teoría que pretenda otorgar un marco teórico lógico y coherente para que tal dicho marco funcione como referente en la investigación de lo verdadero, cierto y sus contrarios lógicos:

Lo falso, no verdadero, no cierto y, además: lo incierto y aquello de lo que jamás se podrá tener certeza o veracidad absoluta al respecto.

AHXIOM entiende y comprende que es una apuesta muy grande y que se requerirá un esfuerzo importante en la aceptación, adimisión y asimilación de las Nociones Consideraciones Lógicas Arcáicas (Primitivas) de AHXIOM, las AhxCLASSS.

Los 3 +1 o 3 y 1+ niveles lógicos jerárquicos AHXIOM:

• ΩMML Es el nivel Tautilógico Autoreferente Fundamental: la hipótesis que funciona y opera como fundamento "aquí y así comienza y se sustenta..." Todo lo demás. En AHXIOM también se le indica y simboliza como Ω.

• ΩML Es el Nivel Logico de los Lenguajes que usamos para Fundamentar, Sostener y Validar el uso de los lenguajes Logicos prácticos de ΩL y sus consecuencias: los de nivel 1, como, la lógica formal.

• ΩL Es el Nivel donde UTILIZAMOS todas las operaciones y funciones Lógicas y Coherentes, Semánticas (Significados, Conceptos y sus Ideas), Sintácticas (Buen Orden, Gramática y Secuenciación en los tiempos, orden y modos de ejecución de las funciones lógicas de los operadores Lógicos son indispensables) y Semióticas (La adecuada simbolización de cada idea y su o sus conceptos). En AXHIOM se le denomina: modelo S.SSS donde la S. Implica, Significa y Simboliza la Simbiosis e Interdependencia en lo SSS de cada una de las 4 S.

Se puede ver que:

Cada vez que se analiza y evalúa un concepto y su idea asociada perteneciente a MML, ML y/o L éste analisis:

Siempre Ocurre en L.

Éste modelo de: "Bajar y Subir y Subir y Bajar" entre niveles logicos AHXIOM nos ofrece una versatilidad que, desde lo conocido por el autor, no está presente en ningún otro modelo teórico.

El Uno Más o 1+, el no nivel: Lo verdaderamente No Lógico.

El siguiente aporte de AHXIOM es "Lo Inmaginable", lo "No Número".

En AHXIOM se agrega, asumiendo y afirmando la "presencia" de Un Algo No Imaginable, no Lógico, más allá del Vacío, una algo que podemos "como Imaginar" e intentar significar y simbolizar, al menos: indicándolo y simbolizándole.

En ΩML y ΩL lo “No Número” se propone como "Lo Opuesto" a lo Autosimilar Supremo o Únicamente Tautológico, lo purameanente Semántico:

Ω que es, al mismo tiempo ΩMML, lo UNO que podemos entender como el Conjtnto de Todos los Conjuntos Posibles y Probables de imaginar en S.SSS AHXIOM y mismo que Se Auto Contiene y Contiene a Todos los Entes Objeto, Contenedores y Agregados imaginables en Ω = ΩMML.

Así:

Lo No Número AHXIOM ES Inimaginable, No Numerable... Ni siqueira como 0 o ∅, cero o conjunto vacío, ES lo Nada Real, más allá de cualquier relidad que pueda existir en Ω e Imaginarse en y desde alguno de los niveles lógicos de Ω. Es decir:

Si lo puedo imaginar: Es y Existe en Ω.

Lo No Número en AHXIOM ni siquiera es idéntico a Si Mismo, ya que eso y ello ES Ω.

Lo No Número y No Ω existe como símbolo semántico y sintáctico en ΩML y ΩL, por lo tanto:

No Número y No Ω No ES lo No Número.

Lo No Número: ES, más no Existe.

En AHXIOM se entiende y comprende que podemos pasarnos la eternidad discutiendo acerca de:

Lo No Número y Ω y ΩMML, como ha ocurrido desde la prehistoria, con los yoguis, el Vedanta Advaita Upanishads, el Tao en el Zen, el Ein Sof Aur u Or cabalista, y: ahí está, en ΩL desde ΩML y como algo contrario a Ω y su/lo ΩMML, para quien le sea de interés intentar salir de su imaginación hacia lo no imaginable. En algunas tradiciones se desciben: lo conocido, lo desconocido y lo qe es aquelo que no se puede conocer.

El SER, el Ser y el ser en AHXIOM.

SER: Verbo Infinitivo que la SER, accion de ser a todo lo que ES. En AHXIOM: Los EOs AHXIOM.

Ser: un Ente Objeto AHXIOM que participa del Verbo SER, ya que un Ser ES un Ser.

El ser: implica el "estar siendo del Ser o los Seres y siendo en el ahora y hacia el deveinr". También es: el llegar a ser de cualquier Ser en Ω y ΩMML.

Los Entes Objeto y/o Objetos Énticos o Enticiales.

En AHXIOM un Ente Objeto ES un Ser en Ω y ΩMML, ML y L.

En AHXIOM se afirma que se usa y agrega el proceso lógico:

Semejanza por Analogía SPA y Analogía por Semejanza APS.

Se puede apreciar una estructura diádica y al mismo tiempo triádica en AHXIOM.

Desde la antigua Grecia se sabe que:

No existen 2 sin un tercero que les relacione.

Esa es la naturaleza diádca polar y especular y triádica relacional básica de AHXIOM.

A↔️B.

Esto nos remite a las razones o ratios y a las proporciones griegas, desde Eudoxo de Cnido, donde:

Una Proportio, Analogía o Proporción es:

La ratio o razón entre 2 ratio-razones:

(A↔️B)↔️(C↔️D)

Vemos que nace la complejidad, ya que:

¿Que relación existe entre cada Variable A, B, C y/o D y su Operador Funcional OF ↔️ y la Función Operadora FO de cada EO, ya A,B, C, D o ↔️ y: "(" o ")"? Incluso:

¿Cuál es la ratio, razón o relación y proporción o analogía existente, qué es lo que hay entre cada uno de esos símbolos y "en donde están" y, especialmente:

Entre ellos o cada uno de ellos y aquel quién hace de: quien es quien los escribe= existencia, lee o les recuerda y experimenta, imagina y piensa, mentaliza y concientza, quién está siendo conciente y es consciente de ellos al atencionar su atencion desde, en y hacia ellos?

Así AHXIOM introduce al Sujeto S¹ Quien, aquél EO y OF (Operación Funcional) Quien Interpreta en S.SSS y Asume las Afirmaciones Aceptadas y Admitidas A.AAA como validas y ciertas en AHXIOM, Aquél Quién es un S¹ Quien las Asume e Integra, las Asimila como viables de "Hacer de" (funcionar y operar como) un modelo eficiente y suficiente para "razonar y proporcionar (de afirmar o proponer proporciones) relaciones lógicas Simbólicas Semántico Sintáctico Semióticas entre y de los EOs AHXIOM y sí mismo.

Este proceso se denomina y simboliza:

A.AAA Ahxiom. Donde A. Es e indica el Asumir de un Sujeto S¹ Quien AAA los EOs que Asimila para su manejo intelectual, operacional y funcional.

Antes de continuar con las Operaciones Funcionales y las Funciones Operadoras OF y FO AHXIOM, definamos los EOs:

Contenedor y Agregado AHXIOM.

En AHXIOM cada EO Es un Contenedor S.SSS A.AAA y es y ha sido: Agregado a y en AHXIOM por un Sujeto S¹ Quien "Hizo" tal A.AAA en S.SSS AHXIOM.

Cada Contenedor y Cada Agregado AHXIOM es al mismo tiempo ambas Cosas, pues cada EO AHXIOM en polisemántico. Los EOs AHXIOM son polisémicos.

Afirmamos que lo antes expuesto con respecto a las ratios razones y proporciones proporciones es suficiente y eficiente para determinar y abarcar tal afirmación anterior. Son polisémicos. Un Contenedor≈ Conjunto, un agregado≈ un elemento.

De las OF y las FO AHXIOM.

Un o una Operador Funcional OF y Función Operadora FO AHXIOM comparten la dualidad triádica que les es ortorgada por un Sujeto S¹ Quien, en el modelo teórico AHXIOM, les otorga S.SSS por el A.AAA.

Un Operador Funcional OF AHXIOM sería el:

+

En A+B

Una Función Operadora en AHXIOM sería el:

=

En: A+B= C.

Podemos ver que hay un tipo de dialéctica diádica entre A y B con el