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Copenhagen, Denmark
1-10 Employees
2010
AtomGraph was founded in 2011 by Martynas Jusevičius and Džiugas Tornau with the idea of enabling organisations to have a holistic overview of their scattered information resources by connecting data. AtomGraph's main products have been specifically designed to take advantage of the unique features of the RDF graph data model. We develop data-driven software to help you build and manage Knowledge Graphs efficiently. Octopus is AtomGraph’s solution that lets you get the most of your enterprise data by retrieving it from third-party cloud services and integrating it in a Knowledge Graph. From years of research and experience we've learned that it is the only existing IT technology having the potential to connect all the relevant business data from both private and public sources, while at the same time being cost-effective in the long term. Džiugas is responsible for commercial and operational activities at AtomGraph. Martynas is a software architect specialising in Semantic Web and Knowledge Graph technologies.
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Manage Knowledge Graphs the Easy Way
... LinkedDataHub is an open-source software for Knowledge Graph management. It makes working with data faster and building applications ...
Mole Valley, United Kingdom
11-50 Employees
2014
From our team’s roots in scalable data engineering and digital media knowledge graphs at the BBC, we have delivered Data Platforms across sectors.Today, we have codified this experience and expertise into market-leading SaaS products that enable our customers get products to market rapidly, while maintaining a low total cost of ownership. We are passionate about purposeful, well structured, high-performance data products.We work with organizations who want the best value from their core digital assets, and we know how to help them achieve that. From simple data models right through to large scale AI-driven platforms, we have the right buy/build/partner approach to achieve speed to market and maximum agility thereafter.
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Knowledge Graphs
... Data Language is a market leader in the design, implementation, and deployment of knowledge graphs (KGs), and we have developed a Knowledge Graph as a Service platform that enables exploration and speed-to-market. ...
United States
51-100 Employees
2015
To empower every data citizen to make knowledge-informed decisions. If you want to drive growth and innovation in today’s complex world of never-ending data capture, you can’t be bogged down with rigid data structures. Our three founders, Kendall Clark, Mike Grove, and Evren Sirin, started pursuing this data-driven mission after meeting at the University of Maryland AI Lab. Stardog Named to the Big Data 50: Companies Driving Innovation in 2022. Stardog a Finalist in DBTA Readers’ Choice Awards 2022.
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Enterprise Knowledge Graph platform
... Learn about the key features powering Stardog’s Enterprise Knowledge Graph Platform. Learn how they help overcome complex data challenges that data integration tools can’t handle. ...
Hyderabad, India
1-10 Employees
2016
So, to bring the minds of the people who believe in what we believe. We believe, open source is one of the best ways to work closely with the end-user community and developers to constantly push the solutions towards nothing but solving the right problems.
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Model, Build and Explore Knowledge Graphs with Invana
... Invana helps people explore, analyse and find insights from data using the power of Knowledge Graphs. ...
Hamburg, Germany
1-10 Employees
2020
DataBorg provides an all-in-one knowledge management suite that empowers companies to leverage their data with intelligent solutions. DataBorg's Knowledge Integration tools securely and compliantly link distributed knowledge into a single, multi-access and performant repository, ideal for use in Artificial Intelligence, Machine Learning and Business Intelligence solutions. DataBorg provides businesses with high-quality tools to overcome challenges with poor data quality and unlock their full potential for success through AI tools and data-driven insights. DataBorg Platform components have been in development for over a decadeand have been used in production by our partners and customers. DataBorg components are being used by our partners across the world. DataBorg helped hundreds of users to shape, evolve and better understand their data. DataBorg components are answering over 1,000,000 annual calls. DataBorg helped generate over 1,000 knowledge graphs so far.
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Text To Knowledge Graph API
... DataBorg - Text To Knowledge Graph ...
Ghent, Belgium
11-50 Employees
2011
ONTOFORCE enables life science companies to unlock hidden insights from data. Feel confident in making decisions that are backed by trusted data. Head of Discovery Biology Innovative biotech specialized in computational drug discovery. With DISQOVER, built on knowledge graph technology, we support life sciences and pharmaceutical companies with innovative data management and visualization.
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Semantic integration and knowledge graphs
... DISQOVER uses the powerful concept of a knowledge graph to integrate, link, and structure data. ...
Berlin, Germany
1-10 Employees
2022
Discover the true potential of decentralised knowledge graphs. We combine the best of Natural Language Processing (NLP) technologies and the reasoning prowess of human intelligence to bring you the most granular and comprehensive knowledge graphs.
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Truth verified Knowledge Graphs
... Truth verified Knowledge Graphs ...
Newcastle, Australia
1-10 Employees
2010
Thank you, your weekly news request has been recorded. Knowledge Graph & dialog support for other LLMs. Thank you, your contact request has been sent. We help you capture, create and do great things with knowledge. Our tools let domain experts inside or outside of your enterprise contribute to your structured knowledge.
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factnexus | knowledge empowered
... Knowledge Graph ...
London, United Kingdom
1-10 Employees
Uncover intricate supplier connections and provide clear explanations for complex logistics. The research tracks of the Central Bank of Italy and Prometheux were the only European program selected for the discussion on "Public Risk Analysis & Alert System". The Central Bank of Italy has partnered with Prometheux to achieve unprecedented scalability and explainability while reasoning over ultra-large financial knowledge graphs.
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Prometheux
... Our scientists come from the Knowledge Graph labs across the University of Oxford and TU ...
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Some interesting numbers and facts about the results you have just received for Knowledge Graph
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 210 |
| Amount of suitable service providers | 144 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 2010 |
| Youngest suiting company | 2022 |
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A Knowledge Graph is a sophisticated data structure that integrates interconnected descriptions of entities — such as objects, events, situations, or concepts — facilitating the creation of a dynamic map of relationships and properties within a given domain. This structure enables machines to process and integrate vast amounts of information by recognizing the nuanced relationships between different entities, thereby simulating a form of understanding akin to human cognitive capabilities. Through the use of semantic querying methods and artificial intelligence, Knowledge Graphs provide a framework for data to be stored, shared, and manipulated in a way that is both efficient and meaningful, making them highly valuable in fields such as search engine optimization, digital marketing, and AI-driven research. Their real-world applications range from enhancing the accuracy of search engine results by understanding the intent behind queries to powering recommendation systems that offer personalized content to users. By structuring data in this interconnected manner, Knowledge Graphs offer a foundational technology that not only improves the retrieval and quality of information but also drives advancements in machine learning by providing context that enables algorithms to learn more about how different concepts are related. The impact of Knowledge Graphs on technology and information management is profound, as they enable more intuitive, context-aware systems that significantly enhance user experience and knowledge discovery.
1. Enhanced Data Interconnectivity
Knowledge Graphs excel in linking diverse data points, creating a web of interconnected information. This structure enables more complex queries and insights, surpassing traditional databases in capturing the nuanced relationships between entities.
2. Improved Search Efficiency
By understanding the context and relationships between data points, Knowledge Graphs significantly enhance search capabilities. Users can expect more accurate and relevant results, as the system leverages its interconnected data structure to understand and fulfill complex queries.
3. Dynamic Data Integration
Knowledge Graphs are inherently flexible, allowing for the seamless addition of new data without disrupting existing structures. This adaptability ensures that the system can evolve with emerging information, maintaining its relevance and utility over time.
4. Better Decision Making
The comprehensive insights offered by Knowledge Graphs support more informed decision-making processes. By providing a holistic view of connected data, these systems enable users to understand complex scenarios and make decisions based on a broad spectrum of relevant information.
While evaluating the different suppliers make sure to check the following criteria:
1. Integration Capabilities
Ensure the supplier's knowledge graph can easily integrate with existing systems and data sources to streamline processes.
2. Data Quality and Diversity
Assess the quality, accuracy, and diversity of data the knowledge graph can manage, ensuring it meets your specific business needs.
3. Scalability
Consider whether the knowledge graph can scale in response to your organization’s growing data needs and complexity.
4. Real-time Data Processing
Check the supplier's ability to handle real-time data processing, which is crucial for timely insights and decisions.
5. Customization and Flexibility
Evaluate the extent to which the knowledge graph can be customized and how flexible it is to adapt to your business requirements.
6. Support and Maintenance
Understand the level of support and maintenance services offered, ensuring that you have adequate assistance for smooth operations.
Knowledge graphs are revolutionizing how businesses across various industries manage and leverage their data. In the healthcare sector, knowledge graphs play a pivotal role in enhancing patient care by integrating and analyzing vast amounts of medical records and research data. This integration allows for more personalized treatment plans and quicker diagnoses, significantly improving patient outcomes. The financial industry benefits from knowledge graphs through improved risk assessment and fraud detection. By synthesizing disparate data sources, these graphs offer a comprehensive view of transactions and behaviors, enabling financial institutions to identify suspicious activities more accurately and mitigate potential threats to their operations. In the realm of supply chain management, knowledge graphs facilitate more efficient logistics and inventory management. They achieve this by providing a unified view of the supply chain, allowing companies to track products in real-time, predict potential disruptions, and optimize inventory levels to meet consumer demand more effectively. Lastly, in customer service, knowledge graphs are employed to deliver more personalized and efficient support. By aggregating customer interactions and preferences, businesses can offer tailored recommendations and resolve issues more swiftly, enhancing the overall customer experience. These use cases illustrate the versatility and value of knowledge graphs in addressing complex challenges across industries, driving innovation, and improving business outcomes.
Knowledge Graph technology, as of the latest evaluations, is positioned at a high Technology Readiness Level (TRL), specifically between levels 8 and 9. This classification signifies that Knowledge Graphs have not only been fully formulated and tested but also validated in real-world environments. The ascent to this TRL can be attributed to several technical advancements and widespread applications in various sectors, from search engines like Google to social networks and enterprise data management. The core of Knowledge Graphs lies in their ability to semantically interlink vast amounts of data, utilizing ontologies that enable machines to understand and infer relationships and properties within the data in a context that mimics human cognition. This semantic web technology has evolved significantly, driven by advancements in artificial intelligence and machine learning, which have enhanced its capability for data integration, reasoning, and querying across disparate data sources. The robustness and maturity of Knowledge Graph technology are underscored by its integration into critical operational frameworks within industries, demonstrating not only high reliability but also the adaptability of this technology to serve complex, real-world information management and analysis needs efficiently.
In the Short-Term, advancements in knowledge graph technology will be marked by improved data integration and semantic search capabilities. As machine learning algorithms become more sophisticated, these graphs will more seamlessly aggregate information from diverse sources, offering users more relevant and timely data. This phase will also see the introduction of more user-friendly interfaces, making knowledge graphs more accessible to non-technical users and thus broadening their application in various industries. The Mid-Term phase will witness the evolution of knowledge graphs into more predictive and personalized tools. By leveraging advancements in artificial intelligence, these graphs will not only store and interlink data but will also anticipate user needs by suggesting connections and insights that were not explicitly sought. This predictive capability will significantly enhance decision-making processes in fields such as healthcare, finance, and supply chain management, where real-time, data-driven insights are paramount. In the Long-Term, knowledge graphs are expected to become fully autonomous entities capable of self-updating, self-correcting, and generating new knowledge without human intervention. This will be achieved through continuous learning and the ability to discern patterns and relationships in vast datasets. Such autonomy will revolutionize knowledge management, allowing for the discovery of groundbreaking insights across disciplines. This era will likely see knowledge graphs at the core of most, if not all, major technological systems, driving innovation and efficiency.
Some interesting questions that has been asked about the results you have just received for Knowledge Graph
What are related technologies to Knowledge Graph?
Based on our calculations related technologies to Knowledge Graph are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Who are Start-Ups in the field of Knowledge Graph?
Which industries are mostly working on Knowledge Graph?
The most represented industries which are working in Knowledge Graph are Information Technology, Software, Data and Analytics, Artificial Intelligence, Science and Engineering
How does ensun find these Knowledge Graph Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
