Discover the booming Intelligent Semantic Data Service market! This in-depth analysis reveals key trends, growth drivers, and leading companies shaping this $15 billion (2025 est.) industry, projected to reach $35 billion by 2033. Learn about market segmentation, regional insights, and the future of AI-powered data analytics.

Semantic Data Catalog AI Market Outlook

According to our latest research, the global Semantic Data Catalog AI market size reached USD 1.47 billion in 2024, exhibiting robust momentum driven by the increasing adoption of AI-driven data management solutions. The market is projected to grow at a CAGR of 23.2% from 2025 to 2033, reaching a forecasted value of USD 11.33 billion by 2033. This remarkable growth trajectory is underpinned by the accelerating need for advanced data governance, regulatory compliance, and the seamless integration of disparate data assets across enterprises worldwide. As organizations grapple with ever-increasing data complexity and volume, the demand for semantic data catalog AI solutions continues to surge, transforming the way enterprises manage, discover, and utilize their data assets.

One of the primary growth factors fueling the Semantic Data Catalog AI market is the exponential rise in data generation across industries. Enterprises are increasingly leveraging digital transformation initiatives, leading to a massive influx of structured and unstructured data. This overwhelming data volume necessitates intelligent cataloging solutions capable of delivering context-aware, automated metadata management, and efficient data discovery. Semantic AI-powered data catalogs provide organizations with the ability to extract deeper insights, enhance data lineage, and ensure data quality, thereby driving operational efficiency and supporting informed decision-making. The integration of AI and machine learning algorithms enables these platforms to continuously learn and adapt, making them indispensable tools for modern data-driven enterprises.

Another significant driver is the growing emphasis on regulatory compliance and data governance. With stringent regulations such as GDPR, CCPA, and other data protection laws coming into effect globally, organizations are under mounting pressure to ensure transparency, accountability, and security in their data practices. Semantic data catalog AI solutions facilitate comprehensive data governance by automating the classification, tagging, and monitoring of sensitive data assets. These solutions empower organizations to establish robust data stewardship frameworks, streamline compliance reporting, and mitigate risks associated with data breaches or non-compliance. As a result, businesses across sectors such as BFSI, healthcare, and government are increasingly investing in semantic data catalog AI platforms to safeguard their data and maintain regulatory alignment.

The evolving landscape of business intelligence and analytics is also a key factor propelling the adoption of semantic data catalog AI solutions. As organizations strive to unlock actionable insights from their data, the need for accurate, real-time, and contextually relevant information becomes paramount. Semantic data catalogs, powered by advanced AI capabilities, enable seamless integration with business intelligence tools, facilitating faster data discovery and improved analytics outcomes. By automating data preparation, enrichment, and lineage tracking, these solutions empower data scientists and analysts to focus on higher-value tasks, accelerating time-to-insight and fostering innovation across the enterprise. This trend is particularly pronounced in sectors such as retail, manufacturing, and IT & telecommunications, where data-driven decision-making is a critical competitive differentiator.

Regionally, North America continues to dominate the Semantic Data Catalog AI market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology vendors, early adoption of AI-driven data management solutions, and a mature digital infrastructure have positioned North America at the forefront of market growth. However, Asia Pacific is emerging as a high-growth region, driven by rapid digitalization, increasing investments in AI technologies, and a burgeoning startup ecosystem. Europe remains a key market, propelled by stringent data protection regulations and a strong focus on data governance. Latin America and the Middle East & Africa are also witnessing steady growth, supported by digital transformation initiatives and rising awareness of the benefits of semantic data catalog AI solutions.

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

Additional file 1 All experiment measurements. Excel file contains all presented measurements for DISC, DOT, and m2801 dataset.

Metadata Enrichment for Retrieval Market Outlook

According to our latest research, the global metadata enrichment for retrieval market size reached USD 2.47 billion in 2024, with a robust year-on-year growth driven by the increasing demand for advanced search and data discovery solutions across industries. The market is projected to grow at a CAGR of 16.2% from 2025 to 2033, reaching an estimated USD 10.99 billion by 2033. This remarkable expansion is propelled by the proliferation of unstructured data, the necessity for efficient information retrieval mechanisms, and the adoption of artificial intelligence-powered metadata enrichment tools.

One of the primary growth drivers of the metadata enrichment for retrieval market is the exponential surge in digital content creation across multiple sectors. Organizations in media and entertainment, e-commerce, and healthcare are generating vast volumes of data, much of which is unstructured and difficult to search or analyze without proper metadata tagging and enrichment. As businesses aim to extract actionable insights and improve operational efficiency, they are increasingly investing in metadata enrichment solutions that leverage machine learning, natural language processing, and semantic analysis. These technologies enable organizations to enhance the discoverability, relevance, and context of stored information, thereby facilitating faster and more accurate retrieval processes. The growing emphasis on digital transformation initiatives and data-driven decision-making further amplifies the demand for sophisticated metadata enrichment platforms.

Another significant factor contributing to the market’s growth is the rising adoption of cloud-based solutions. As enterprises migrate their data assets to the cloud, the need for scalable and automated metadata enrichment tools becomes paramount. Cloud deployment not only offers flexibility and cost-efficiency but also ensures seamless updates and integration with other enterprise applications. This trend is particularly prominent among small and medium enterprises (SMEs), which benefit from the lower upfront costs and operational agility provided by cloud-based metadata enrichment services. Furthermore, the integration of artificial intelligence and machine learning algorithms into these platforms allows for continuous improvement in metadata quality, accuracy, and contextualization, driving further market growth.

Regulatory compliance and data governance requirements are also playing a pivotal role in shaping the metadata enrichment for retrieval market. Industries such as BFSI, healthcare, and government are subject to stringent data management regulations that mandate proper classification, retention, and retrieval of information. Metadata enrichment not only streamlines compliance efforts but also reduces the risks associated with data breaches and loss. As organizations seek to mitigate compliance-related risks and enhance their data governance frameworks, the adoption of advanced metadata enrichment solutions is expected to witness sustained momentum. Additionally, the increasing focus on data privacy and security further incentivizes enterprises to invest in robust metadata management and enrichment capabilities.

From a regional perspective, North America continues to dominate the global metadata enrichment for retrieval market, accounting for the largest share in 2024 due to the presence of leading technology providers, early adoption of advanced analytics, and a strong emphasis on digital transformation. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, fueled by rapid digitization, expanding internet penetration, and increased investments in IT infrastructure across emerging economies. Europe is also witnessing significant growth, driven by stringent data protection regulations and the increasing adoption of AI-powered metadata enrichment tools among enterprises and public sector organizations.

Component Analysis

The compone

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

Collection of tools developed by GO Consortium and by third parties. Tools are listed by category or alphabetically and continue to be improved and expanded.

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

This study presents a scientometric analysis of the representation of museological objects in the international scientific literature. It employs a dual quantitative-qualitative approach, combining bibliometric analysis, domain analysis, and content analysis. Data from the Web of Science Core Collection were used, spanning from 2012 to 2022, and tools such as EndNote x20, Biblioshiny, and VOSviewer. The scientific production over time shows an initial stability in research output from 2012 to 2016, followed by a marked increase from 2017 onwards. The analysis of 491 retrieved documents reveals that 40 authors are the elite contributors in museological studies, revealing the field's global significance. Integration of information science with museology is evident in the retrieved works, reflecting an interdisciplinary approach. Finally, the research concludes that online access to museum collections is still a challenge for most museums, underscoring the importance of assignment of metadata and semantic enrichment.

Snapshot img

Data Catalog Market Size 2025-2029

The data catalog market size is valued to increase USD 5.03 billion, at a CAGR of 29.5% from 2024 to 2029. Rising demand for self-service analytics will drive the data catalog market.

Major Market Trends & Insights

North America dominated the market and accounted for a 39% growth during the forecast period.
By Component - Solutions segment was valued at USD 822.80 billion in 2023
By Deployment - Cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 554.30 million
Market Future Opportunities: USD 5031.50 million
CAGR : 29.5%
North America: Largest market in 2023

Market Summary

The market is a dynamic and evolving landscape, driven by the increasing demand for self-service analytics and the rise of data mesh architecture. Core technologies, such as metadata management and data discovery, play a crucial role in enabling organizations to effectively manage and utilize their data assets. Applications, including data governance and data integration, are also seeing significant growth as businesses seek to optimize their data management processes.
However, maintaining catalog accuracy over time poses a challenge, with concerns surrounding data lineage, data quality, and data security. According to recent estimates, the market is expected to account for over 30% of the overall data management market share by 2025, underscoring its growing importance in the digital transformation era.

What will be the Size of the Data Catalog Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Catalog Market Segmented and what are the key trends of market segmentation?

The data catalog industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Deployment

  Cloud
  On-premises


Type

  Technical metadata
  Business metadata
  Operational metadata


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

Data catalog solutions have gained significant traction in today's data-driven business landscape, addressing complexities in data discovery, governance, collaboration, and data lifecycle management. These solutions enable users to search and discover relevant datasets for analytical or reporting purposes, thereby reducing the time spent locating data, promoting data reuse, and ensuring the usage of appropriate datasets for specific tasks. Centralized metadata storage is a key feature of data catalog solutions, offering detailed information about datasets, including source, schema, data quality, lineage, and other essential attributes. This metadata-centric approach enhances understanding of data assets, supports data governance initiatives, and provides users with the necessary context for effective data utilization.

Data catalog solutions also facilitate semantic enrichment, data versioning, data security protocols, data access control, and data model design. Semantic enrichment adds meaning and context to data, making it easier to understand and use. Data versioning ensures that different versions of datasets are managed effectively, while data access control restricts access to sensitive data. Data model design helps create an accurate representation of data structures and relationships. Moreover, data catalog solutions offer data discovery tools, data lineage tracking, data governance policies, schema management, data lake management, ETL process optimization, and data quality monitoring. Data discovery tools help users locate relevant data quickly and efficiently.

Data lineage tracking enables users to trace the origin and movement of data throughout its lifecycle. Data governance policies ensure compliance with regulatory requirements and organizational standards. Schema management maintains the structure and consistency of data, while data lake management simplifies the management of large volumes of data. ETL process optimization improves the efficiency of data integration, and data quality monitoring ensures that data is accurate and reliable. Businesses across various sectors, including healthcare, finance, retail, and manufacturing, are increasingly adopting data catalog solutions to streamline their data management and analytics processes. According to recent studies, the adoption of data catalog solutions has grown by approximately 25%, with an estimated 30% of organizations planning to implement t

Metadata Enrichment for Streaming Market Outlook

According to our latest research, the global metadata enrichment for streaming market size reached USD 1.48 billion in 2024, reflecting the rapid adoption of advanced content management solutions across digital streaming platforms. The market is projected to grow at a robust CAGR of 17.3% from 2025 to 2033, positioning the industry to reach an estimated USD 6.19 billion by 2033. This impressive growth is primarily driven by the surging demand for personalized content experiences, the proliferation of streaming services, and the increasing importance of data-driven content monetization strategies.

The primary growth factor fueling the metadata enrichment for streaming market is the exponential rise in digital content consumption. As consumers continue to shift away from traditional broadcast media toward on-demand streaming platforms, the need for enhanced content discoverability and user engagement has become paramount. Metadata enrichment plays a critical role in this landscape by providing structured, contextual information about content, enabling more accurate recommendations, intuitive search functionalities, and seamless personalization. The surge in original programming by OTT platforms and broadcasters also necessitates robust metadata frameworks to manage vast content libraries efficiently. This trend is further amplified by the integration of artificial intelligence and machine learning technologies, which automate metadata tagging, enhance semantic analysis, and improve overall content curation processes.

Another significant driver of market expansion is the competitive differentiation sought by streaming providers. With an ever-increasing number of players in the digital streaming ecosystem, platforms are leveraging metadata enrichment to deliver unique user experiences, optimize content monetization, and streamline rights management. Advanced metadata solutions allow for dynamic advertising insertion, granular audience segmentation, and improved compliance with regional content regulations. Moreover, the growing adoption of multilingual and cross-regional content has created a pressing need for metadata enrichment tools capable of supporting diverse languages, cultural nuances, and content formats. This has led to increased investments in both software and services dedicated to metadata enhancement, fueling overall market growth.

The proliferation of connected devices and the rise of smart TVs, mobile applications, and voice-activated assistants have further underscored the importance of metadata enrichment for streaming services. As viewers access content across multiple devices and platforms, maintaining consistency in content discovery and personalization becomes increasingly complex. Metadata enrichment enables seamless cross-device experiences by ensuring that content attributes, recommendations, and user preferences are synchronized and contextually relevant. Additionally, the integration of metadata with advertising and analytics platforms allows content providers to optimize ad targeting, measure audience engagement, and derive actionable insights from viewer behavior. These capabilities are essential for sustaining subscriber growth and maximizing revenue generation in an intensely competitive market.

From a regional perspective, North America continues to dominate the metadata enrichment for streaming market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading OTT platforms, high internet penetration, and a mature digital infrastructure have established North America as a hub for innovation in metadata technologies. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of streaming services, increasing smartphone adoption, and the emergence of local content providers. Europe remains a key market due to stringent content regulations, a diverse linguistic landscape, and the growing demand for cross-border content distribution. As streaming adoption accelerates worldwide, regional dynamics will continue to shape the evolution of metadata enrichment solutions.

Industrial Knowledge Graph Platform Market Outlook

Based on our latest research, the global Industrial Knowledge Graph Platform market size was valued at USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 25.8% expected through the forecast period. With this trajectory, the market is projected to reach USD 9.08 billion by 2033. This exponential growth is fueled by the surge in industrial digitalization, the increasing need for contextual data integration, and the adoption of artificial intelligence (AI) and machine learning (ML) across industrial sectors. The market’s rapid expansion is underpinned by the critical role that knowledge graph platforms play in unifying disparate data sources, driving operational efficiency, and enabling advanced analytics for enterprise decision-making.

One of the primary growth drivers for the Industrial Knowledge Graph Platform market is the escalating demand for real-time, context-rich insights across industrial operations. As industries such as manufacturing, energy, and automotive embrace Industry 4.0 principles, the volume and complexity of data generated from interconnected devices and systems have increased dramatically. Knowledge graph platforms excel at integrating structured and unstructured data from diverse sources, enabling organizations to create a comprehensive, interconnected view of their assets, processes, and supply chains. This capability is crucial for enhancing operational transparency, optimizing resource allocation, and supporting predictive analytics, which collectively contribute to improved productivity and reduced downtime.

Another key factor propelling market growth is the widespread adoption of AI and ML technologies within industrial environments. Industrial knowledge graph platforms serve as foundational infrastructure for advanced AI applications by providing a semantic layer that contextualizes data relationships. This semantic enrichment empowers AI-driven solutions to deliver more accurate predictions, uncover hidden patterns, and automate complex decision-making processes. As organizations strive to achieve greater agility and resilience in the face of global supply chain disruptions and evolving regulatory requirements, knowledge graph platforms are increasingly seen as indispensable tools for digital transformation and competitive differentiation.

Furthermore, the growing emphasis on asset management, risk mitigation, and process optimization is fueling the adoption of industrial knowledge graph platforms. These platforms facilitate holistic visibility into asset lifecycles, maintenance schedules, and operational risks by connecting siloed data repositories and enabling cross-domain analytics. Industries such as oil & gas, pharmaceuticals, and chemicals, which operate in highly regulated environments, benefit significantly from the ability to trace data lineage, ensure compliance, and proactively manage risks. The integration of knowledge graphs with existing enterprise systems, including ERP, MES, and SCADA, further enhances their value proposition by streamlining workflows and supporting real-time decision-making.

Regionally, North America leads the global market, driven by early technology adoption, strong presence of key vendors, and significant investments in industrial IoT and AI initiatives. Europe follows closely, supported by robust manufacturing and automotive sectors, as well as stringent regulatory standards that encourage data integration and transparency. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, government-led digitalization programs, and the proliferation of smart manufacturing initiatives in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as local industries increasingly recognize the value of knowledge graph platforms for operational excellence and risk management.

Component Analysis

<br

This dataset contains multiple interrelated files used in the context of experiments involving semantic enrichment and spatial contextualization of modern art collections from the Museum of Modern Art (MoMA). It includes metadata on artworks, artist relationships, and spatial assignments (rooms/floors), as well as a structured knowledge graph.ArtworksWithRandomRooms.csvA subset of artworks from the MoMA collection enriched with randomly assigned exhibition rooms and floor-level information. Each row corresponds to an artwork with attributes including artist name, date, style, genre, classification, and synthetic spatial location (floor, room).ArtworksWithRandomRooms_2.csvAn alternative version of the previous dataset with a different random room assignment, useful for comparing layout-sensitive recommendations or spatial distribution experiments.Artworks4thFloor.csvContains artworks specifically located on the 4th floor of the museum. All artworks share the same spatial attributes (floor = "4th floor", room = 407), allowing evaluation of fixed-location or room-centric recommendation scenarios.Artists.csvProvides biographical metadata about artists referenced in the artworks datasets. Fields include artist name, nationality, gender, and life span (BeginDate, EndDate), enabling demographic or historical analysis.artistRelations.csvEncodes semantic relationships between artists (e.g., influence, collaboration) through directed links. Each row denotes a relation from one artist to another, supporting graph-based reasoning and network analysis.momaGraph.graphmlA GraphML-formatted knowledge graph capturing entities such as artworks, artists, styles, genres, and their relationships. This graph serves as a semantic backbone for tasks like recommendation, enrichment, or path-based inference.generated_ratings_with_item_info.csvA synthetically-generated ground truth for 10,000 visitors, with explicit ratings in the range [1,5] for every artwork in ArtworksWithRandomRooms files.All files are provided under an open license to support reproducible research in cultural heritage AI, semantic web, and recommender systems.

BackgroundPhenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO.ResultsHPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA).ConclusionsHPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (https://sourceforge.net/p/hposim/).

Industrial Knowledge Graphs Market Outlook

According to our latest research, the global Industrial Knowledge Graphs market size in 2024 stands at USD 2.1 billion, with a robust compound annual growth rate (CAGR) of 22.6% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 16.2 billion. This growth is primarily driven by the increasing need for advanced data integration and contextualization solutions across industrial sectors, enabling organizations to unlock actionable insights from complex datasets and enhance operational efficiency.

The rapid digital transformation across industries is a key growth factor for the Industrial Knowledge Graphs market. Organizations are increasingly adopting Industrial Knowledge Graphs to break down traditional data silos and interconnect disparate data sources, providing a unified and contextualized view of enterprise data. This capability is critical in industrial environments, where operational data, sensor outputs, maintenance logs, and enterprise resource planning (ERP) systems must be seamlessly integrated for real-time decision-making. The proliferation of Industrial Internet of Things (IIoT) devices and the exponential growth in machine-generated data have further accelerated the adoption of knowledge graphs, as they offer a scalable and flexible approach to data modeling, relationship mapping, and semantic search, supporting a wide range of industrial use cases.

Another significant driver for market expansion is the increasing focus on predictive maintenance, asset management, and supply chain optimization within the manufacturing, energy, and automotive sectors. Industrial Knowledge Graphs enable advanced analytics and machine learning applications by connecting heterogeneous data points, identifying hidden patterns, and supporting root cause analysis. This leads to reduced downtime, optimized asset utilization, and minimized operational risks. As industries strive for greater competitiveness and resilience, the ability to harness data-driven insights through knowledge graphs is becoming a strategic imperative. Additionally, the growing adoption of cloud-based deployment models is making these solutions more accessible, scalable, and cost-effective for organizations of all sizes, further fueling market growth.

The Industrial Knowledge Graphs market is also benefiting from advancements in artificial intelligence (AI) and natural language processing (NLP) technologies. These innovations are enhancing the ability of knowledge graphs to understand context, infer relationships, and generate actionable recommendations. As AI-driven automation and cognitive analytics become more prevalent in industrial operations, knowledge graphs serve as the foundational infrastructure for enabling intelligent, context-aware applications. The integration of AI with knowledge graphs is expected to drive new opportunities for process automation, quality control, and compliance management, positioning knowledge graphs as a critical enabler of Industry 4.0 initiatives.

Regionally, North America currently leads the Industrial Knowledge Graphs market, driven by strong investments in digital transformation, advanced manufacturing, and IIoT adoption. Europe follows closely, with a strong focus on industrial automation and sustainability. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, government initiatives supporting smart manufacturing, and increasing adoption of AI-driven technologies. Latin America and the Middle East & Africa are also emerging as promising markets, with growing interest in asset optimization and predictive maintenance solutions. Overall, the global market is poised for significant expansion as industries worldwide recognize the transformative potential of knowledge graphs in driving operational excellence and innovation.

Component Analysis

The Industrial Knowledge Graphs market by component is segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment encompasses platforms and tools designed to create, manage, and query knowledge graphs, enabling seamless integration of structured and unstructured industrial data. These software solutions provide the backbone for data modeling, relationship mapping, and semantic enrichment, allowing organizations to build comprehensive knowl

This study presents a scientometric analysis of the representation of museological objects in the international scientific literature. It employs a dual quantitative-qualitative approach, combining bibliometric analysis, domain analysis, and content analysis. Data from the Web of Science Core Collection were used, spanning from 2012 to 2022, and tools such as EndNote x20, Biblioshiny, and VOSviewer. The scientific production over time shows an initial stability in research output from 2012 to 2016, followed by a marked increase from 2017 onwards. The analysis of 491 retrieved documents reveals that 40 authors are the elite contributors in museological studies, revealing the field's global significance. Integration of information science with museology is evident in the retrieved works, reflecting an interdisciplinary approach. Finally, the research concludes that online access to museum collections is still a challenge for most museums, underscoring the importance of assignment of metadata and semantic enrichment.

Data used for modeling CityJSON into a graph database structure. It also includes asset management maintenance records to enable semantic enrichment through a graph-based approach. This dataset supports the demonstration of CityJSON graph analytics use cases in the context of asset management.