PA‑DIM Semantic Model for Process IoT Market Outlook

According to our latest research, the global PA‑DIM Semantic Model for Process IoT market size reached USD 1.62 billion in 2024, with a robust year-on-year expansion. The market is expected to grow at a CAGR of 16.3% from 2025 to 2033, projecting a value of USD 4.67 billion by 2033. This growth is primarily driven by the increasing adoption of Industry 4.0 standards, the need for standardized data exchange, and the proliferation of smart manufacturing initiatives that demand seamless interoperability across diverse process automation systems.

The growth of the PA‑DIM Semantic Model for Process IoT market is underpinned by the rising demand for data-driven decision-making in process industries. As manufacturers and process operators strive to optimize operational efficiency, reduce downtime, and enhance product quality, the need for standardized data models becomes critical. The PA‑DIM (Process Automation Device Information Model) semantic model enables consistent, machine-readable data representation across heterogeneous IoT devices and systems. This harmonization allows for improved integration, analytics, and automation, which in turn accelerates digital transformation initiatives across industries such as oil & gas, chemicals, pharmaceuticals, and food & beverage. The ability to unify disparate device data for real-time monitoring and control is a key factor driving widespread adoption.

Another major growth factor for the PA‑DIM Semantic Model for Process IoT market is the increasing regulatory pressure and compliance requirements in process industries. Regulatory bodies are mandating stricter safety, quality, and environmental standards, which require comprehensive and reliable data capture, storage, and reporting. The PA‑DIM semantic model facilitates transparent data exchange and auditability, making it easier for organizations to comply with international standards such as ISA-95, OPC UA, and ISO 9001. This regulatory alignment not only minimizes compliance risks but also encourages process standardization, which further fuels market expansion. Additionally, the growing emphasis on predictive maintenance and asset management, supported by the PA‑DIM model, is enabling organizations to shift from reactive to proactive maintenance strategies, leading to cost savings and increased asset longevity.

The accelerating pace of digitalization and the convergence of IT and OT (Operational Technology) ecosystems are also significant contributors to market growth. The PA‑DIM semantic model bridges the gap between these traditionally siloed environments by offering a common language for device information. This interoperability is crucial for implementing advanced analytics, machine learning, and AI-driven process optimization. As organizations invest in cloud-based platforms and edge computing solutions, the PA‑DIM model ensures that data from sensors, actuators, and control systems is accessible, contextualized, and actionable. The ongoing shift towards smart factories and connected enterprises is expected to sustain high demand for PA‑DIM-enabled solutions throughout the forecast period.

Regionally, North America continues to dominate the PA‑DIM Semantic Model for Process IoT market due to its early adoption of digital technologies, strong presence of leading process industries, and significant investments in industrial automation. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, government-led smart manufacturing initiatives, and increasing awareness of the benefits of semantic interoperability. Europe also represents a substantial share, backed by its stringent regulatory frameworks and focus on sustainability. The Middle East & Africa and Latin America are witnessing steady growth as industries in these regions modernize their infrastructure and seek to improve operational efficiencies.

Component Analysis

The Component segment

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

I. IDENTIFYING INFORMATION

Title* SuperSim (repackaged for Superlim) v1.1

Subtitle A test set for word similarity and relatedness in Swedish

Created by* Simon Hengchen (simon.hengchen@gu.se), Nina Tahmasebi (nina.tahmasebi@gu.se)

Publisher(s)* Språkbanken Text

Link(s) / permanent identifier(s)* https://spraakbanken.gu.se/en/resources/superlim

License(s)* CC BY 4.0

Abstract* SuperSim is a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1360 word-pairs independently judged for both relatedness and similarity by five annotators.

Funded by* Swedish Research Council (grant no. 2018-01184 to Nina Tahmasebi); Språkbanken Text

Cite as [1]

Related datasets See https://doi.org/10.5281/zenodo.4660084 for the complete data set accompanying [1], including baseline models and corpus material. The data described in this documentation sheet is the gold data from this larger archive. This repackaging of the gold data was done in the context of the SuperLim collection. See https://spraakbanken.gu.se/en/resources/superlim

II. USAGE

Key applications Evaluation of language models

Intended task(s)/usage(s) (1) Predict semantic similarity of word pairs from a language model

(2) Predict semantic relatedness of word paris from a language model

Recommended evaluation measures Krippendorff's alpha (the official SuperLim measure), Spearman's rho

Dataset function(s) Few-shot training ("prompting"), testing

Recommended split(s) A few-shot training set (aka "prompt", 10%), test set (90%). The prompt was added with the GPT-like models in mind. For those models that do not need a prompt, it can be ignored. The word pairs in the train test are the same for the two tasks.

III. DATA

Primary data* Text

Language* Swedish

Dataset in numbers* 1360 word pairs with semantic similarity and semantic relatedness scores, of those 131 train items and 1229 test items.

Nature of the content* Semantic similarity refers to the extent to which two concepts share semantic properties. Synonymy is the culmination of this concept. Relatedness is a looser lexical conceptual relation that refers to the general (psychological) assocation that may arise for instance because there are causal or instrumental relations between two concepts, or because concepts co-occur frequently, etc, etc. Similarity and relatedness are given as scores between 0 and 10, these scores are in turn averages of judgements on an 11-point scale (0–10).

Format* The data is split over two files, one for each score. The files are provided both as JSONL and tab separated. TSVs contain the following 8 columns:

(1) word 1

(2) word 2

(3)–(7) individual annotator scores (integer valued)

(8) average score (real valued)

Data source(s)* The word pairs were translated from SimLex-999 [2] and WordSim353 [3]. The complete set was manually checked and if needed pairs were adjusted (split into multiple or removed) depending on the lexical distinctions made in Swedish. The similarity and relatedness judgements were collected from five annotators, who were paid for the assignment. One of the annotators was also involved in translating the dataset. See discussion in [1].

Data collection method(s)* Online collection of judgements from (paid) annotators. Annotators used written instructions from SimLex-999 [2]. See discussion in [1].

Data selection and filtering* See discussion in [1]

Data preprocessing* See discussion in [1]

Data labeling* Both the similarity and relatedness scores are manual (gold standard).

Annotator characteristics All annotators are native speakers of Swedish who hold linguistic degrees. Two have prior lexicographic experience. See [1] for more details.

IV. ETHICS AND CAVEATS

Ethical considerations None to report.

Things to watch out for The word pairs are presented out of context. Superlim presently does not prescribe a methodology for the application of contextual (dynamic) language models to this data, which means we can expect considerable variation between test data uses. For reasons of comparability and reproducability, users must make sure to report their chosen method clearly. See also the remarks in the FAQ on https://spraakbanken.gu.se/resurser/superlim

V. ABOUT DOCUMENTATION

Data last updated* 20220920 (v1.1), Aleksandrs Berdicevskis

Which changes have been made, compared to the previous version* Minor format changes

Access to previous versions Work in progress

This document created* 20210611, Gerlof Bouma (gerlof.bouma@gu.se)

This document last updated* 20230203, Aleksandrs Berdicevskis

Where to look for further details The attached readme file

Documentation template version* v1.1

VI. OTHER

Related projects SimLex-999 [2]; WordSim353 [3]

References [1] Hengchen and Tahmasebi (2021): SuperSim: a test set for word similarity and relatedness in Swedish. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). https://ep.liu.se/ecp/178/027/ecp2021178027.pdf

[2] Hill, Reichart and Korhonen (2015): SimLex-999: Evaluating semantic models with (genuine) similarity estimation. Computational Linguistics, 41(4): 665–695. https://doi.org/10.1162/COLI_a_00237

[3] Finkelstein, Gabrilovich, Matias, Rivlin, Solan, Wolfman and Ruppin (2002): Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems, 20(1):116-131. https://doi.org/10.1145/503104.503110

This lexicon is subdivided into five different subsets:L0072-01 Full lexiconL0072-02 Phonetic layerL0072-03 Morphological layerL0072-04 Syntactic layerL0072-05 Semantic layerPAROLE-SIMPLE-CLIPS is a four-level, general purpose lexicon that has been elaborated over three different projects. The kernel of the morphological and syntactic lexicons was built in the framework of the LE-PAROLE project. The linguistic model and the core of the semantic lexicon were elaborated in the LE-SIMPLE project, while the phonological level of description and the extension of the lexical coverage were performed in the context of the Italian project Corpora e Lessici dell'Italiano Parlato e Scritto (CLIPS). The PAROLE-SIMPLE-CLIPS Pisa Italian Lexicon comprises a total of 387,267 phonetic units, 53,044 morphological units (53,044 lemmas), 37,406 syntactic units (28,111 lemmas) and 28,346 semantic units (19,216 lemmas). It was encoded at the semantic level, in full accordance with the international standards set out in the PAROLE-SIMPLE model and based on EAGLES. Syntactic and semantic encoding were performed jointly with Thamus (Consortium for Multilingual Documentary Engineering).PAROLE-SIMPLE-CLIPS offers therefore the advantage of being compatible with the other eleven PAROLE-SIMPLE lexicons that were built for European languages and that share a common theoretical model, representation language and building methodology.A PAROLE-SIMPLE-CLIPS entry gathers together all the phonological, morphological and inherent syntactic and semantic properties of a headword. Its subcategorization pattern is (or are) described in terms of optionality, syntactic function, syntagmatic realization as well as morpho-syntactic, syntactic and lexical properties of each slot filler. At the semantic level, the theoretical approach adopted by the SIMPLE model is essentially grounded on a revisited version of some fundamental aspects of the Generative Lexicon. A SIMPLE-CLIPS semantic unit is richly endowed with a wide range of fine-grained, structured information, most relevant for NLP applications. First among them, the ontological typing: the lexicon is in fact structured in terms of a multidimensional type system based on both hierarchical and non-hierarchical conceptual relations, taking into account the principle of orthogonal inheritance. Other relevant information types in a word entry are its domain of use; type of denoted event; synonymy and morphological derivation relations; membership in a class of regular polysemy as well as any relevant distinctive semantic features. Particularly outstanding is the information encoded in the Extended Qualia Structure (a set of 60 semantic relations that allow modelling both the different meaning dimensions of a word sense and its relationships to other lexical units) and the Predicative Representation which describes the semantic scenario the word sense considered is involved in and characterizes its participants in terms of thematic role...

The global semantic knowledge discovery software market is projected to reach a value of $2,249.3 million by 2033, expanding at a CAGR of 7.9% during the forecast period of 2025-2033. This growth is primarily driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies, which are enabling organizations to automate and streamline their knowledge discovery processes. The cloud-based deployment model is expected to gain significant traction over the forecast period due to its cost-effectiveness, scalability, and flexibility. The increasing demand for personalized and relevant content in various industries, such as education, advertising, and transportation, is also fueling the growth of the semantic knowledge discovery software market. Enterprises are leveraging these solutions to analyze large volumes of unstructured data and extract meaningful insights, which can be utilized for decision-making, product development, and customer engagement. North America and Europe are anticipated to be the dominant regions in the market, owing to the presence of a large number of well-established vendors and early adoption of AI and ML technologies. Asia Pacific is another promising region, driven by the rapid growth of the IT and telecommunications sectors.

ASHRAE 223P Semantic Interop Gateway Market Outlook

According to our latest research, the global ASHRAE 223P Semantic Interop Gateway market size reached USD 1.18 billion in 2024, demonstrating robust momentum with a Compound Annual Growth Rate (CAGR) of 13.6%. This growth is primarily driven by the accelerating adoption of interoperability standards in smart building and industrial automation sectors. By 2033, the market is forecasted to reach USD 3.92 billion, reflecting the increasing demand for seamless integration across diverse digital infrastructure and the critical role of semantic interoperability in future-ready smart environments. As per our latest research, the market’s expansion is fueled by the convergence of IoT, data-driven decision-making, and the imperative for scalable, interoperable systems across various domains.

The growth trajectory of the ASHRAE 223P Semantic Interop Gateway market is underpinned by the rapid digitalization of urban infrastructure and the proliferation of smart devices. As cities and enterprises embrace digital transformation, the need to unify disparate building management systems and industrial protocols has become paramount. The ASHRAE 223P standard, which defines a framework for semantic interoperability, is increasingly being adopted to enable seamless data exchange and integration. This is particularly crucial in environments where multiple vendors and legacy systems coexist, making interoperability a significant challenge. The gateway solutions, by translating and harmonizing data semantics, are empowering organizations to unlock new efficiencies, enhance automation, and drive innovation in building operations, energy management, and industrial processes.

Another significant growth factor for the ASHRAE 223P Semantic Interop Gateway market is the rising emphasis on sustainability and energy optimization. Governments and regulatory bodies across the globe are implementing stringent energy codes and standards, compelling commercial and industrial facilities to invest in advanced automation and monitoring solutions. The ability of semantic interop gateways to facilitate real-time data sharing and analytics across heterogeneous systems supports comprehensive energy management strategies. This not only aids in achieving regulatory compliance but also delivers tangible cost savings and operational resilience. The growing adoption of cloud-based solutions further amplifies the market, as organizations seek scalable, flexible, and future-proof interoperability frameworks that can adapt to evolving regulatory and business requirements.

The expanding ecosystem of smart cities and the integration of IoT devices are also propelling the demand for semantic interoperability. As urban environments become more connected, the need to ensure that various devices, platforms, and applications can communicate effectively becomes critical. The ASHRAE 223P Semantic Interop Gateway enables this by providing a standardized approach to data semantics, thereby reducing integration complexity and fostering innovation in urban mobility, public safety, and citizen services. These gateways are instrumental in enabling the convergence of IT and operational technologies, driving value across a wide spectrum of applications, from smart grids to intelligent transportation systems.

From a regional perspective, North America currently dominates the ASHRAE 223P Semantic Interop Gateway market, accounting for the largest share due to early adoption of smart building technologies and strong regulatory frameworks. Europe follows closely, driven by ambitious sustainability targets and advanced industrial automation. The Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, government-led smart city initiatives, and increasing investments in digital infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing awareness of interoperability standards and the modernization of urban and industrial infrastructure.

Component Analysis

The Component segment of the ASHRAE 223P Semantic Interop Gateway market is categorized into software, hardware, and services, each playing a pivotal role in the overall value chain. Software solutions constitute the core of semantic interoperability, providing the algorithms, data models, and translation engines required to harmonize diverse protocols and data

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

Project Haystack Semantic Tagging Tools Market Outlook

According to our latest research, the Project Haystack Semantic Tagging Tools market size reached USD 1.36 billion in 2024, reflecting robust adoption across smart building and energy management sectors. The market is poised for significant expansion, projected to reach USD 4.47 billion by 2033, growing at a CAGR of 14.2% during the forecast period. This rapid growth is driven by increasing demand for data-driven building automation, the proliferation of IoT devices, and a global push for energy efficiency and sustainability.

The primary growth factor for the Project Haystack Semantic Tagging Tools market is the surging integration of IoT and smart devices within building automation systems. As the number of connected devices multiplies, the complexity and volume of data generated have exponentially increased. Project Haystack provides a standardized methodology for semantic data modeling and tagging, enabling seamless interoperability among disparate building systems. Organizations are leveraging these tools to unlock actionable insights from their data, optimize building operations, and enhance occupant comfort. The market’s momentum is further accelerated by the growing awareness of the benefits of semantic interoperability, which ensures that data from various sources can be easily understood, exchanged, and aggregated for advanced analytics and machine learning applications.

Another crucial growth driver is the global emphasis on energy management and sustainability initiatives. Governments and regulatory bodies are enforcing stringent energy efficiency standards, compelling building owners and facility managers to adopt advanced technologies. Project Haystack Semantic Tagging Tools play a pivotal role in enabling real-time monitoring, predictive maintenance, and automated control of energy-consuming assets. By providing a unified semantic framework, these tools help organizations identify inefficiencies, reduce operational costs, and achieve sustainability targets. The trend toward net-zero buildings and green certifications is further propelling the adoption of semantic tagging solutions, particularly in commercial and industrial sectors.

Additionally, the evolution of cloud computing and the increasing availability of cloud-based deployment options are significantly influencing market growth. Cloud-based Project Haystack solutions offer scalability, ease of integration, and remote accessibility, making them attractive to enterprises of all sizes. The ability to deploy semantic tagging tools without substantial upfront infrastructure investment is particularly beneficial for small and medium-sized enterprises (SMEs) seeking to modernize their building operations. Furthermore, the rise of smart cities and digital twins is creating new avenues for market expansion, as semantic tagging is foundational to the interoperability and data standardization required in these complex ecosystems.

From a regional perspective, North America remains the dominant market for Project Haystack Semantic Tagging Tools, driven by early adoption of smart building technologies and a mature IoT ecosystem. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, increased construction of green buildings, and government-led smart city initiatives. Europe is also experiencing significant uptake, particularly in countries with stringent energy efficiency regulations and strong sustainability commitments. Latin America and the Middle East & Africa are emerging markets, with growing awareness and gradual adoption of semantic tagging tools in commercial and industrial facilities.

Component Analysis

The Project Haystack Semantic Tagging Tools market is segmented by component into software and services. The software segment encompasses platforms and tools that facilitate semantic data modeling, tagging, and management in building automation and energy management systems. This segment holds the largest market share, driven by the increasing demand for sophisticated solutions that can efficiently handle the growing complexity of building data. Project Haystack-compliant software enables organizations to standardize data from multiple sources, ensuring seamless integration and interoperability. The continuous evolution of these platforms, with enhanced features such as AI-powered analytics and intuitive user interfaces, is further f

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.

This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towards what?". Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis, we determine what works and what might be done to make further improvements for ORL. Data for ORL Download MPQA 2.0 corpus. Check mpqa2-pytools for example usage. Splits can be found in the datasplit folder. Data for SRL The data is provided by: CoNLL-2005 Shared Task, but the original words are from the Penn Treebank dataset, which is not publicly available. How to train models? python main.py --adv_coef 0.0 --model fs --exp_setup_id new --n_layers_orl 0 --begin_fold 0 --end_fold 4 python main.py --adv_coef 0.0 --model html --exp_setup_id new --n_layers_orl 1 --n_layers_shared 2 --begin_fold 0 --end_fold 4 python main.py --adv_coef 0.0 --model sp --exp_setup_id new --n_layers_orl 3 --begin_fold 0 --end_fold 4 python main.py --adv_coef 0.1 --model asp --exp_setup_id prior --n_layers_orl 3 --begin_fold 0 --end_fold 10

Data sharing restrictions are common in NLP datasets. For example, Twitter policies do not allow sharing of tweet text, though tweet IDs may be shared. The situation is even more common in clinical NLP, where patient health information must be protected, and annotations over health text, when released at all, often require the signing of complex data use agreements. The SemEval-2021 Task 10 framework asks participants to develop semantic annotation systems in the face of data sharing constraints. A participant's goal is to develop an accurate system for a target domain when annotations exist for a related domain but cannot be distributed. Instead of annotated training data, participants are given a model trained on the annotations. Then, given unlabeled target domain data, they are asked to make predictions.

Website: https://machine-learning-for-medical-language.github.io/source-free-domain-adaptation/

CodaLab site: https://competitions.codalab.org/competitions/26152

Github repository: https://github.com/Machine-Learning-for-Medical-Language/source-free-domain-adaptation

Annotated corpora for MESINESP2 shared-task (Spanish BioASQ track, see https://temu.bsc.es/mesinesp2). BioASQ 2021 will be held at CLEF 2021 (scheduled in Bucharest, Romania in September) http://clef2021.clef-initiative.eu/

Introduction: These corpora contain the data for each of the subtracks of MESINESP2 shared-task:

[Subtrack 1] MESINESP - Medical indexing:

Training set: It contains all spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish. We have filtered out empty abstracts and non-Spanish abstracts. We have built the training dataset with the data crawled on 01/29/2021. This means that the data is a snapshot of that moment and that may change over time since LILACS and IBECS usually add or modify indexes after the first inclusion in the database. We distribute two different datasets:

Articles training set: This corpus contains the set of 237574 Spanish scientific papers in VHL that have at least one DeCS code assigned to them.

Full training set: This corpus contains the whole set of 249474 Spanish documents from VHL that have at leas one DeCS code assigned to them.

Development set: We provide a development set manually indexed by expert annotators. This dataset includes 1065 articles annotated with DeCS by three expert indexers in this controlled vocabulary. The articles were initially indexed by 7 annotators, after analyzing the Inter-Annotator Agreement among their annotations we decided to select the 3 best ones, considering their annotations the valid ones to build the test set. From those 1065 records:

213 articles were annotated by more than one annotator. We have selected de union between annotations.

852 articles were annotated by only one of the three selected annotators with better performance.

Test set: To be published

[Subtrack 2] MESINESP - Clinical trials:

Training set: The training dataset contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API. Clinical trials are not indexed with DeCS terminology, we have used as training data a set of 3560 clinical trials that were automatically annotated in the first edition of MESINESP and that were published as a Silver Standard outcome. Because the performance of the models used by the participants was variable, we have only selected predictions from runs with a MiF higher than 0.41, which corresponds with the submission of the best team.

Development set: We provide a development set manually indexed by expert annotators. This dataset includes 147 clinical trials annotated with DeCS by seven expert indexers in this controlled vocabulary.

Test set: To be published

[Subtrack 3] MESINESP - Patents: To be published

Files structure:

Subtrack1-Scientific_Literature.zip contains the corpora generated for subtrack 1. Content:

Subtrack1:

Train

training_set_track1_all.json: Full training set for subtrack 1.

training_set_track1_only_articles.json: Articles training set for subtrack 1.

Development

development_set_subtrack1.json: Manually annotated development set for subtrack 1.

Subtrack2-Clinical_Trials.zip contains the corpora generated for subtrack 2. Content:

Subtrack2:

Train

training_set_subtrack2.json: Training set for subtrack 2.

Development

development_set_subtrack2.json: Manually annotated development set for subtrack 2.

DeCS2020.tsv contains a DeCS table with the following structure:

DeCS code

Preferred descriptor (the preferred label in the Latin Spanish DeCS 2020 set)

List of synonyms (the descriptors and synonyms from Latin Spanish DeCS 2020 set, separated by pipes.

DeCS2020.obo contains the *.obo file with the hierarchical relationships between DeCS descriptors.

*Note: The obo and tsv files with DeCS2020 descriptors contain some additional COVID19 descriptors that will be included in future versions of DeCS. These items were provided by the Pan American Health Organization (PAHO), which has kindly shared this content to improve the results of the task by taking these descriptors into account.

For further information, please visit https://temu.bsc.es/mesinesp2/ or email us at encargo-pln-life@bsc.es

Since the dawn of computer vision, semantic and geometric scene understanding has been an essential problem. It impacted early works and led to numerous real-world applications. A scene is a place where an agent can act with or navigate. Scene understanding is the process of analyzing the semantic information inside the static 2D image and the relationships between the scene contents. This dissertation presents the use of deep learning models and how they can be applied to recognize the surrounding environment given a single still image. The scene captured in an image contains different objects from various classes and they are looking in different directions. These objects appear in the image either small because they are far from the camera or large because they are very close to the camera. When the scene is complex, we find that some objects are occluded and it is hard sometimes to predict contextual information about the complete 3D object. Also, using a single view always leads to ill-posed problems as there is no enough information to reveal the ambiguity. So, to build a useful and complete understanding of a given scene, we have to answer the following questions: (1) what are the object classes that appear in the image? (2) from which direction do they look at the camera? (3) are the objects far or close to the camera? (4) if some object parts are hidden, how can the machine generate the 3D information of the object? More specifically, this dissertation approaches scene understanding by answering the above-mentioned questions. First, we present a multi-task CNN model that performs object classification and viewpoint estimation simultaneously. These two problems seem to have opposite representative features. For object classification, the features should be powerful so that they are orientation-invariant features. For viewpoint estimation, the features should preserve the orientation characteristic and the ability to describe the same object with different viewpoints. To this end, the proposed CNN has a shared network for both tasks and task-specific sub-networks. With this design, the mentioned problems can be solved using the same model without proposing parallel models and separating the tasks. Second, we address the problem of depth estimation from a single image. We formulate this problem as a regression task and propose two different CNN models to solve this task. The first model uses a series of stacked convolutional layers that generate the depth map from a single image. This model is a fully convolutional network that regresses the depth information for each pixel. However, the generated depth images are smaller than the input images and they are blurry. To overcome this drawback, we propose a second model that has encoder-decoder architecture. This model has an encoder part to extract useful features and a decoder part to reconstruct the image and generate the final depth image. The useful information extracted in the encoder part is transferred to the decoder part using the skip-connections. This information helps in generating more accurate depth images with sharper objects’ parts. For this task, we select a non-convex loss function to train the models and optimize the network weights. The no-convex loss function is robust against to the outliers and converges faster. In the last part of the dissertation, we propose a CNN model that reconstructs 3D point clouds of objects form a single image. Single-view reconstruction is a hard task as the 3D structure can be inferred accurately for specific object classes given restricted assumptions. The proposed model is a simple, yet powerful, that utilizes an initial point cloud of a sphere shape to generate the final point cloud. For this problem, we use a large-scale 3D dataset to train the model. Many images are rendered from each 3D model and used for training. To generate the ground-truth point clouds, we sample the points from the object’s surface. The proposed model takes a single image of an object and generates a point cloud that presents the desired object accurately. An initial sphere is used to spread the points evenly on the reconstructed object surface and prevents the points to be grouped in some parts of the objects.This dissertation demonstrates the leverage of using deep learning technology to give a comprehensive understanding of a scene using a single image only. We conclude this dissertation with a general discussion and propose several potential future research directions for better deep learning understanding and interpretation in solving computer vision tasks.

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.

The Semantic Knowledge Discovery Software market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from complex and unstructured data. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $6 billion by 2033. This growth is fueled by several key factors. The rising adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industries is enabling more sophisticated semantic analysis, leading to improved decision-making. Furthermore, the proliferation of big data, coupled with the limitations of traditional data analysis methods, is driving the demand for solutions that can effectively uncover hidden patterns and relationships within vast datasets. The growing emphasis on data-driven decision-making across sectors like healthcare, finance, and research and development is also contributing significantly to market expansion. Major restraints to market growth include the high initial investment costs associated with implementing semantic knowledge discovery software, the complexity of integrating these solutions with existing IT infrastructure, and the scarcity of skilled professionals capable of managing and interpreting the results generated by these systems. However, these challenges are being addressed through the development of more user-friendly software, cloud-based deployment models that reduce upfront costs, and increased training and education programs focused on semantic technology. The market is segmented by deployment mode (cloud, on-premise), industry (healthcare, finance, manufacturing, etc.), and functionality (data integration, knowledge graph construction, semantic search). Key players like Expert System SpA, ChemAxon, Collexis (Elsevier), MAANA, OntoText, Cambridge Semantics, and Nervana (Intel) are actively shaping the market landscape through innovation and strategic partnerships. The North American market currently holds a significant share, but regions like Asia-Pacific are expected to witness rapid growth in the coming years.

Semantic Search Platform Market Outlook

As per our latest research, the global Semantic Search Platform market size reached USD 6.4 billion in 2024, reflecting rapid adoption across diverse industries. The market is anticipated to grow at a robust CAGR of 21.7% from 2025 to 2033, propelling the total market value to USD 45.3 billion by 2033. This remarkable expansion is primarily driven by the increasing need for advanced search capabilities that deliver contextually relevant results, leveraging artificial intelligence and natural language processing to transform data-driven decision-making and user experiences.

One of the primary growth factors fueling the Semantic Search Platform market is the exponential rise in unstructured data generated by enterprises, consumers, and digital platforms. Traditional keyword-based search methods are no longer sufficient to extract meaningful insights from vast datasets, prompting businesses to invest in semantic search solutions that understand intent, context, and relationships between entities. The integration of AI-powered semantic technologies enables organizations to enhance information retrieval, streamline workflows, and deliver personalized experiences, which is particularly critical in sectors such as e-commerce, healthcare, and BFSI. As digital transformation accelerates, companies are increasingly recognizing the value of semantic search in improving operational efficiency and customer satisfaction.

Another significant driver is the rapid advancements in natural language processing (NLP) and machine learning algorithms. These technologies empower semantic search platforms to comprehend user queries in a human-like manner, identifying nuances, synonyms, and contextual meaning. The evolution of generative AI and large language models has further elevated the capabilities of semantic search, enabling platforms to handle complex, multi-intent queries and provide richer, more accurate results. This technological progress has prompted both established enterprises and innovative startups to embrace semantic search solutions, thereby expanding the market’s reach across various verticals. Furthermore, the proliferation of multilingual and voice-based search functionalities is enabling broader adoption, especially in regions with diverse linguistic landscapes.

The growing emphasis on enhancing user experience and decision-making processes is also a major catalyst for market growth. In highly competitive sectors such as retail, IT and telecommunications, and media, organizations are leveraging semantic search platforms to deliver hyper-personalized recommendations, content discovery, and customer support. By understanding the context and intent behind user queries, businesses can anticipate needs, reduce search friction, and boost engagement. Additionally, the integration of semantic search with enterprise knowledge management systems and digital assistants is streamlining information access for employees, driving productivity and innovation. This trend is expected to intensify as organizations increasingly prioritize data-driven strategies and digital customer journeys.

From a regional perspective, North America remains the largest market for semantic search platforms, accounting for a significant share of global revenues in 2024, followed by Europe and Asia Pacific. The strong presence of technology giants, early adoption of AI-driven solutions, and a mature digital infrastructure contribute to North America's leadership. Europe is witnessing substantial growth, driven by strict data privacy regulations, digital transformation initiatives, and increased investments in AI research. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by the rapid expansion of e-commerce, mobile internet penetration, and government-led digital initiatives. Latin America and the Middle East & Africa are also registering steady progress, albeit from a smaller base, as organizations in these regions embrace next-generation search technologies to enhance competitiveness and service delivery.

Component Analysis

The Semantic Search Platform market is segmented by component into software and services, each playing a crucial role in shaping the industry’s landscape. The software segment dominates the market, accounting for the majority of revenue in 2024. Semantic search software comprises advanced algorithms, AI engines, and user interfaces that enable organizations to deploy context-

Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj , similarityij ). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.

For more details see:

The web page of the RUSSE evaluation campaign: http://russe.nlpub.ru/downloads

The original publication "Panchenko A., Ustalov D., Arefyev N., Paperno D. Konstantinova N., Loukachevitch N. and Biemann C. undefinedHuman and Machine Judgements about Russian Semantic Relatedness. In Proceedings of the 5th Conference on Analysis of Images, Social Networks and Texts (AIST'2016). Communications in Computer and Information Science (CCIS). Springler-Verlag Berlin Heidelberg": https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/aist_2016_hmj.pdf

Results of discussion groups at the Linked Science Workshop 2013 held at the International Semantic Web Conference. (http://linkedscience.org/events/lisc2013/) Participants were asked to develop a matrices about how semantic web/linked data solutions can help address reproducbility/re* problems. The results are documented in the spreadsheets above and described in videos (to be posted) The participants also developed a set of challenges for the Linked Science and broader semantic web community to help address these re* problems. See below or (lisc2013-challenges.txt) Linked Science Community Challenges The Linked Science 2013 workshop discussion participants identified several challenges to the Linked Data/Semantic Web community in order to help reproducibility (and other re* problems i.e. repurposing, reuse, etc) in science. 1) Promote the basics of linked data for reproducibility Many basic linked data technologies (e.g. content negotiation or the use of dereferenceable URLs) could be usable for scientific reproducibility and reproducibility. The goal here would be to develop a set of how-to documents that guide e-scientists on how to use these technologies to support scientific re* problems. An important point would be to tie these solutions directly to domain scientist problems. 2) Integrate Semantic Web technologies and the publishing process. Publishing is central to the scientific process and the issues of reusing scientific work. Semantic Web technologies should be integrated into the publishing process to enable reuse. 3) Make it easier to publish data and then work with it than work directly on your own data. Publishing data should enable a scientist to do more. Can we make it so that publishing data is so useful to the scientist themselves that it would be their first option? 4) Provide an integrated view of the how, what, when, where, and why of the scientific process. Linked data technologies are designed for integration and aggregation. Can we use these technologies to provide an integrated view over all the questions one might have with respect to a scientific experiment? 5) Provide a mechanisms for dealing with copyright on data both from a technical and social perspective. Dealing with copyright is not always straightforward. Can we eliminate the barriers to reuse through helping scientists with these copyright issues in an automatic fashion. 6) Get an altmetric based award into one of our own venues. Part of supporting re* problems is promoting sharing. We should "eat are own dogfood" by promoting and rewarding sharing in the major semantic web venues. We suggest an award based on some sort of altmetric. 7) Make sure the EBI RDF platform does not get shut down in two years. The European Bioinformatics Institute has released RDF versions with SPARQL endpoints for many of their core data sets. They are making it available for two years and checking on whether it is used to determine if it continues in the long term. This is a key data resource for using Linked Data for reproducibility - let's make sure it keeps going.

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

Project Haystack Semantic Tagging Tools Market Outlook

According to our latest research, the global Project Haystack Semantic Tagging Tools market size reached USD 1.42 billion in 2024, demonstrating robust momentum fueled by increasing digitalization across building and industrial automation sectors. The market is expected to expand at a CAGR of 15.2% during the forecast period, reaching a projected value of USD 4.17 billion by 2033. This significant growth is primarily attributed to the rising demand for interoperability and data standardization in smart infrastructure, as well as the growing adoption of Internet of Things (IoT) technologies in commercial, industrial, and residential environments worldwide.

The primary growth driver for the Project Haystack Semantic Tagging Tools market is the accelerating adoption of building automation and energy management solutions. As organizations strive to optimize operational efficiency and reduce energy consumption, the need for unified, standardized data models has become paramount. Project Haystack’s open-source semantic tagging framework enables seamless integration and sharing of data across diverse systems, devices, and platforms. This capability is particularly critical in smart buildings and industrial automation, where disparate systems must communicate effectively to deliver actionable insights. The proliferation of smart sensors, cloud platforms, and data analytics tools further amplifies the value of semantic tagging, driving widespread implementation across new and retrofitted infrastructure.

Another significant factor contributing to market expansion is the increasing regulatory emphasis on sustainability and energy efficiency. Governments and industry bodies across North America, Europe, and Asia Pacific are mandating stricter standards for building performance and energy reporting. Project Haystack Semantic Tagging Tools facilitate compliance by enabling precise, granular data collection and reporting, which is essential for meeting regulatory requirements and achieving certifications such as LEED and BREEAM. Additionally, the rise of smart cities initiatives globally has accelerated the deployment of semantic tagging tools to support integrated citywide management of utilities, transportation, and public services, further boosting market demand.

Technological advancements and the growing maturity of cloud-based solutions are also propelling the Project Haystack Semantic Tagging Tools market. The shift towards cloud deployment models allows organizations to scale their data infrastructure rapidly, enhance data accessibility, and reduce IT overheads. Cloud-based semantic tagging tools enable real-time data integration and analytics across geographically dispersed assets, supporting advanced use cases such as predictive maintenance, remote monitoring, and adaptive control. As more enterprises embrace digital transformation, the adoption of semantic tagging tools is expected to surge, creating lucrative opportunities for software vendors and service providers.

From a regional perspective, North America continues to dominate the global market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront of smart building and industrial IoT adoption, driven by substantial investments in infrastructure modernization and sustainability initiatives. Europe’s market growth is underpinned by stringent energy efficiency regulations and the rapid proliferation of smart city projects. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by urbanization, government-led digitalization programs, and expanding manufacturing sectors in countries such as China, Japan, and India. The Middle East & Africa and Latin America are also experiencing steady adoption, supported by ongoing infrastructure development and increasing awareness of the benefits of semantic data tagging.

Component Analysis

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